WorldWideScience

Sample records for network centrality analysis

  1. Fast network centrality analysis using GPUs

    Directory of Open Access Journals (Sweden)

    Shi Zhiao

    2011-05-01

    Full Text Available Abstract Background With the exploding volume of data generated by continuously evolving high-throughput technologies, biological network analysis problems are growing larger in scale and craving for more computational power. General Purpose computation on Graphics Processing Units (GPGPU provides a cost-effective technology for the study of large-scale biological networks. Designing algorithms that maximize data parallelism is the key in leveraging the power of GPUs. Results We proposed an efficient data parallel formulation of the All-Pairs Shortest Path problem, which is the key component for shortest path-based centrality computation. A betweenness centrality algorithm built upon this formulation was developed and benchmarked against the most recent GPU-based algorithm. Speedup between 11 to 19% was observed in various simulated scale-free networks. We further designed three algorithms based on this core component to compute closeness centrality, eccentricity centrality and stress centrality. To make all these algorithms available to the research community, we developed a software package gpu-fan (GPU-based Fast Analysis of Networks for CUDA enabled GPUs. Speedup of 10-50× compared with CPU implementations was observed for simulated scale-free networks and real world biological networks. Conclusions gpu-fan provides a significant performance improvement for centrality computation in large-scale networks. Source code is available under the GNU Public License (GPL at http://bioinfo.vanderbilt.edu/gpu-fan/.

  2. Centrality measures in temporal networks with time series analysis

    Science.gov (United States)

    Huang, Qiangjuan; Zhao, Chengli; Zhang, Xue; Wang, Xiaojie; Yi, Dongyun

    2017-05-01

    The study of identifying important nodes in networks has a wide application in different fields. However, the current researches are mostly based on static or aggregated networks. Recently, the increasing attention to networks with time-varying structure promotes the study of node centrality in temporal networks. In this paper, we define a supra-evolution matrix to depict the temporal network structure. With using of the time series analysis, the relationships between different time layers can be learned automatically. Based on the special form of the supra-evolution matrix, the eigenvector centrality calculating problem is turned into the calculation of eigenvectors of several low-dimensional matrices through iteration, which effectively reduces the computational complexity. Experiments are carried out on two real-world temporal networks, Enron email communication network and DBLP co-authorship network, the results of which show that our method is more efficient at discovering the important nodes than the common aggregating method.

  3. Balanced Centrality of Networks

    Science.gov (United States)

    Sciriha, Irene

    2014-01-01

    There is an age-old question in all branches of network analysis. What makes an actor in a network important, courted, or sought? Both Crossley and Bonacich contend that rather than its intrinsic wealth or value, an actor's status lies in the structures of its interactions with other actors. Since pairwise relation data in a network can be stored in a two-dimensional array or matrix, graph theory and linear algebra lend themselves as great tools to gauge the centrality (interpreted as importance, power, or popularity, depending on the purpose of the network) of each actor. We express known and new centralities in terms of only two matrices associated with the network. We show that derivations of these expressions can be handled exclusively through the main eigenvectors (not orthogonal to the all-one vector) associated with the adjacency matrix. We also propose a centrality vector (SWIPD) which is a linear combination of the square, walk, power, and degree centrality vectors with weightings of the various centralities depending on the purpose of the network. By comparing actors' scores for various weightings, a clear understanding of which actors are most central is obtained. Moreover, for threshold networks, the (SWIPD) measure turns out to be independent of the weightings. PMID:27437494

  4. How good are network centrality measures? Longitudinal analysis of ...

    Indian Academy of Sciences (India)

    All such networks are prone to congestion and traf- fic delay. A vast amount of research has been devoted to modeling traffic flow and optimizing transport pro- cesses in complex networks. For example, earlier studies have modeled the spread of disease by using airline transportation networks [13]. Delay propagation in the.

  5. A distribution analysis of the central Maya lowlands ecoinformation network: its rises, falls, and changes

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    Joel D. Gunn

    2017-03-01

    Full Text Available We report a study of central Maya lowland dynastic information networks, i.e., six cities' external elite ceramic influences, and how they reflect the decision-making practices of Maya elites over 3000 years. Forest cover, i.e., Moraceae family pollen, was added to the network analysis to provide ecological boundary conditions, thus ecologically moderated information networks. Principal components analysis revealed three dominant patterns. First, the networking of interior cities into powerful polities in the Late Preclassic and Classic periods (400 BCE-800 CE. In a second pattern, coastal cities emerged as key entrepôts based on marine navigation (Terminal and Postclassic periods, 800-1500 CE. Climate dynamics and sustainability considerations facilitated the transition. Forest cover, a measure of ecosystem health, shows interior forests diminished as interior cities networked but rebounded as their networks declined. By contrast, coastal forests flourished with networks implying that the marine-based economy was sustainable. Third, in the Classic, the network-dominant coast, west or east, changed with interior polities' political struggles, the critical transition occurring after 695 CE as Tikal gained dominance over the Calakmul-Caracol alliance. Beginning with the Late Preclassic about 2000 years ago, it is possible to assign names to the decision makers by referencing the growing literature on written Maya records. Although the detectable decision sequence evident in this analysis is very basic, we believe it does open possible avenues to much deeper understanding as the study proceeds into the future. The Integrated History and Future of People on Earth-Maya working group that sponsored the analysis anticipates that it will provide actionable social science intelligence for future decision making at the global scale.

  6. The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis.

    Science.gov (United States)

    Richetin, Juliette; Preti, Emanuele; Costantini, Giulio; De Panfilis, Chiara

    2017-01-01

    We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy.

  7. The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis.

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    Juliette Richetin

    Full Text Available We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively. We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy.

  8. The centrality of affective instability and identity in Borderline Personality Disorder: Evidence from network analysis

    Science.gov (United States)

    Costantini, Giulio; De Panfilis, Chiara

    2017-01-01

    We argue that the series of traits characterizing Borderline Personality Disorder samples do not weigh equally. In this regard, we believe that network approaches employed recently in Personality and Psychopathology research to provide information about the differential relationships among symptoms would be useful to test our claim. To our knowledge, this approach has never been applied to personality disorders. We applied network analysis to the nine Borderline Personality Disorder traits to explore their relationships in two samples drawn from university students and clinical populations (N = 1317 and N = 96, respectively). We used the Fused Graphical Lasso, a technique that allows estimating networks from different populations separately while considering their similarities and differences. Moreover, we examined centrality indices to determine the relative importance of each symptom in each network. The general structure of the two networks was very similar in the two samples, although some differences were detected. Results indicate the centrality of mainly affective instability, identity, and effort to avoid abandonment aspects in Borderline Personality Disorder. Results are consistent with the new DSM Alternative Model for Personality Disorders. We discuss them in terms of implications for therapy. PMID:29040324

  9. Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv.

    Science.gov (United States)

    Melak, Tilahun; Gakkhar, Sunita

    2015-01-01

    Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG) yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

  10. Comparative Genome and Network Centrality Analysis to Identify Drug Targets of Mycobacterium tuberculosis H37Rv

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    Tilahun Melak

    2015-01-01

    Full Text Available Potential drug targets of Mycobacterium tuberculosis H37Rv were identified through systematically integrated comparative genome and network centrality analysis. The comparative analysis of the complete genome of Mycobacterium tuberculosis H37Rv against Database of Essential Genes (DEG yields a list of proteins which are essential for the growth and survival of the pathogen. Those proteins which are nonhomologous with human were selected. The resulting proteins were then prioritized by using the four network centrality measures: degree, closeness, betweenness, and eigenvector. Proteins whose centrality value is close to the centre of gravity of the interactome network were proposed as a final list of potential drug targets for the pathogen. The use of an integrated approach is believed to increase the success of the drug target identification process. For the purpose of validation, selective comparisons have been made among the proposed targets and previously identified drug targets by various other methods. About half of these proteins have been already reported as potential drug targets. We believe that the identified proteins will be an important input to experimental study which in the way could save considerable amount of time and cost of drug target discovery.

  11. Analysis of Road Network Pattern Considering Population Distribution and Central Business District.

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    Fangxia Zhao

    Full Text Available This paper proposes a road network growing model with the consideration of population distribution and central business district (CBD attraction. In the model, the relative neighborhood graph (RNG is introduced as the connection mechanism to capture the characteristics of road network topology. The simulation experiment is set up to illustrate the effects of population distribution and CBD attraction on the characteristics of road network. Moreover, several topological attributes of road network is evaluated by using coverage, circuitness, treeness and total length in the experiment. Finally, the suggested model is verified in the simulation of China and Beijing Highway networks.

  12. Analysis of Road Network Pattern Considering Population Distribution and Central Business District.

    Science.gov (United States)

    Zhao, Fangxia; Sun, Huijun; Wu, Jianjun; Gao, Ziyou; Liu, Ronghui

    2016-01-01

    This paper proposes a road network growing model with the consideration of population distribution and central business district (CBD) attraction. In the model, the relative neighborhood graph (RNG) is introduced as the connection mechanism to capture the characteristics of road network topology. The simulation experiment is set up to illustrate the effects of population distribution and CBD attraction on the characteristics of road network. Moreover, several topological attributes of road network is evaluated by using coverage, circuitness, treeness and total length in the experiment. Finally, the suggested model is verified in the simulation of China and Beijing Highway networks.

  13. Localization and centrality in networks

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    Martin, Travis; Zhang, Xiao; Newman, M. E. J.

    2014-11-01

    Eigenvector centrality is a common measure of the importance of nodes in a network. Here we show that under common conditions the eigenvector centrality displays a localization transition that causes most of the weight of the centrality to concentrate on a small number of nodes in the network. In this regime the measure is no longer useful for distinguishing among the remaining nodes and its efficacy as a network metric is impaired. As a remedy, we propose an alternative centrality measure based on the nonbacktracking matrix, which gives results closely similar to the standard eigenvector centrality in dense networks where the latter is well behaved but avoids localization and gives useful results in regimes where the standard centrality fails.

  14. Combining fuzzy logic and eigenvector centrality measure in social network analysis

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    Parand, Fereshteh-Azadi; Rahimi, Hossein; Gorzin, Mohsen

    2016-10-01

    The rapid growth of social networks use has made a great platform to present different services, increasing beneficiary of services and business profit. Therefore considering different levels of member activities in these networks, finding highly active members who can have the influence on the choice and the role of other members of the community is one the most important and challenging issues in recent years. These nodes that usually have a high number of relations with a lot of quality interactions are called influential nodes. There are various types of methods and measures presented to find these nodes. Among all the measures, centrality is the one that identifies various types of influential nodes in a network. Here we define four different factors which affect the strength of a relationship. A fuzzy inference system calculates the strength of each relation, creates a crisp matrix in which the corresponding elements identify the strength of each relation, and using this matrix eigenvector measure calculates the most influential node. Applying our suggested method resulted in choosing a more realistic central node with consideration of the strength of all friendships.

  15. Correlation of Eigenvector Centrality to Other Centrality Measures : Random, Small-World and Real-World Networks

    OpenAIRE

    Xiaojia He; Natarajan Meghanathan

    2016-01-01

    In this paper, we thoroughly investigate correlations of eigenvector centrality to five centrality measures, including degree centrality, betweenness centrality, clustering coefficient centrality, closeness centrality, and farness centrality, of various types of network (random network, small world network, and real-world network). For each network, we compute those six centrality measures, from which the correlation coefficient is determined. Our analysis suggests that the degree centrali...

  16. Network centrality of metro systems.

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    Sybil Derrible

    Full Text Available Whilst being hailed as the remedy to the world's ills, cities will need to adapt in the 21(st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no "winner takes all" unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node, but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48. Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc. to develop more sustainable cities.

  17. Hirsch index as a network centrality measure

    OpenAIRE

    Campiteli, Monica G.; Holanda, Adriano J.; Soles, Paulo R. C.; Soares, Leonardo H. D.; Kinouchi, Osame

    2010-01-01

    We study the h Hirsch index as a local node centrality measure for complex networks in general. The h index is compared with the Degree centrality (a local measure), the Betweenness and Eigenvector centralities (two non-local measures) in the case of a biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II) as test environments. In both networks, the Hirsch index has poor correlation with Betweenness centrality but correlates well with Eigen...

  18. Azimuthal pion fluctuation in ultra relativistic nuclear collisions and centrality dependence—a study with chaos based complex network analysis

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    Bhaduri, Anirban; Bhaduri, Susmita; Ghosh, Dipak

    2017-07-01

    Various works on multiplicity fluctuation have investigated the dynamics of particle production process and eventually have tried to reveal a signature of phase transition in ultra-relativistic nuclear collisions. Analysis of fluctuations of spatial patterns has been conducted in terms of conventional approach. However, analysis with fractal dynamics on the scaling behavior of the void has not been explored yet. In this work we have attempted to analyze pion fluctuation in terms of the scaling behavior of the void probability distribution in azimuthal space in ultra-relativistic nuclear collisions in the light of complex networks. A radically different and rigorous method viz. Visibility Graph was applied on the data of 32S-Ag/Br interaction at an incident energy of 200 GeV per nucleon. The analysis reveals strong scaling behavior of void probability distributions in azimuthal space and a strong centrality dependence.

  19. Network Centrality Analysis in Fungi Reveals Complex Regulation of Lost and Gained Genes.

    Science.gov (United States)

    Coulombe-Huntington, Jasmin; Xia, Yu

    2017-01-01

    Gene gain and loss shape both proteomes and the networks they form. The increasing availability of closely related sequenced genomes and of genome-wide network data should enable a better understanding of the evolutionary forces driving gene gain, gene loss and evolutionary network rewiring. Using orthology mappings across 23 ascomycete fungi genomes, we identified proteins that were lost, gained or universally conserved across the tree, enabling us to compare genes across all stages of their life-cycle. Based on a collection of genome-wide network and gene expression datasets from baker's yeast, as well as a few from fission yeast, we found that gene loss is more strongly associated with network and expression features of closely related species than that of distant species, consistent with the evolutionary modulation of gene loss propensity through network rewiring. We also discovered that lost and gained genes, as compared to universally conserved "core" genes, have more regulators, more complex expression patterns and are much more likely to encode for transcription factors. Finally, we found that the relative rate of network integration of new genes into the different types of networks agrees with experimentally measured rates of network rewiring. This systems-level view of the life-cycle of eukaryotic genes suggests that the gain and loss of genes is tightly coupled to the gain and loss of network interactions, that lineage-specific adaptations drive regulatory complexity and that the relative rates of integration of new genes are consistent with network rewiring rates.

  20. Centrality measures in social networks

    Science.gov (United States)

    Moreau, Michele

    Complex networks represent an extensive variety of systems in nature and human interactions. Networks are graphs that describe the structures of interacting systems and give substantial information about the patterns of connections between the nodes in a particular system. In turn, knowing about the structure of networks and their arrangements enables one to make certain types of predictions about their behavior. With that larger motivation, this thesis research emphasizes different measurement metrics such as degree distribution, assortativity and clustering coefficients, transitivity, modularity, network diameter, and the average path length to associate the configurations of the different networks to determine certain types of behavior. The main focus of this thesis is on social networks, where the assortative patterns of social networks were identified. The various parameters used in the study of the networks were calculated and defined using the software packages Networkx and Gephi. The different types of networks are from the Stanford Network Analysis Project (SNAP) website. In particular, the focus is on using the numerical values of the coefficients to infer differences in the forms of contact in different social networks. The ability to do so has implications for detecting preferences when it comes to the relations between groups of people in social networks. As a result of social networks displaying assortative behaviors, the data indicates that these networks could also project some traits of 'narrow-mindedness' due to the formation of different clusters. Another significant repercussion of this research is the ability of a community to thrive successfully based on the interactions of the people with one another.

  1. Network Centrality of Metro Systems

    Science.gov (United States)

    Derrible, Sybil

    2012-01-01

    Whilst being hailed as the remedy to the world’s ills, cities will need to adapt in the 21st century. In particular, the role of public transport is likely to increase significantly, and new methods and technics to better plan transit systems are in dire need. This paper examines one fundamental aspect of transit: network centrality. By applying the notion of betweenness centrality to 28 worldwide metro systems, the main goal of this paper is to study the emergence of global trends in the evolution of centrality with network size and examine several individual systems in more detail. Betweenness was notably found to consistently become more evenly distributed with size (i.e. no “winner takes all”) unlike other complex network properties. Two distinct regimes were also observed that are representative of their structure. Moreover, the share of betweenness was found to decrease in a power law with size (with exponent 1 for the average node), but the share of most central nodes decreases much slower than least central nodes (0.87 vs. 2.48). Finally the betweenness of individual stations in several systems were examined, which can be useful to locate stations where passengers can be redistributed to relieve pressure from overcrowded stations. Overall, this study offers significant insights that can help planners in their task to design the systems of tomorrow, and similar undertakings can easily be imagined to other urban infrastructure systems (e.g., electricity grid, water/wastewater system, etc.) to develop more sustainable cities. PMID:22792373

  2. Reconstruction and analysis of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis

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    Aymerich Stéphane

    2008-02-01

    Full Text Available Abstract Background Few genome-scale models of organisms focus on the regulatory networks and none of them integrates all known levels of regulation. In particular, the regulations involving metabolite pools are often neglected. However, metabolite pools link the metabolic to the genetic network through genetic regulations, including those involving effectors of transcription factors or riboswitches. Consequently, they play pivotal roles in the global organization of the genetic and metabolic regulatory networks. Results We report the manually curated reconstruction of the genetic and metabolic regulatory networks of the central metabolism of Bacillus subtilis (transcriptional, translational and post-translational regulations and modulation of enzymatic activities. We provide a systematic graphic representation of regulations of each metabolic pathway based on the central role of metabolites in regulation. We show that the complex regulatory network of B. subtilis can be decomposed as sets of locally regulated modules, which are coordinated by global regulators. Conclusion This work reveals the strong involvement of metabolite pools in the general regulation of the metabolic network. Breaking the metabolic network down into modules based on the control of metabolite pools reveals the functional organization of the genetic and metabolic regulatory networks of B. subtilis.

  3. The central role of AMP-kinase and energy homeostasis impairment in Alzheimer's disease: a multifactor network analysis.

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    Laura Caberlotto

    Full Text Available Alzheimer's disease is the most common cause of dementia worldwide, affecting the elderly population. It is characterized by the hallmark pathology of amyloid-β deposition, neurofibrillary tangle formation, and extensive neuronal degeneration in the brain. Wealth of data related to Alzheimer's disease has been generated to date, nevertheless, the molecular mechanism underlying the etiology and pathophysiology of the disease is still unknown. Here we described a method for the combined analysis of multiple types of genome-wide data aimed at revealing convergent evidence interest that would not be captured by a standard molecular approach. Lists of Alzheimer-related genes (seed genes were obtained from different sets of data on gene expression, SNPs, and molecular targets of drugs. Network analysis was applied for identifying the regions of the human protein-protein interaction network showing a significant enrichment in seed genes, and ultimately, in genes associated to Alzheimer's disease, due to the cumulative effect of different combinations of the starting data sets. The functional properties of these enriched modules were characterized, effectively considering the role of both Alzheimer-related seed genes and genes that closely interact with them. This approach allowed us to present evidence in favor of one of the competing theories about AD underlying processes, specifically evidence supporting a predominant role of metabolism-associated biological process terms, including autophagy, insulin and fatty acid metabolic processes in Alzheimer, with a focus on AMP-activated protein kinase. This central regulator of cellular energy homeostasis regulates a series of brain functions altered in Alzheimer's disease and could link genetic perturbation with neuronal transmission and energy regulation, representing a potential candidate to be targeted by therapy.

  4. Extraction of elementary rate constants from global network analysis of E. coli central metabolism

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    Broderick Gordon

    2008-05-01

    Full Text Available Abstract Background As computational performance steadily increases, so does interest in extending one-particle-per-molecule models to larger physiological problems. Such models however require elementary rate constants to calculate time-dependent rate coefficients under physiological conditions. Unfortunately, even when in vivo kinetic data is available, it is often in the form of aggregated rate laws (ARL that do not specify the required elementary rate constants corresponding to mass-action rate laws (MRL. There is therefore a need to develop a method which is capable of automatically transforming ARL kinetic information into more detailed MRL rate constants. Results By incorporating proteomic data related to enzyme abundance into an MRL modelling framework, here we present an efficient method operating at a global network level for extracting elementary rate constants from experiment-based aggregated rate law (ARL models. The method combines two techniques that can be used to overcome the difficult properties in parameterization. The first, a hybrid MRL/ARL modelling technique, is used to divide the parameter estimation problem into sub-problems, so that the parameters of the mass action rate laws for each enzyme are estimated in separate steps. This reduces the number of parameters that have to be optimized simultaneously. The second, a hybrid algebraic-numerical simulation and optimization approach, is used to render some rate constants identifiable, as well as to greatly narrow the bounds of the other rate constants that remain unidentifiable. This is done by incorporating equality constraints derived from the King-Altman and Cleland method into the simulated annealing algorithm. We apply these two techniques to estimate the rate constants of a model of E. coli glycolytic pathways. The simulation and statistical results show that our innovative method performs well in dealing with the issues of high computation cost, stiffness, local

  5. Centrality Robustness and Link Prediction in Complex Social Networks

    DEFF Research Database (Denmark)

    Davidsen, Søren Atmakuri; Ortiz-Arroyo, Daniel

    2012-01-01

    This chapter addresses two important issues in social network analysis that involve uncertainty. Firstly, we present am analysis on the robustness of centrality measures that extend the work presented in Borgati et al. using three types of complex network structures and one real social network....... Secondly, we present a method to predict edges in dynamic social networks. Our experimental results indicate that the robustness of the centrality measures applied to more realistic social networks follows a predictable pattern and that the use of temporal statistics could improve the accuracy achieved...

  6. An Analysis of Density and Degree-Centrality According to the Social Networking Structure Formed in an Online Learning Environment

    Science.gov (United States)

    Ergün, Esin; Usluel, Yasemin Koçak

    2016-01-01

    In this study, we assessed the communication structure in an educational online learning environment using social network analysis (SNA). The communication structure was examined with respect to time, and instructor's participation. The course was implemented using ELGG, a network learning environment, blended with face-to-face sessions over a…

  7. Qualitative exploration of centralities in municipal science education networks

    DEFF Research Database (Denmark)

    von der Fehr, Ane; Sølberg, Jan

    2016-01-01

    This article examines the social nature of educational change by conducting a social network analysis of social networks involving stakeholders of science education from teachers to political stakeholders. Social networks that comprise supportive structures for development of science education...... they expressed as well and thereby how they contributed to social capital in the MSE networks. We found that the central stakeholders comprised a small core of key people that always included MSE coordinators. These results imply that few central people in MSE networks play pivotal roles in development...... are diverse and in order to understand how municipal stakeholders may support such development, we explored four different municipal science education networks (MSE networks) using three different measures of centrality. The centrality measures differed in terms of what kind of stakeholder functions...

  8. Intrinsic functional plasticity of the sensorimotor network in relapsing-remitting multiple sclerosis: evidence from a centrality analysis.

    Science.gov (United States)

    Zhuang, Ying; Zhou, Fuqing; Gong, Honghan

    2015-01-01

    Advanced MRI studies have revealed regional alterations in the sensorimotor cortex of patients with relapsing-remitting multiple sclerosis (RRMS). However, the organizational features underlying the relapsing phase and the subsequent remitting phase have not been directly shown at the functional network or the connectome level. Therefore, this study aimed to characterize MS-related centrality disturbances of the sensorimotor network (SMN) and to assess network integrity and connectedness. Thirty-four patients with clinically definite RRMS and well-matched healthy controls participated in the study. Twenty-three patients in the remitting phase underwent one resting-state functional MRI, and 11 patients in the relapsing-remitting phase underwent two different MRIs. We measured voxel-wise centrality metrics to determine direct (degree centrality, DC) and global (eigenvector centrality, EC) functional relationships across the entire SMN. In the relapsing phase, DC was significantly decreased in the bilateral primary motor and somatosensory cortex (M1/S1), left dorsal premotor (PMd), and operculum-integrated regions. However, DC was increased in the peripheral SMN areas. The decrease in DC in the bilateral M1/S1 was associated with the expanded disability status scale (EDSS) and total white matter lesion loads (TWMLLs), suggesting that this adaptive response is related to the extent of brain damage in the rapid-onset attack stage. During the remission process, these alterations in centrality were restored in the bilateral M1/S1 and peripheral SMN areas. In the remitting phase, DC was reduced in the premotor, supplementary motor, and operculum-integrated regions, reflecting an adaptive response due to brain atrophy. However, DC was enhanced in the right M1 and left parietal-integrated regions, indicating chronic reorganization. In both the relapsing and remitting phases, the changes in EC and DC were similar. The alterations in centrality within the SMN indicate rapid

  9. Intrinsic functional plasticity of the sensorimotor network in relapsing-remitting multiple sclerosis: evidence from a centrality analysis.

    Directory of Open Access Journals (Sweden)

    Ying Zhuang

    Full Text Available Advanced MRI studies have revealed regional alterations in the sensorimotor cortex of patients with relapsing-remitting multiple sclerosis (RRMS. However, the organizational features underlying the relapsing phase and the subsequent remitting phase have not been directly shown at the functional network or the connectome level. Therefore, this study aimed to characterize MS-related centrality disturbances of the sensorimotor network (SMN and to assess network integrity and connectedness.Thirty-four patients with clinically definite RRMS and well-matched healthy controls participated in the study. Twenty-three patients in the remitting phase underwent one resting-state functional MRI, and 11 patients in the relapsing-remitting phase underwent two different MRIs. We measured voxel-wise centrality metrics to determine direct (degree centrality, DC and global (eigenvector centrality, EC functional relationships across the entire SMN.In the relapsing phase, DC was significantly decreased in the bilateral primary motor and somatosensory cortex (M1/S1, left dorsal premotor (PMd, and operculum-integrated regions. However, DC was increased in the peripheral SMN areas. The decrease in DC in the bilateral M1/S1 was associated with the expanded disability status scale (EDSS and total white matter lesion loads (TWMLLs, suggesting that this adaptive response is related to the extent of brain damage in the rapid-onset attack stage. During the remission process, these alterations in centrality were restored in the bilateral M1/S1 and peripheral SMN areas. In the remitting phase, DC was reduced in the premotor, supplementary motor, and operculum-integrated regions, reflecting an adaptive response due to brain atrophy. However, DC was enhanced in the right M1 and left parietal-integrated regions, indicating chronic reorganization. In both the relapsing and remitting phases, the changes in EC and DC were similar.The alterations in centrality within the SMN indicate

  10. Lobby index as a network centrality measure

    Science.gov (United States)

    Campiteli, M. G.; Holanda, A. J.; Soares, L. D. H.; Soles, P. R. C.; Kinouchi, O.

    2013-11-01

    We study the lobby index (l-index for short) as a local node centrality measure for complex networks. The l-index is compared with degree (a local measure), betweenness and Eigenvector centralities (two global measures) in the case of a biological network (Yeast interaction protein-protein network) and a linguistic network (Moby Thesaurus II). In both networks, the l-index has a poor correlation with betweenness but correlates with degree and Eigenvector centralities. Although being local, the l-index carries more information about its neighbors than degree centrality. Also, it requires much less time to compute when compared with Eigenvector centrality. Results show that the l-index produces better results than degree and Eigenvector centrality for ranking purposes.

  11. Analysis of the characteristics of the global virtual water trade network using degree and eigenvector centrality, with a focus on food and feed crops

    National Research Council Canada - National Science Library

    Lee, Sang-Hyun; Mohtar, Rabi H; Choi, Jin-Yong; Yoo, Seung-Hwan

    2016-01-01

    ..., and influential countries, using degree and eigenvector centrality during the period 2006-2010. The degree centrality was used to measure the connectivity, and eigenvector centrality was used to measure the influence on the entire GVWT network...

  12. Eigenvector centrality of nodes in multiplex networks

    OpenAIRE

    Solá Conde, Luis; Romance del Río, Miguel; Criado, R.; Flores Álvarez, Julio; García del Amo, Alejandro; Boccaletti, Stefano

    2013-01-01

    We extend the concept of eigenvector centrality to multiplex networks, and introduce several alternative parameters that quantify the importance of nodes in a multi-layered networked system, including the definition of vectorial-type centralities. In addition, we rigorously show that, under reasonable conditions, such centrality measures exist and are unique. Computer experiments and simulations demonstrate that the proposed measures provide substantially different results when applied to the...

  13. Centrality measures for immunization of weighted networks

    Directory of Open Access Journals (Sweden)

    Mohammad Khansari

    2016-03-01

    Full Text Available Effective immunization of individual communities with minimal cost in vaccination has made great discussion surrounding the realm of complex networks. Meanwhile, proper realization of relationship among people in society and applying it to social networks brings about substantial improvements in immunization. Accordingly, weighted graph in which link weights represent the intensity and intimacy of relationships is an acceptable approach. In this work we employ weighted graphs and a wide variety of weighted centrality measures to distinguish important individuals in contagion of diseases. Furthermore, we propose new centrality measures for weighted networks. Our experimental results show that Radiality-Degree centrality is satisfying for weighted BA networks. Additionally, PageRank-Degree and Radiality-Degree centralities showmoreacceptable performance in targeted immunization of weighted networks.

  14. Dynamics-based centrality for directed networks.

    Science.gov (United States)

    Masuda, Naoki; Kori, Hiroshi

    2010-11-01

    Determining the relative importance of nodes in directed networks is important in, for example, ranking websites, publications, and sports teams, and for understanding signal flows in systems biology. A prevailing centrality measure in this respect is the PageRank. In this work, we focus on another class of centrality derived from the Laplacian of the network. We extend the Laplacian-based centrality, which has mainly been applied to strongly connected networks, to the case of general directed networks such that we can quantitatively compare arbitrary nodes. Toward this end, we adopt the idea used in the PageRank to introduce global connectivity between all the pairs of nodes with a certain strength. Numerical simulations are carried out on some networks. We also offer interpretations of the Laplacian-based centrality for general directed networks in terms of various dynamical and structural properties of networks. Importantly, the Laplacian-based centrality defined as the stationary density of the continuous-time random walk with random jumps is shown to be equivalent to the absorption probability of the random walk with sinks at each node but without random jumps. Similarly, the proposed centrality represents the importance of nodes in dynamics on the original network supplied with sinks but not with random jumps.

  15. Eigenvector centrality of nodes in multiplex networks

    Science.gov (United States)

    Solá, Luis; Romance, Miguel; Criado, Regino; Flores, Julio; García del Amo, Alejandro; Boccaletti, Stefano

    2013-09-01

    We extend the concept of eigenvector centrality to multiplex networks, and introduce several alternative parameters that quantify the importance of nodes in a multi-layered networked system, including the definition of vectorial-type centralities. In addition, we rigorously show that, under reasonable conditions, such centrality measures exist and are unique. Computer experiments and simulations demonstrate that the proposed measures provide substantially different results when applied to the same multiplex structure, and highlight the non-trivial relationships between the different measures of centrality introduced.

  16. Spectral centrality measures in complex networks.

    Science.gov (United States)

    Perra, Nicola; Fortunato, Santo

    2008-09-01

    Complex networks are characterized by heterogeneous distributions of the degree of nodes, which produce a large diversification of the roles of the nodes within the network. Several centrality measures have been introduced to rank nodes based on their topological importance within a graph. Here we review and compare centrality measures based on spectral properties of graph matrices. We shall focus on PageRank (PR), eigenvector centrality (EV), and the hub and authority scores of the HITS algorithm. We derive simple relations between the measures and the (in)degree of the nodes, in some limits. We also compare the rankings obtained with different centrality measures.

  17. Association and Centrality in Criminal Networks

    DEFF Research Database (Denmark)

    Petersen, Rasmus Rosenqvist

    Network-based techniques are widely used in criminal investigations because patterns of association are actionable and understandable. Existing network models with nodes as first class entities and their related measures (e.g., social networks and centrality measures) are unable to capture...... and analyze the structural richness required to model and investigate criminal network entities and their associations. We demonstrate a need to rethink entity associations with one specific case (inspired by \\textit{The Wire}, a tv series about organized crime in Baltimore, United States) and corroborated...... by similar evidence from other cases. Our goal is to develop centrality measures for fragmented and non-navigational states of criminal network investigations. A network model with three basic first class entities is presented together with a topology of associations between network entities. We implement...

  18. Random walk centrality in interconnected multilayer networks

    CERN Document Server

    Solé-Ribalta, Albert; Gómez, Sergio; Arenas, Alex

    2015-01-01

    Real-world complex systems exhibit multiple levels of relationships. In many cases they require to be modeled as interconnected multilayer networks, characterizing interactions of several types simultaneously. It is of crucial importance in many fields, from economics to biology and from urban planning to social sciences, to identify the most (or the less) influential nodes in a network using centrality measures. However, defining the centrality of actors in interconnected complex networks is not trivial. In this paper, we rely on the tensorial formalism recently proposed to characterize and investigate this kind of complex topologies, and extend two well known random walk centrality measures, the random walk betweenness and closeness centrality, to interconnected multilayer networks. For each of the measures we provide analytical expressions that completely agree with numerically results.

  19. The locust standard brain: a 3D standard of the central complex as a platform for neural network analysis

    Directory of Open Access Journals (Sweden)

    Basil El Jundi

    2010-02-01

    Full Text Available Many insects use the pattern of polarized light in the sky for spatial orientation and navigation. We have investigated the polarization vision system in the desert locust. To create a common platform for anatomical studies on polarization vision pathways, Kurylas et al. (2008 have generated a three-dimensional (3D standard brain from confocal microscopy image stacks of 10 male brains, using two different standardization methods, the Iterative Shape Averaging (ISA procedure and the Virtual Insect Brain (VIB protocol. Comparison of both standardization methods showed that the VIB standard is ideal for comparative volume analysis of neuropils, whereas the ISA standard is the method of choice to analyze the morphology and connectivity of neurons. The central complex is a key processing stage for polarization information in the locust brain. To investigate neuronal connections between diverse central-complex neurons, we generated a higher-resolution standard atlas of the central complex and surrounding areas, using the ISA method based on brain sections from 20 individual central complexes. To explore the usefulness of this atlas, two central-complex neurons, a polarization-sensitive columnar neuron (type CPU1a and a tangential neuron that is activated during flight, the giant-fan shaped (GFS neuron, were reconstructed three-dimensionally from brain sections. To examine whether the GFS neuron is a candidate to contribute to synaptic input to the CPU1a neuron, we registered both neurons into the standardized central complex. Visualization of both neurons revealed a potential connection of the CPU1a and GFS neurons in layer II of the upper division of the central body.

  20. Network centrality in the human functional connectome

    NARCIS (Netherlands)

    Zuo, X.N.; Ehmke, R.; Mennes, M.J.J.; Imperati, D.; Castellanos, F.X.; Sporns, O.; Milham, M.P.

    2012-01-01

    The network architecture of functional connectivity within the human brain connectome is poorly understood at the voxel level. Here, using resting state functional magnetic resonance imaging data from 1003 healthy adults, we investigate a broad array of network centrality measures to provide novel

  1. Network centrality in the human functional connectome.

    Science.gov (United States)

    Zuo, Xi-Nian; Ehmke, Ross; Mennes, Maarten; Imperati, Davide; Castellanos, F Xavier; Sporns, Olaf; Milham, Michael P

    2012-08-01

    The network architecture of functional connectivity within the human brain connectome is poorly understood at the voxel level. Here, using resting state functional magnetic resonance imaging data from 1003 healthy adults, we investigate a broad array of network centrality measures to provide novel insights into connectivity within the whole-brain functional network (i.e., the functional connectome). We first assemble and visualize the voxel-wise (4 mm) functional connectome as a functional network. We then demonstrate that each centrality measure captures different aspects of connectivity, highlighting the importance of considering both global and local connectivity properties of the functional connectome. Beyond "detecting functional hubs," we treat centrality as measures of functional connectivity within the brain connectome and demonstrate their reliability and phenotypic correlates (i.e., age and sex). Specifically, our analyses reveal age-related decreases in degree centrality, but not eigenvector centrality, within precuneus and posterior cingulate regions. This implies that while local or (direct) connectivity decreases with age, connections with hub-like regions within the brain remain stable with age at a global level. In sum, these findings demonstrate the nonredundancy of various centrality measures and raise questions regarding their underlying physiological mechanisms that may be relevant to the study of neurodegenerative and psychiatric disorders.

  2. Global network centrality of university rankings

    Science.gov (United States)

    Guo, Weisi; Del Vecchio, Marco; Pogrebna, Ganna

    2017-10-01

    Universities and higher education institutions form an integral part of the national infrastructure and prestige. As academic research benefits increasingly from international exchange and cooperation, many universities have increased investment in improving and enabling their global connectivity. Yet, the relationship of university performance and its global physical connectedness has not been explored in detail. We conduct, to our knowledge, the first large-scale data-driven analysis into whether there is a correlation between university relative ranking performance and its global connectivity via the air transport network. The results show that local access to global hubs (as measured by air transport network betweenness) strongly and positively correlates with the ranking growth (statistical significance in different models ranges between 5% and 1% level). We also found that the local airport's aggregate flight paths (degree) and capacity (weighted degree) has no effect on university ranking, further showing that global connectivity distance is more important than the capacity of flight connections. We also examined the effect of local city economic development as a confounding variable and no effect was observed suggesting that access to global transportation hubs outweighs economic performance as a determinant of university ranking. The impact of this research is that we have determined the importance of the centrality of global connectivity and, hence, established initial evidence for further exploring potential connections between university ranking and regional investment policies on improving global connectivity.

  3. The central oscillatory network of orthostatic tremor.

    Science.gov (United States)

    Muthuraman, Muthuraman; Hellriegel, Helge; Paschen, Steffen; Hofschulte, Frank; Reese, Rene; Volkmann, Jens; Witt, K; Deuschl, G; Raethjen, Jan

    2013-09-01

    Orthostatic tremor (OT) is a movement disorder of the legs and trunk that is present in the standing position but typically absent when sitting. The pathological central network involved in orthostatic tremor is still unknown. In this study we analyzed 15 patients with simultaneous high-resolution electroencephalography and electromyography recording to assess corticomuscular coherence. In 1 patient we were able to simultaneously record the local field potential in the ventrolateral thalamus and electroencephalography. Dynamic imaging of coherent source analysis was used to find the sources in the brain that are coherent with the peripheral tremor signal. When standing, the network for the tremor frequency consisted of unilateral activation in the primary motor leg area, supplementary motor area, primary sensory cortex, two prefrontal/premotor sources, thalamus, and cerebellum for the whole 30-second segment recorded. The source coherence dynamics for the primary leg area and the thalamic source signals with the tibialis anterior muscle showed that they were highly coherent for the whole 30 seconds for the contralateral side but markedly decreased after 15 seconds for the ipsilateral side. The source signal and the recorded thalamus signal followed the same time frequency dynamics of coherence in 1 patient. The corticomuscular interaction in OT follows a consistent pattern with an initially bilateral pattern and then a segregated unilateral pattern after 15 seconds. This may add to the feeling of unsteadiness. It also makes the thalamus unlikely as the main source of orthostatic tremor. © 2013 International Parkinson and Movement Disorder Society.

  4. Random walk centrality for temporal networks

    Science.gov (United States)

    Rocha, Luis E. C.; Masuda, Naoki

    2014-06-01

    Nodes can be ranked according to their relative importance within a network. Ranking algorithms based on random walks are particularly useful because they connect topological and diffusive properties of the network. Previous methods based on random walks, for example the PageRank, have focused on static structures. However, several realistic networks are indeed dynamic, meaning that their structure changes in time. In this paper, we propose a centrality measure for temporal networks based on random walks under periodic boundary conditions that we call TempoRank. It is known that, in static networks, the stationary density of the random walk is proportional to the degree or the strength of a node. In contrast, we find that, in temporal networks, the stationary density is proportional to the in-strength of the so-called effective network, a weighted and directed network explicitly constructed from the original sequence of transition matrices. The stationary density also depends on the sojourn probability q, which regulates the tendency of the walker to stay in the node, and on the temporal resolution of the data. We apply our method to human interaction networks and show that although it is important for a node to be connected to another node with many random walkers (one of the principles of the PageRank) at the right moment, this effect is negligible in practice when the time order of link activation is included.

  5. The Information Behavior of Puerto Rican Migrants to Central Florida, 2003-2009: Grounded Analysis of Six Case Studies Use of Social Networks during the Migration Process

    Science.gov (United States)

    Rodriguez-Mori, Howard

    2009-01-01

    The study of the information behavior of Puerto Ricans and their reliance on personal social networks to procure needed information upon their migration to Central Florida is the core of this research. Life experiences of the researcher, as well as unstructured observations made in Puerto Rico from 1980 to 1996, and in Central Florida from 1996 to…

  6. Single-Cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks Involved in the Central Circadian Clock.

    Science.gov (United States)

    Park, James; Zhu, Haisun; O'Sullivan, Sean; Ogunnaike, Babatunde A; Weaver, David R; Schwaber, James S; Vadigepalli, Rajanikanth

    2016-01-01

    Single-cell heterogeneity confounds efforts to understand how a population of cells organizes into cellular networks that underlie tissue-level function. This complexity is prominent in the mammalian suprachiasmatic nucleus (SCN). Here, individual neurons exhibit a remarkable amount of asynchronous behavior and transcriptional heterogeneity. However, SCN neurons are able to generate precisely coordinated synaptic and molecular outputs that synchronize the body to a common circadian cycle by organizing into cellular networks. To understand this emergent cellular network property, it is important to reconcile single-neuron heterogeneity with network organization. In light of recent studies suggesting that transcriptionally heterogeneous cells organize into distinct cellular phenotypes, we characterized the transcriptional, spatial, and functional organization of 352 SCN neurons from mice experiencing phase-shifts in their circadian cycle. Using the community structure detection method and multivariate analytical techniques, we identified previously undescribed neuronal phenotypes that are likely to participate in regulatory networks with known SCN cell types. Based on the newly discovered neuronal phenotypes, we developed a data-driven neuronal network structure in which multiple cell types interact through known synaptic and paracrine signaling mechanisms. These results provide a basis from which to interpret the functional variability of SCN neurons and describe methodologies toward understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.

  7. Single-cell Transcriptional Analysis Reveals Novel Neuronal Phenotypes and Interaction Networks involved In the Central Circadian Clock

    Directory of Open Access Journals (Sweden)

    James Park

    2016-10-01

    Full Text Available Single-cell heterogeneity confounds efforts to understand how a population of cells organizes into cellular networks that underlie tissue-level function. This complexity is prominent in the mammalian suprachiasmatic nucleus (SCN. Here, individual neurons exhibit a remarkable amount of asynchronous behavior and transcriptional heterogeneity. However, SCN neurons are able to generate precisely coordinated synaptic and molecular outputs that synchronize the body to a common circadian cycle by organizing into cellular networks. To understand this emergent cellular network property, it is important to reconcile single-neuron heterogeneity with network organization. In light of recent studies suggesting that transcriptionally heterogeneous cells organize into distinct cellular phenotypes, we characterized the transcriptional, spatial, and functional organization of 352 SCN neurons from mice experiencing phase-shifts in their circadian cycle. Using the community structure detection method and multivariate analytical techniques, we identified previously undescribed neuronal phenotypes that are likely to participate in regulatory networks with known SCN cell types. Based on the newly discovered neuronal phenotypes, we developed a data-driven neuronal network structure in which multiple cell types interact through known synaptic and paracrine signaling mechanisms. These results provide a basis from which to interpret the functional variability of SCN neurons and describe methodologies towards understanding how a population of heterogeneous single cells organizes into cellular networks that underlie tissue-level function.

  8. Towards a methodology for validation of centrality measures in complex networks.

    Directory of Open Access Journals (Sweden)

    Komal Batool

    Full Text Available BACKGROUND: Living systems are associated with Social networks - networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as "centralities" have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important? PURPOSE: The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets. METHOD: We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes. RESULTS: Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify

  9. Towards a methodology for validation of centrality measures in complex networks.

    Science.gov (United States)

    Batool, Komal; Niazi, Muaz A

    2014-01-01

    Living systems are associated with Social networks - networks made up of nodes, some of which may be more important in various aspects as compared to others. While different quantitative measures labeled as "centralities" have previously been used in the network analysis community to find out influential nodes in a network, it is debatable how valid the centrality measures actually are. In other words, the research question that remains unanswered is: how exactly do these measures perform in the real world? So, as an example, if a centrality of a particular node identifies it to be important, is the node actually important? The goal of this paper is not just to perform a traditional social network analysis but rather to evaluate different centrality measures by conducting an empirical study analyzing exactly how do network centralities correlate with data from published multidisciplinary network data sets. We take standard published network data sets while using a random network to establish a baseline. These data sets included the Zachary's Karate Club network, dolphin social network and a neural network of nematode Caenorhabditis elegans. Each of the data sets was analyzed in terms of different centrality measures and compared with existing knowledge from associated published articles to review the role of each centrality measure in the determination of influential nodes. Our empirical analysis demonstrates that in the chosen network data sets, nodes which had a high Closeness Centrality also had a high Eccentricity Centrality. Likewise high Degree Centrality also correlated closely with a high Eigenvector Centrality. Whereas Betweenness Centrality varied according to network topology and did not demonstrate any noticeable pattern. In terms of identification of key nodes, we discovered that as compared with other centrality measures, Eigenvector and Eccentricity Centralities were better able to identify important nodes.

  10. Statistical analysis of network data with R

    CERN Document Server

    Kolaczyk, Eric D

    2014-01-01

    Networks have permeated everyday life through everyday realities like the Internet, social networks, and viral marketing. As such, network analysis is an important growth area in the quantitative sciences, with roots in social network analysis going back to the 1930s and graph theory going back centuries. Measurement and analysis are integral components of network research. As a result, statistical methods play a critical role in network analysis. This book is the first of its kind in network research. It can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. This text builds on Eric D. Kolaczyk’s book Statistical Analysis of Network Data (Springer, 2009).

  11. Comparative Efficacy of Antimicrobial Central Venous Catheters in Reducing Catheter-Related Bloodstream Infections in Adults: Abridged Cochrane Systematic Review and Network Meta-Analysis.

    Science.gov (United States)

    Chong, Huey Yi; Lai, Nai Ming; Apisarnthanarak, Anucha; Chaiyakunapruk, Nathorn

    2017-05-15

    The efficacy of antimicrobial central venous catheters (CVCs) remains questionable. In this network meta-analysis, we aimed to assess the comparative efficacy of antimicrobial CVC impregnations in reducing catheter-related infections in adults. We searched 4 electronic databases (Medline, the Cochrane Central Register of Controlled Trials, Embase, CINAHL) and internet sources for randomized controlled trials, ongoing clinical trials, and unpublished studies up to August 2016. Studies that assessed CVCs with antimicrobial impregnation with nonimpregnated catheters or catheters with another impregnation were included. Primary outcomes were clinically diagnosed sepsis, catheter-related bloodstream infection (CRBSI), and all-cause mortality. We performed a network meta-analysis to estimate risk ratio (RR) with 95% confidence interval (CI). Sixty studies with 17255 catheters were included. The effects of 14 impregnations were investigated. Both CRBSI and catheter colonization were the most commonly evaluated outcomes. Silver-impregnated CVCs significantly reduced clinically diagnosed sepsis compared with silver-impregnated cuffs (RR, 0.54 [95% CI, .29-.99]). When compared to no impregnation, significant CRBSI reduction was associated with minocycline-rifampicin (RR, 0.29 [95% CI, .16-.52]) and silver (RR, 0.57 [95% CI, .38-.86]) impregnations. No impregnations significantly reduced all-cause mortality. For catheter colonization, significant decreases were shown by miconazole-rifampicin (RR, 0.14 [95% CI, .05-.36]), 5-fluorouracil (RR, 0.34 [95% CI, .14-.82]), and chlorhexidine-silver sulfadiazine (RR, 0.60 [95% CI, .50-.72]) impregnations compared with no impregnation. None of the studies evaluated antibiotic/antiseptic resistance as the outcome. Current evidence suggests that the minocycline-rifampicin-impregnated CVC appears to be the most effective in preventing CRBSI. However, its overall benefits in reducing clinical sepsis and mortality remain uncertain

  12. From local to central: a network analysis of who manages plant pest and disease outbreaks across scales

    Directory of Open Access Journals (Sweden)

    Ryan R. J. McAllister

    2015-03-01

    Full Text Available One of the key determinants of success in managing natural resources is "institutional fit," i.e., how well the suite of required actions collectively match the scale of the environmental problem. The effective management of pest and pathogen threats to plants is a natural resource problem of particular economic, social, and environmental importance. Responses to incursions are managed by a network of decision makers and managers acting at different spatial and temporal scales. We applied novel network theoretical methods to assess the propensity of growers, local industry, local state government, and state and national government head offices to foster either within- or across-scale coordination during the successful 2001 Australian response to the outbreak of the fungal pathogen black sigatoka (Mycosphaerella fijiensis. We also reconstructed the response network to proxy what that network would look like today under the Australian government's revised response system. We illustrate a structural move in the plant biosecurity response system from one that was locally driven to the current top-down system, in which the national government leads coordination of a highly partitioned engagement process. For biological incursions that spread widely across regions, nationally rather than locally managed responses may improve coordination of diverse tasks. However, in dealing with such challenges of institutional fit, local engagement will always be critical in deploying flexible and adaptive local responses based on a national system. The methods we propose detect where and how network structures foster cross-scale interactions, which will contribute to stronger empirical studies of cross-scale environmental governance.

  13. EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS*

    OpenAIRE

    Taylor, Dane; MYERS, SEAN A.; Clauset, Aaron; Porter, Mason A.; Mucha, Peter J.

    2017-01-01

    Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centralit...

  14. Identification of cancer fusion drivers using network fusion centrality

    OpenAIRE

    Wu, Chia-Chin; Kannan, Kalpana; Lin, Steven; Yen, Laising; Milosavljevic, Aleksandar

    2013-01-01

    Summary: Gene fusions are being discovered at an increasing rate using massively parallel sequencing technologies. Prioritization of cancer fusion drivers for validation cannot be performed using traditional single-gene based methods because fusions involve portions of two partner genes. To address this problem, we propose a novel network analysis method called fusion centrality that is specifically tailored for prioritizing gene fusions. We first propose a domain-based fusion model built on ...

  15. GFT centrality: A new node importance measure for complex networks

    Science.gov (United States)

    Singh, Rahul; Chakraborty, Abhishek; Manoj, B. S.

    2017-12-01

    Identifying central nodes is very crucial to design efficient communication networks or to recognize key individuals of a social network. In this paper, we introduce Graph Fourier Transform Centrality (GFT-C), a metric that incorporates local as well as global characteristics of a node, to quantify the importance of a node in a complex network. GFT-C of a reference node in a network is estimated from the GFT coefficients derived from the importance signal of the reference node. Our study reveals the superiority of GFT-C over traditional centralities such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and Google PageRank centrality, in the context of various arbitrary and real-world networks with different degree-degree correlations.

  16. Analysis of the characteristics of the global virtual water trade network using degree and eigenvector centrality, with a focus on food and feed crops

    Directory of Open Access Journals (Sweden)

    S.-H. Lee

    2016-10-01

    Full Text Available This study aims to analyze the characteristics of global virtual water trade (GVWT, such as the connectivity of each trader, vulnerable importers, and influential countries, using degree and eigenvector centrality during the period 2006–2010. The degree centrality was used to measure the connectivity, and eigenvector centrality was used to measure the influence on the entire GVWT network. Mexico, Egypt, China, the Republic of Korea, and Japan were classified as vulnerable importers, because they imported large quantities of virtual water with low connectivity. In particular, Egypt had a 15.3 Gm3 year−1 blue water saving effect through GVWT: the vulnerable structure could cause a water shortage problem for the importer. The entire GVWT network could be changed by a few countries, termed "influential traders". We used eigenvector centrality to identify those influential traders. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high eigenvector centrality with large volumes of green water trade. In the case of blue water trade, western Asia, Pakistan, and India had high eigenvector centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used high proportions of internal water resources for virtual water export (32.9 and 25.1 %; thus other traders should carefully consider water resource management in these exporters.

  17. Analysis of the characteristics of the global virtual water trade network using degree and eigenvector centrality, with a focus on food and feed crops

    Science.gov (United States)

    Lee, Sang-Hyun; Mohtar, Rabi H.; Choi, Jin-Yong; Yoo, Seung-Hwan

    2016-10-01

    This study aims to analyze the characteristics of global virtual water trade (GVWT), such as the connectivity of each trader, vulnerable importers, and influential countries, using degree and eigenvector centrality during the period 2006-2010. The degree centrality was used to measure the connectivity, and eigenvector centrality was used to measure the influence on the entire GVWT network. Mexico, Egypt, China, the Republic of Korea, and Japan were classified as vulnerable importers, because they imported large quantities of virtual water with low connectivity. In particular, Egypt had a 15.3 Gm3 year-1 blue water saving effect through GVWT: the vulnerable structure could cause a water shortage problem for the importer. The entire GVWT network could be changed by a few countries, termed "influential traders". We used eigenvector centrality to identify those influential traders. In GVWT for food crops, the USA, Russian Federation, Thailand, and Canada had high eigenvector centrality with large volumes of green water trade. In the case of blue water trade, western Asia, Pakistan, and India had high eigenvector centrality. For feed crops, the green water trade in the USA, Brazil, and Argentina was the most influential. However, Argentina and Pakistan used high proportions of internal water resources for virtual water export (32.9 and 25.1 %); thus other traders should carefully consider water resource management in these exporters.

  18. The salience network is responsible for switching between the default mode network and the central executive network: replication from DCM.

    Science.gov (United States)

    Goulden, Nia; Khusnulina, Aygul; Davis, Nicholas J; Bracewell, Robert M; Bokde, Arun L; McNulty, Jonathan P; Mullins, Paul G

    2014-10-01

    With the advent of new analysis methods in neuroimaging that involve independent component analysis (ICA) and dynamic causal modelling (DCM), investigations have focused on measuring both the activity and connectivity of specific brain networks. In this study we combined DCM with spatial ICA to investigate network switching in the brain. Using time courses determined by ICA in our dynamic causal models, we focused on the dynamics of switching between the default mode network (DMN), the network which is active when the brain is not engaging in a specific task, and the central executive network (CEN), which is active when the brain is engaging in a task requiring attention. Previous work using Granger causality methods has shown that regions of the brain which respond to the degree of subjective salience of a stimulus, the salience network, are responsible for switching between the DMN and the CEN (Sridharan et al., 2008). In this work we apply DCM to ICA time courses representing these networks in resting state data. In order to test the repeatability of our work we applied this to two independent datasets. This work confirms that the salience network drives the switching between default mode and central executive networks and that our novel technique is repeatable. Crown Copyright © 2014. Published by Elsevier Inc. All rights reserved.

  19. Cropping Pattern Detection and Change Analysis in Central Luzon, Philippines Using Multi-Temporal MODIS Imagery and Artificial Neural Network Classifier

    Science.gov (United States)

    dela Torre, D. M.; Perez, G. J. P.

    2016-12-01

    Cropping practices in the Philippines has been intensifying with greater demand for food and agricultural supplies in view of an increasing population and advanced technologies for farming. This has not been monitored regularly using traditional methods but alternative methods using remote sensing has been promising yet underutilized. This study employed multi-temporal data from MODIS and neural network classifier to map annual land use in agricultural areas from 2001-2014 in Central Luzon, the primary rice growing area of the Philippines. Land use statistics derived from these maps were compared with historical El Nino events to examine how land area is affected by drought events. Fourteen maps of agricultural land use was produced, with the primary classes being single-cropping, double-cropping and perennial crops with secondary classes of forests, urban, bare, water and other classes. Primary classes were produced from the neural network classifier while secondary classes were derived from NDVI threshold masks. The overall accuracy for the 2014 map was 62.05% and a kappa statistic of 0.45. 155.56% increase in single-cropping systems from 2001 to 2014 was observed while double cropping systems decreased by 14.83%. Perennials increased by 76.21% while built-up areas decreased by 12.22% within the 14-year interval. There are several sources of error including mixed-pixels, scale-conversion problems and limited ground reference data. An analysis including El Niño events in 2004 and 2010 demonstrated that marginally irrigated areas that usually planted twice in a year resorted to single cropping, indicating that scarcity of water limited the intensification allowable in the area. Findings from this study can be used to predict future use of agricultural land in the country and also examine how farmlands have responded to climatic factors and stressors.

  20. Identification of cancer fusion drivers using network fusion centrality

    Science.gov (United States)

    Wu, Chia-Chin; Kannan, Kalpana; Lin, Steven; Yen, Laising; Milosavljevic, Aleksandar

    2013-01-01

    Summary: Gene fusions are being discovered at an increasing rate using massively parallel sequencing technologies. Prioritization of cancer fusion drivers for validation cannot be performed using traditional single-gene based methods because fusions involve portions of two partner genes. To address this problem, we propose a novel network analysis method called fusion centrality that is specifically tailored for prioritizing gene fusions. We first propose a domain-based fusion model built on the theory of exon/domain shuffling. The model leads to a hypothesis that a fusion is more likely to be an oncogenic driver if its partner genes act like hubs in a network because the fusion mutation can deregulate normal functions of many other genes and their pathways. The hypothesis is supported by the observation that for most known cancer fusion genes, at least one of the fusion partners appears to be a hub in a network, and even for many fusions both partners appear to be hubs. Based on this model, we construct fusion centrality, a multi-gene-based network metric, and use it to score fusion drivers. We show that the fusion centrality outperforms other single gene-based methods. Specifically, the method successfully predicts most of 38 newly discovered fusions that had validated oncogenic importance. To our best knowledge, this is the first network-based approach for identifying fusion drivers. Availability: Matlab code implementing the fusion centrality method is available upon request from the corresponding authors. Contact: perwu777@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:23505294

  1. Magnitude Characterization Using Complex Networks in Central Chile

    Science.gov (United States)

    Pasten, D.; Comte, D.; Munoz, V.

    2013-12-01

    Studies using complex networks are applied to many systems, like traffic, social networks, internet and earth science. In this work we make an analysis using complex networks applied to magnitude of seismicity in the central zone of Chile, we use the preferential attachment in order to construct a seismic network using local magnitudes and the hypocenters of a seismic data set in central Chile. In order to work with a complete catalogue in magnitude, the data associated with the linear part of the Gutenberg-Richter law, with magnitudes greater than 2.7, were taken. We then make a grid in space, so that each seismic event falls into a certain cell, depending on the location of its hypocenter. Now the network is constructed: the first node corresponds to the cell where the first seismic event occurs. The node has an associated number which is the magnitude of the event which occured in it, and a probability is assigned to the node. The probability is a nonlinear mapping of the magnitude (a Gaussian function was taken), so that nodes with lower magnitude events are more likely to be attached to. Each time a new node is added to the network, it is attached to the previous node which has the larger probability; the link is directed from the previous node to the new node. In this way, a directed network is constructed, with a ``preferential attachment''-like growth model, using the magnitudes as the parameter to determine the probability of attachment to future nodes. Several events could occur in the same node. In this case, the probability is calculated using the average of the magnitudes of the events occuring in that node. Once the directed network is finished, the corresponding undirected network is constructed, by making all links symmetric, and eliminating the loops which may appear when two events occur in the same cell. The resulting directed network is found to be scale free (with very low values of the power-law distribution exponent), whereas the undirected

  2. Measures of node centrality in mobile social networks

    Science.gov (United States)

    Gao, Zhenxiang; Shi, Yan; Chen, Shanzhi

    2015-02-01

    Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

  3. An agent-based model of centralized institutions, social network technology, and revolution.

    Science.gov (United States)

    Makowsky, Michael D; Rubin, Jared

    2013-01-01

    This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.

  4. An agent-based model of centralized institutions, social network technology, and revolution.

    Directory of Open Access Journals (Sweden)

    Michael D Makowsky

    Full Text Available This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.

  5. A new measure of centrality for brain networks.

    Directory of Open Access Journals (Sweden)

    Karen E Joyce

    2010-08-01

    Full Text Available Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood. Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.

  6. A new measure of centrality for brain networks.

    Science.gov (United States)

    Joyce, Karen E; Laurienti, Paul J; Burdette, Jonathan H; Hayasaka, Satoru

    2010-08-16

    Recent developments in network theory have allowed for the study of the structure and function of the human brain in terms of a network of interconnected components. Among the many nodes that form a network, some play a crucial role and are said to be central within the network structure. Central nodes may be identified via centrality metrics, with degree, betweenness, and eigenvector centrality being three of the most popular measures. Degree identifies the most connected nodes, whereas betweenness centrality identifies those located on the most traveled paths. Eigenvector centrality considers nodes connected to other high degree nodes as highly central. In the work presented here, we propose a new centrality metric called leverage centrality that considers the extent of connectivity of a node relative to the connectivity of its neighbors. The leverage centrality of a node in a network is determined by the extent to which its immediate neighbors rely on that node for information. Although similar in concept, there are essential differences between eigenvector and leverage centrality that are discussed in this manuscript. Degree, betweenness, eigenvector, and leverage centrality were compared using functional brain networks generated from healthy volunteers. Functional cartography was also used to identify neighborhood hubs (nodes with high degree within a network neighborhood). Provincial hubs provide structure within the local community, and connector hubs mediate connections between multiple communities. Leverage proved to yield information that was not captured by degree, betweenness, or eigenvector centrality and was more accurate at identifying neighborhood hubs. We propose that this metric may be able to identify critical nodes that are highly influential within the network.

  7. EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS*

    Science.gov (United States)

    TAYLOR, DANE; MYERS, SEAN A.; CLAUSET, AARON; PORTER, MASON A.; MUCHA, PETER J.

    2017-01-01

    Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes’ centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court. PMID:29046619

  8. EIGENVECTOR-BASED CENTRALITY MEASURES FOR TEMPORAL NETWORKS.

    Science.gov (United States)

    Taylor, Dane; Myers, Sean A; Clauset, Aaron; Porter, Mason A; Mucha, Peter J

    2017-01-01

    Numerous centrality measures have been developed to quantify the importances of nodes in time-independent networks, and many of them can be expressed as the leading eigenvector of some matrix. With the increasing availability of network data that changes in time, it is important to extend such eigenvector-based centrality measures to time-dependent networks. In this paper, we introduce a principled generalization of network centrality measures that is valid for any eigenvector-based centrality. We consider a temporal network with N nodes as a sequence of T layers that describe the network during different time windows, and we couple centrality matrices for the layers into a supra-centrality matrix of size NT × NT whose dominant eigenvector gives the centrality of each node i at each time t. We refer to this eigenvector and its components as a joint centrality, as it reflects the importances of both the node i and the time layer t. We also introduce the concepts of marginal and conditional centralities, which facilitate the study of centrality trajectories over time. We find that the strength of coupling between layers is important for determining multiscale properties of centrality, such as localization phenomena and the time scale of centrality changes. In the strong-coupling regime, we derive expressions for time-averaged centralities, which are given by the zeroth-order terms of a singular perturbation expansion. We also study first-order terms to obtain first-order-mover scores, which concisely describe the magnitude of nodes' centrality changes over time. As examples, we apply our method to three empirical temporal networks: the United States Ph.D. exchange in mathematics, costarring relationships among top-billed actors during the Golden Age of Hollywood, and citations of decisions from the United States Supreme Court.

  9. Understanding complex interactions using social network analysis.

    Science.gov (United States)

    Pow, Janette; Gayen, Kaberi; Elliott, Lawrie; Raeside, Robert

    2012-10-01

    The aim of this paper is to raise the awareness of social network analysis as a method to facilitate research in nursing research. The application of social network analysis in assessing network properties has allowed greater insight to be gained in many areas including sociology, politics, business organisation and health care. However, the use of social networks in nursing has not received sufficient attention. Review of literature and illustration of the application of the method of social network analysis using research examples. First, the value of social networks will be discussed. Then by using illustrative examples, the value of social network analysis to nursing will be demonstrated. The method of social network analysis is found to give greater insights into social situations involving interactions between individuals and has particular application to the study of interactions between nurses and between nurses and patients and other actors. Social networks are systems in which people interact. Two quantitative techniques help our understanding of these networks. The first is visualisation of the network. The second is centrality. Individuals with high centrality are key communicators in a network. Applying social network analysis to nursing provides a simple method that helps gain an understanding of human interaction and how this might influence various health outcomes. It allows influential individuals (actors) to be identified. Their influence on the formation of social norms and communication can determine the extent to which new interventions or ways of thinking are accepted by a group. Thus, working with key individuals in a network could be critical to the success and sustainability of an intervention. Social network analysis can also help to assess the effectiveness of such interventions for the recipient and the service provider. © 2012 Blackwell Publishing Ltd.

  10. Node and layer eigenvector centralities for multiplex networks

    OpenAIRE

    Tudisco, Francesco; Arrigo, Francesca; Gautier, Antoine

    2017-01-01

    Eigenvector-based centrality measures are among the most popular centrality measures in network science. The underlying idea is intuitive and the mathematical description is extremely simple in the framework of standard, mono-layer networks. Moreover, several efficient computational tools are available for their computation. Moving up in dimensionality, several efforts have been made in the past to describe an eigenvector-based centrality measure that generalizes Bonacich index to the case of...

  11. CFO finance network centrality, errors and internal control material weaknessess

    NARCIS (Netherlands)

    Schabus, M.

    2015-01-01

    CFOs finance networks matter in determining certain accounting and reporting outcomes. Drawing on social network theory, this study shows that CFO centrality in a network of financial experts is inversely related to the occurrence of restatements due to errors and disclosure of internal control

  12. Centralities in simplicial complexes. Applications to protein interaction networks.

    Science.gov (United States)

    Estrada, Ernesto; Ross, Grant J

    2018-02-07

    Complex networks can be used to represent complex systems which originate in the real world. Here we study a transformation of these complex networks into simplicial complexes, where cliques represent the simplices of the complex. We extend the concept of node centrality to that of simplicial centrality and study several mathematical properties of degree, closeness, betweenness, eigenvector, Katz, and subgraph centrality for simplicial complexes. We study the degree distributions of these centralities at the different levels. We also compare and describe the differences between the centralities at the different levels. Using these centralities we study a method for detecting essential proteins in PPI networks of cells and explain the varying abilities of the centrality measures at the different levels in identifying these essential proteins. Copyright © 2017 Elsevier Ltd. All rights reserved.

  13. Social network analysis and dual rover communications

    Science.gov (United States)

    Litaker, Harry L.; Howard, Robert L.

    2013-10-01

    Social network analysis (SNA) refers to the collection of techniques, tools, and methods used in sociometry aiming at the analysis of social networks to investigate decision making, group communication, and the distribution of information. Human factors engineers at the National Aeronautics and Space Administration (NASA) conducted a social network analysis on communication data collected during a 14-day field study operating a dual rover exploration mission to better understand the relationships between certain network groups such as ground control, flight teams, and planetary science. The analysis identified two communication network structures for the continuous communication and Twice-a-Day Communication scenarios as a split network and negotiated network respectfully. The major nodes or groups for the networks' architecture, transmittal status, and information were identified using graphical network mapping, quantitative analysis of subjective impressions, and quantified statistical analysis using Sociometric Statue and Centrality. Post-questionnaire analysis along with interviews revealed advantages and disadvantages of each network structure with team members identifying the need for a more stable continuous communication network, improved robustness of voice loops, and better systems training/capabilities for scientific imagery data and operational data during Twice-a-Day Communications.

  14. Social network analysis

    NARCIS (Netherlands)

    de Nooy, W.; Crothers, C.

    2009-01-01

    Social network analysis (SNA) focuses on the structure of ties within a set of social actors, e.g., persons, groups, organizations, and nations, or the products of human activity or cognition such as web sites, semantic concepts, and so on. It is linked to structuralism in sociology stressing the

  15. Centralized Networks to Generate Human Body Motions.

    Science.gov (United States)

    Vakulenko, Sergei; Radulescu, Ovidiu; Morozov, Ivan; Weber, Andres

    2017-12-14

    We consider continuous-time recurrent neural networks as dynamical models for the simulation of human body motions. These networks consist of a few centers and many satellites connected to them. The centers evolve in time as periodical oscillators with different frequencies. The center states define the satellite neurons' states by a radial basis function (RBF) network. To simulate different motions, we adjust the parameters of the RBF networks. Our network includes a switching module that allows for turning from one motion to another. Simulations show that this model allows us to simulate complicated motions consisting of many different dynamical primitives. We also use the model for learning human body motion from markers' trajectories. We find that center frequencies can be learned from a small number of markers and can be transferred to other markers, such that our technique seems to be capable of correcting for missing information resulting from sparse control marker settings.

  16. Network performance analysis

    CERN Document Server

    Bonald, Thomas

    2013-01-01

    The book presents some key mathematical tools for the performance analysis of communication networks and computer systems.Communication networks and computer systems have become extremely complex. The statistical resource sharing induced by the random behavior of users and the underlying protocols and algorithms may affect Quality of Service.This book introduces the main results of queuing theory that are useful for analyzing the performance of these systems. These mathematical tools are key to the development of robust dimensioning rules and engineering methods. A number of examples i

  17. mRNA chip-based analysis on transcription factor regulatory network central nodes of protection targets of Deproteinized Extract of Calf Blood on acute liver injury in mice.

    Science.gov (United States)

    Xu, Guangyu; Xu, Jinhe; Han, Xiao; Li, Hongyu; Yuan, Guangxin; An, Liping; Du, Peige

    2018-01-27

    Our previous study found that Deproteinized Extract of Calf Blood (DECB) could protect the acute liver injury induced by carbon tetrachloride in mice, but the target-related transcription factors and their regulatory networks were not comprehensively studied. Based on the mRNA expression microarray data obtained in the previous study, the mRNA transcription factor regulatory networks were constructed by screening the transcription factors of differentially expressed genes and their corresponding target proteins, and the analysis on the functions and pathways of the regulatory network central nodes was performed. Eight genes Ltf, Tnf, Il6, Jun, Il12b, Stat3, Rel and Crem could regulate the inflammatory factors, and TNF signaling pathway and Jak-STAT signaling pathway might play an important role in the mechanism through which DECB protected the liver of mice. DECB can not only inhibit the apoptosis of hepatocytes, but also inhibit the inflammatory cytokines. Copyright © 2018. Published by Elsevier B.V.

  18. Network systems security analysis

    Science.gov (United States)

    Yilmaz, Ä.°smail

    2015-05-01

    Network Systems Security Analysis has utmost importance in today's world. Many companies, like banks which give priority to data management, test their own data security systems with "Penetration Tests" by time to time. In this context, companies must also test their own network/server systems and take precautions, as the data security draws attention. Based on this idea, the study cyber-attacks are researched throughoutly and Penetration Test technics are examined. With these information on, classification is made for the cyber-attacks and later network systems' security is tested systematically. After the testing period, all data is reported and filed for future reference. Consequently, it is found out that human beings are the weakest circle of the chain and simple mistakes may unintentionally cause huge problems. Thus, it is clear that some precautions must be taken to avoid such threats like updating the security software.

  19. Educational Research Network for West and Central Africa ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    This grant will assist the Educational Research Network for West and Central Africa (ERNWACA) by providing funding for succession planning, recruiting a regional coordinator (to be based in Mali) and strengthening the Network's ... IDRC partner the World Economic Forum is building a hub for inclusive growth solutions.

  20. The efficiency of the Norwegian central network; Effektiviteten i Sentralnettet

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1997-12-31

    This report from a project on the efficiency of the central electricity transfer network in Norway (1) examines relevant methods and principles for evaluation and follow-up of the efficiency of the network, (2) elucidates the choice of method by means of examples, (3) analyses the connection between possibilities for efficiency improvement, need for investment, and income and yield in Statnet. Statnet is one of the 40 owners of the central network. The two methods currently used to measure efficiency differences are discussed, one of them further developed and applied to the comparison of costs in various central networks (in different countries) and to operation and maintenance of central network systems. The efficiency of the Norwegian network was found to be less than that of the Swedish network. The difference between the two networks as to operational costs was larger than the difference between the Norwegian owners. The results for capital costs are uncertain because they are based on historical data of questionable quality. 1 ref

  1. Range-limited centrality measures in complex networks

    Science.gov (United States)

    Ercsey-Ravasz, Mária; Lichtenwalter, Ryan N.; Chawla, Nitesh V.; Toroczkai, Zoltán

    2012-06-01

    Here we present a range-limited approach to centrality measures in both nonweighted and weighted directed complex networks. We introduce an efficient method that generates for every node and every edge its betweenness centrality based on shortest paths of lengths not longer than ℓ=1,...,L in the case of nonweighted networks, and for weighted networks the corresponding quantities based on minimum weight paths with path weights not larger than wℓ=ℓΔ, ℓ=1,2...,L=R/Δ. These measures provide a systematic description on the positioning importance of a node (edge) with respect to its network neighborhoods one step out, two steps out, etc., up to and including the whole network. They are more informative than traditional centrality measures, as network transport typically happens on all length scales, from transport to nearest neighbors to the farthest reaches of the network. We show that range-limited centralities obey universal scaling laws for large nonweighted networks. As the computation of traditional centrality measures is costly, this scaling behavior can be exploited to efficiently estimate centralities of nodes and edges for all ranges, including the traditional ones. The scaling behavior can also be exploited to show that the ranking top list of nodes (edges) based on their range-limited centralities quickly freezes as a function of the range, and hence the diameter-range top list can be efficiently predicted. We also show how to estimate the typical largest node-to-node distance for a network of N nodes, exploiting the afore-mentioned scaling behavior. These observations were made on model networks and on a large social network inferred from cell-phone trace logs (˜5.5×106 nodes and ˜2.7×107 edges). Finally, we apply these concepts to efficiently detect the vulnerability backbone of a network (defined as the smallest percolating cluster of the highest betweenness nodes and edges) and illustrate the importance of weight-based centrality measures in

  2. Analysis of computer networks

    CERN Document Server

    Gebali, Fayez

    2015-01-01

    This textbook presents the mathematical theory and techniques necessary for analyzing and modeling high-performance global networks, such as the Internet. The three main building blocks of high-performance networks are links, switching equipment connecting the links together, and software employed at the end nodes and intermediate switches. This book provides the basic techniques for modeling and analyzing these last two components. Topics covered include, but are not limited to: Markov chains and queuing analysis, traffic modeling, interconnection networks and switch architectures and buffering strategies.   ·         Provides techniques for modeling and analysis of network software and switching equipment; ·         Discusses design options used to build efficient switching equipment; ·         Includes many worked examples of the application of discrete-time Markov chains to communication systems; ·         Covers the mathematical theory and techniques necessary for ana...

  3. Centrality in children's best friend networks: the role of social behaviour.

    Science.gov (United States)

    Betts, Lucy R; Stiller, James

    2014-03-01

    Centrality is an indicator of an individual's relative importance within a social group. Predictors of centrality in best friendship networks were examined in 146 children (70 boys and 76 girls, Mage  = 9.95). Children completed measures of social confidence, social desirability, friendship quality, school liking, and loneliness and nominated their best friends from within their class at two time points, 3 months apart. Multigroup path analysis revealed gender differences in the antecedents of centrality. Social confidence, social desirability, and friendship quality predicted changes in the indicators of centrality in best friend networks over time. Boys' social behaviour positively predicted changes in centrality, whereas girls' social behaviour negatively predicted changes in centrality. Together, these findings suggest that some aspects of social behaviour are influential for centrality in best friend groups. © 2013 The British Psychological Society.

  4. Weighted Complex Network Analysis of Pakistan Highways

    Directory of Open Access Journals (Sweden)

    Yasir Tariq Mohmand

    2013-01-01

    Full Text Available The structure and properties of public transportation networks have great implications in urban planning, public policies, and infectious disease control. This study contributes a weighted complex network analysis of travel routes on the national highway network of Pakistan. The network is responsible for handling 75 percent of the road traffic yet is largely inadequate, poor, and unreliable. The highway network displays small world properties and is assortative in nature. Based on the betweenness centrality of the nodes, the most important cities are identified as this could help in identifying the potential congestion points in the network. Keeping in view the strategic location of Pakistan, such a study is of practical importance and could provide opportunities for policy makers to improve the performance of the highway network.

  5. Network centrality measures and systemic risk: An application to the Turkish financial crisis

    Science.gov (United States)

    Kuzubaş, Tolga Umut; Ömercikoğlu, Inci; Saltoğlu, Burak

    2014-07-01

    In this paper, we analyze the performance of several network centrality measures in detecting systemically important financial institutions (SIFI) using data from the Turkish Interbank market during the financial crisis in 2000. We employ various network investigation tools such as volume, transactions, links, connectivity and reciprocity to gain a clearer picture of the network topology of the interbank market. We study the main borrower role of Demirbank in the crash of the banking system with network centrality measures which are extensively used in the network theory. This ex-post analysis of the crisis shows that centrality measures perform well in identifying and monitoring systemically important financial institutions which provide useful insights for financial regulations.

  6. A dynamical approach to identify vertices' centrality in complex networks

    Science.gov (United States)

    Guo, Long; Zhang, Wen-Yao; Luo, Zhong-Jie; Gao, Fu-Juan; Zhang, Yi-Cheng

    2017-12-01

    In this paper, we proposed a dynamical approach to assess vertices' centrality according to the synchronization process of the Kuramoto model. In our approach, the vertices' dynamical centrality is calculated based on the Difference of vertices' Synchronization Abilities (DSA), which are different from traditional centrality measurements that are related to the topological properties. Through applying our approach to complex networks with a clear community structure, we have calculated all vertices' dynamical centrality and found that vertices at the end of weak links have higher dynamical centrality. Meanwhile, we analyzed the robustness and efficiency of our dynamical approach through testing the probabilities that some known vital vertices were recognized. Finally, we applied our dynamical approach to identify community due to its satisfactory performance in assessing overlapping vertices. Our present work provides a new perspective and tools to understand the crucial role of heterogeneity in revealing the interplay between the dynamics and structure of complex networks.

  7. [Central venous blood gas analysis].

    Science.gov (United States)

    Marano, Marco; D'Amato, Anna; Guiotto, Giovanna; Schiraldi, Fernando

    2015-01-01

    The hemodialysis might interfere with patients hemodynamic, as the technique allows a sophisticated game with extra and intravascular fluids. As the cardiocirculatory response could sometimes be unpredictable, it is interesting to collect valuable information by reaching a deep understanding of the tissue metabolism which is mirrored by the blood gas analysis of variations in arterial and central venous blood samples. Particularly interesting are the time course variations of the central venous hemoglobin saturation (ScvO2), which are directly related to the patient with O2-demand as well as to the O2-Delivery (DO2). The ScvO2 is determined by four parameters (cardiac output, Hb concentration, arterial Hb saturation and O2 consumption): If the fluids subtraction during dialysis was about to determine an occult hypoperfusion, the ScvO2 reduction would be a timely warning sign to be considered. Moreover, while the normal veno-arterial PCO2 difference is 2-4 mmHg, whenever a mismatch between O2-demand and DO2arise, a larger v-aPCO2 difference should be observed.

  8. Complex Network Analysis of Guangzhou Metro

    Directory of Open Access Journals (Sweden)

    Yasir Tariq Mohmand

    2015-11-01

    Full Text Available The structure and properties of public transportation networks can provide suggestions for urban planning and public policies. This study contributes a complex network analysis of the Guangzhou metro. The metro network has 236 kilometers of track and is the 6th busiest metro system of the world. In this paper topological properties of the network are explored. We observed that the network displays small world properties and is assortative in nature. The network possesses a high average degree of 17.5 with a small diameter of 5. Furthermore, we also identified the most important metro stations based on betweenness and closeness centralities. These could help in identifying the probable congestion points in the metro system and provide policy makers with an opportunity to improve the performance of the metro system.

  9. Does antimicrobial lock solution reduce catheter-related infections in hemodialysis patients with central venous catheters? A Bayesian network meta-analysis.

    Science.gov (United States)

    Zhang, Jun; Wang, Bo; Li, Rongke; Ge, Long; Chen, Kee-Hsin; Tian, Jinhui

    2017-04-01

    The purpose of our study is to carry out a Bayesian network meta-analysis comparing the efficacy of different antimicrobial lock solutions (ALS) for prevention of catheter-related infections (CRI) in patients with hemodialysis (HD) and ranking these ALS for practical consideration. We searched six electronic databases, earlier relevant meta-analysis and reference lists of included studies for randomized controlled trials (RCTs) that compared ALS for preventing episodes of CRI in patients with HD either head-to-head or against control interventions using non-ALS. Two authors independently assessed the methodological quality of included studies using the Cochrane risk of bias tool and extracted relevant information according to a predesigned extraction form. Data were analysed using the WinBUGS (V.1.4.3) and the Stata (V.13.0). Finally, 18 studies involving 2395 patients and evaluating 9 ALS strategies were included. Network meta-analysis showed that gentamicin plus citrate (OR 0.07, 95% CrI 0.00-0.48) and gentamicin plus heparin (OR 0.04, 95% CrI 0.00-0.23) were statistically superior to heparin alone in terms of reducing CRBSI. For exit site infection and all-cause mortality, no significant difference in the intervention effect (p > 0.05) was detected for all included ALS when compared to heparin. Moreover, all ALS were similar in efficacy (p > 0.05) from each other for CRBSI, exit site infection and all-cause mortality. Our findings indicated that gentamicin plus heparin may be selected for the prophylaxis of CRI in patients undergoing HD with CVCs. Whether this strategy will lead to antimicrobial resistance remains unclear in view of the relatively short duration of included studies. More attentions should be made regarding head-to-head comparisons of the most commonly used ALS in this field.

  10. Central Difference Formula in Numerical Analysis.

    Science.gov (United States)

    de Alwis, Tilak

    1992-01-01

    Describes numerical differentiation and the central difference formula in numerical analysis. Presents three computer programs that approximate the first derivative of a function utilizing the central difference formula. Analyzes conditions under which the approximation formula is exact. (MDH)

  11. Social Network Centrality and Leadership Status

    Science.gov (United States)

    Lansford, Jennifer E.; Costanzo, Philip R.; Grimes, Christina; Putallaz, Martha; Miller, Shari; Malone, Patrick S.

    2009-01-01

    Seventh-grade students (N = 324) completed social cognitive maps to identify peer groups and peer group leaders, sociometric nominations to describe their peers’ behaviors, and questionnaires to assess their own behaviors. Peer group members resembled one another in levels of direct and indirect aggression and substance use; girls’ cliques were more behaviorally homogenous than were boys’ cliques. On average, leaders (especially if they were boys) were perceived as engaging in more problem behaviors than were nonleaders. In girls’ cliques, peripheral group members were more similar to their group leader on indirect aggression than were girls who were more central to the clique. Peer leaders perceived themselves as being more able to influence peers but did not differ from nonleaders in their perceived susceptibility to peer influence. The findings contribute to our understanding of processes through which influence may occur in adolescent peer groups. PMID:19763241

  12. Industrial entrepreneurial network: Structural and functional analysis

    Science.gov (United States)

    Medvedeva, M. A.; Davletbaev, R. H.; Berg, D. B.; Nazarova, J. J.; Parusheva, S. S.

    2016-12-01

    Structure and functioning of two model industrial entrepreneurial networks are investigated in the present paper. One of these networks is forming when implementing an integrated project and consists of eight agents, which interact with each other and external environment. The other one is obtained from the municipal economy and is based on the set of the 12 real business entities. Analysis of the networks is carried out on the basis of the matrix of mutual payments aggregated over the certain time period. The matrix is created by the methods of experimental economics. Social Network Analysis (SNA) methods and instruments were used in the present research. The set of basic structural characteristics was investigated: set of quantitative parameters such as density, diameter, clustering coefficient, different kinds of centrality, and etc. They were compared with the random Bernoulli graphs of the corresponding size and density. Discovered variations of random and entrepreneurial networks structure are explained by the peculiarities of agents functioning in production network. Separately, were identified the closed exchange circuits (cyclically closed contours of graph) forming an autopoietic (self-replicating) network pattern. The purpose of the functional analysis was to identify the contribution of the autopoietic network pattern in its gross product. It was found that the magnitude of this contribution is more than 20%. Such value allows using of the complementary currency in order to stimulate economic activity of network agents.

  13. Properties of centralized cooperative sensing in cognitive radio networks

    Science.gov (United States)

    Skokowski, Paweł; Malon, Krzysztof; Łopatka, Jerzy

    2017-04-01

    Spectrum sensing is a functionality that enables network creation in the cognitive radio technology. Spectrum sensing is use for building the situation awareness knowledge for better use of radio resources and to adjust network parameters in case of jamming, interferences from legacy systems, decreasing link quality caused e.g. by nodes positions changes. This paper presents results from performed tests to compare cooperative centralized sensing versus local sensing. All tests were performed in created simulator developed in Matlab/Simulink environment.

  14. Social network analysis applied to team sports analysis

    CERN Document Server

    Clemente, Filipe Manuel; Mendes, Rui Sousa

    2016-01-01

    Explaining how graph theory and social network analysis can be applied to team sports analysis, This book presents useful approaches, models and methods that can be used to characterise the overall properties of team networks and identify the prominence of each team player. Exploring the different possible network metrics that can be utilised in sports analysis, their possible applications and variances from situation to situation, the respective chapters present an array of illustrative case studies. Identifying the general concepts of social network analysis and network centrality metrics, readers are shown how to generate a methodological protocol for data collection. As such, the book provides a valuable resource for students of the sport sciences, sports engineering, applied computation and the social sciences.

  15. Multidimensional Analysis of Linguistic Networks

    Science.gov (United States)

    Araújo, Tanya; Banisch, Sven

    Network-based approaches play an increasingly important role in the analysis of data even in systems in which a network representation is not immediately apparent. This is particularly true for linguistic networks, which use to be induced from a linguistic data set for which a network perspective is only one out of several options for representation. Here we introduce a multidimensional framework for network construction and analysis with special focus on linguistic networks. Such a framework is used to show that the higher is the abstraction level of network induction, the harder is the interpretation of the topological indicators used in network analysis. Several examples are provided allowing for the comparison of different linguistic networks as well as to networks in other fields of application of network theory. The computation and the intelligibility of some statistical indicators frequently used in linguistic networks are discussed. It suggests that the field of linguistic networks, by applying statistical tools inspired by network studies in other domains, may, in its current state, have only a limited contribution to the development of linguistic theory.

  16. Improving Dense Network Performance through Centralized Scheduling and Interference Coordination

    DEFF Research Database (Denmark)

    Lopez, Victor Fernandez; Pedersen, Klaus I.; Alvarez, Beatriz Soret

    2017-01-01

    and the receiver sides. As a network coordination scheme, we apply a centralized joint cell association and scheduling mechanism based on dynamic cell switching, by which users are not always served by the strongest perceived cell. The method simultaneously resultsin more balanced loads and increased performance...

  17. Using Network Centrality Measures to Improve National Journal Classification Lists

    DEFF Research Database (Denmark)

    Zuccala, Alesia Ann; Robinson-Garcia, Nicolas; Repiso, Rafael

    2017-01-01

    (as in the latter). This can create a few problems. Based on a sample of Library and Information Science publications, the aim of this paper is to examine both the Danish and Spanish classification lists, and determine the potential use of network centrality measures for identifying possible...

  18. West and Central African Research and Education Networking ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    West and Central African Research and Education Networking (WACREN). For universities and research centres around the world, the Internet has become an important resource for teaching, learning and research. But, African universities have always faced important challenges to accessing cheap and reliable bandwidth ...

  19. Communication for Influence : Building ICTD Networks in Central ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    This project seeks to help achieve universal affordable access to broadband information and communication technology (ICT) infrastructure in a number of countries on the East and West coasts of Africa. It will do so by building regional ICT for development (ICTD) research and advocacy networks in Central, East and West ...

  20. Communication for Influence : Building ICTD Networks in Central ...

    International Development Research Centre (IDRC) Digital Library (Canada)

    Communication for Influence : Building ICTD Networks in Central, East and West Africa. This project seeks to help achieve universal affordable access to broadband information and communication technology (ICT) infrastructure in a number of countries on ... Bénin : lí oí¹ il faut 3, 4, même 5 cartes SIM pour faire un appel.

  1. Centrality measures and thermodynamic formalism for complex networks.

    Science.gov (United States)

    Delvenne, Jean-Charles; Libert, Anne-Sophie

    2011-04-01

    In the study of small and large networks it is customary to perform a simple random walk where the random walker jumps from one node to one of its neighbors with uniform probability. The properties of this random walk are intimately related to the combinatorial properties of the network. In this paper we propose to use the Ruelle-Bowens random walk instead, whose probability transitions are chosen in order to maximize the entropy rate of the walk on an unweighted graph. If the graph is weighted, then a free energy is optimized instead of the entropy rate. Specifically, we introduce a centrality measure for large networks, which is the stationary distribution attained by the Ruelle-Bowens random walk; we name it entropy rank. We introduce a more general version, which is able to deal with disconnected networks, under the name of free-energy rank. We compare the properties of those centrality measures with the classic PageRank and hyperlink-induced topic search (HITS) on both toy and real-life examples, in particular their robustness to small modifications of the network. We show that our centrality measures are more discriminating than PageRank, since they are able to distinguish clearly pages that PageRank regards as almost equally interesting, and are more sensitive to the medium-scale details of the graph.

  2. Network Analysis, Architecture, and Design

    CERN Document Server

    McCabe, James D

    2007-01-01

    Traditionally, networking has had little or no basis in analysis or architectural development, with designers relying on technologies they are most familiar with or being influenced by vendors or consultants. However, the landscape of networking has changed so that network services have now become one of the most important factors to the success of many third generation networks. It has become an important feature of the designer's job to define the problems that exist in his network, choose and analyze several optimization parameters during the analysis process, and then prioritize and evalua

  3. Network topology analysis.

    Energy Technology Data Exchange (ETDEWEB)

    Kalb, Jeffrey L.; Lee, David S.

    2008-01-01

    Emerging high-bandwidth, low-latency network technology has made network-based architectures both feasible and potentially desirable for use in satellite payload architectures. The selection of network topology is a critical component when developing these multi-node or multi-point architectures. This study examines network topologies and their effect on overall network performance. Numerous topologies were reviewed against a number of performance, reliability, and cost metrics. This document identifies a handful of good network topologies for satellite applications and the metrics used to justify them as such. Since often multiple topologies will meet the requirements of the satellite payload architecture under development, the choice of network topology is not easy, and in the end the choice of topology is influenced by both the design characteristics and requirements of the overall system and the experience of the developer.

  4. Small vessel disease and cognitive impairment : The relevance of central network connections

    NARCIS (Netherlands)

    Reijmer, Yael D.; Fotiadis, Panagiotis; Piantoni, Giovanni; Boulouis, Gregoire; Kelly, Kathleen E.; Gurol, Mahmut E.; Leemans, Alexander; O'Sullivan, Michael J.; Greenberg, Steven M.; Viswanathan, Anand

    2016-01-01

    Central brain network connections greatly contribute to overall network efficiency. Here we examined whether small vessel disease (SVD) related white matter alterations in central brain network connections have a greater impact on executive functioning than alterations in non-central brain network

  5. How Central Is Too Central? Organizing Interorganizational Collaboration Networks for Breakthrough Innovation

    NARCIS (Netherlands)

    Dong, John Qi; McCarthy, Killian; Schoenmakers, Wilfred

    Firms increasingly look to collaboration with alliance partners in their quest for breakthrough innovation. But how does the position of a firm in its alliance network weighted by the centrality of its partnersa concept which we term partner-weighted alliance centralityand the heterogeneities in the

  6. Modeling infection transmission in primate networks to predict centrality-based risk.

    Science.gov (United States)

    Romano, Valéria; Duboscq, Julie; Sarabian, Cécile; Thomas, Elodie; Sueur, Cédric; MacIntosh, Andrew J J

    2016-07-01

    Social structure can theoretically regulate disease risk by mediating exposure to pathogens via social proximity and contact. Investigating the role of central individuals within a network may help predict infectious agent transmission as well as implement disease control strategies, but little is known about such dynamics in real primate networks. We combined social network analysis and a modeling approach to better understand transmission of a theoretical infectious agent in wild Japanese macaques, highly social animals which form extended but highly differentiated social networks. We collected focal data from adult females living on the islands of Koshima and Yakushima, Japan. Individual identities as well as grooming networks were included in a Markov graph-based simulation. In this model, the probability that an individual will transmit an infectious agent depends on the strength of its relationships with other group members. Similarly, its probability of being infected depends on its relationships with already infected group members. We correlated: (i) the percentage of subjects infected during a latency-constrained epidemic; (ii) the mean latency to complete transmission; (iii) the probability that an individual is infected first among all group members; and (iv) each individual's mean rank in the chain of transmission with different individual network centralities (eigenvector, strength, betweenness). Our results support the hypothesis that more central individuals transmit infections in a shorter amount of time and to more subjects but also become infected more quickly than less central individuals. However, we also observed that the spread of infectious agents on the Yakushima network did not always differ from expectations of spread on random networks. Generalizations about the importance of observed social networks in pathogen flow should thus be made with caution, since individual characteristics in some real world networks appear less relevant than

  7. Multilayer motif analysis of brain networks

    Science.gov (United States)

    Battiston, Federico; Nicosia, Vincenzo; Chavez, Mario; Latora, Vito

    2017-04-01

    In the last decade, network science has shed new light both on the structural (anatomical) and on the functional (correlations in the activity) connectivity among the different areas of the human brain. The analysis of brain networks has made possible to detect the central areas of a neural system and to identify its building blocks by looking at overabundant small subgraphs, known as motifs. However, network analysis of the brain has so far mainly focused on anatomical and functional networks as separate entities. The recently developed mathematical framework of multi-layer networks allows us to perform an analysis of the human brain where the structural and functional layers are considered together. In this work, we describe how to classify the subgraphs of a multiplex network, and we extend the motif analysis to networks with an arbitrary number of layers. We then extract multi-layer motifs in brain networks of healthy subjects by considering networks with two layers, anatomical and functional, respectively, obtained from diffusion and functional magnetic resonance imaging. Results indicate that subgraphs in which the presence of a physical connection between brain areas (links at the structural layer) coexists with a non-trivial positive correlation in their activities are statistically overabundant. Finally, we investigate the existence of a reinforcement mechanism between the two layers by looking at how the probability to find a link in one layer depends on the intensity of the connection in the other one. Showing that functional connectivity is non-trivially constrained by the underlying anatomical network, our work contributes to a better understanding of the interplay between the structure and function in the human brain.

  8. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

    Directory of Open Access Journals (Sweden)

    Divya Mistry

    Full Text Available Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1 inclusion or exclusion of gene co-expression data, (2 impact of different coexpression measures, and (3 impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The

  9. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network.

    Science.gov (United States)

    Mistry, Divya; Wise, Roger P; Dickerson, Julie A

    2017-01-01

    Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network's adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can be

  10. Spontaneous centralization of control in a network of company ownerships.

    Directory of Open Access Journals (Sweden)

    Sebastian M Krause

    Full Text Available We introduce a model for the adaptive evolution of a network of company ownerships. In a recent work it has been shown that the empirical global network of corporate control is marked by a central, tightly connected "core" made of a small number of large companies which control a significant part of the global economy. Here we show how a simple, adaptive "rich get richer" dynamics can account for this characteristic, which incorporates the increased buying power of more influential companies, and in turn results in even higher control. We conclude that this kind of centralized structure can emerge without it being an explicit goal of these companies, or as a result of a well-organized strategy.

  11. Social network analysis to cluster sociobibliometric information

    Directory of Open Access Journals (Sweden)

    Jorge Ricardo Vivas

    Full Text Available This paper examines the benefits of using Social Network Analysis in the field of sociobibliometric exploration. There are considered practical and conceptual limits and reaches. The proposal is illustrated through a study about a journals network of behavior modification by Peiró and Carpintero (1981. In this context it is shown the utility of using reticular properties of Density, Centrality, Betweenness, Power and Clusterig as indicators that allow obtaining novel and complementary information to the one extracted by the classic methods of bibliometric exploration.

  12. Tourism Destinations Network Analysis, Social Network Analysis Approach

    Directory of Open Access Journals (Sweden)

    2015-09-01

    Full Text Available The tourism industry is becoming one of the world's largest economical sources, and is expected to become the world's first industry by 2020. Previous studies have focused on several aspects of this industry including sociology, geography, tourism management and development, but have paid less attention to analytical and quantitative approaches. This study introduces some network analysis techniques and measures aiming at studying the structural characteristics of tourism networks. More specifically, it presents a methodology to analyze tourism destinations network. We apply the methodology to analyze mazandaran’s Tourism destination network, one of the most famous tourism areas of Iran.

  13. Introduction to Social Network Analysis

    Science.gov (United States)

    Zaphiris, Panayiotis; Ang, Chee Siang

    Social Network analysis focuses on patterns of relations between and among people, organizations, states, etc. It aims to describe networks of relations as fully as possible, identify prominent patterns in such networks, trace the flow of information through them, and discover what effects these relations and networks have on people and organizations. Social network analysis offers a very promising potential for analyzing human-human interactions in online communities (discussion boards, newsgroups, virtual organizations). This Tutorial provides an overview of this analytic technique and demonstrates how it can be used in Human Computer Interaction (HCI) research and practice, focusing especially on Computer Mediated Communication (CMC). This topic acquires particular importance these days, with the increasing popularity of social networking websites (e.g., youtube, myspace, MMORPGs etc.) and the research interest in studying them.

  14. DiffSLC: A graph centrality method to detect essential proteins of a protein-protein interaction network

    Science.gov (United States)

    Wise, Roger P.; Dickerson, Julie A.

    2017-01-01

    Identification of central genes and proteins in biomolecular networks provides credible candidates for pathway analysis, functional analysis, and essentiality prediction. The DiffSLC centrality measure predicts central and essential genes and proteins using a protein-protein interaction network. Network centrality measures prioritize nodes and edges based on their importance to the network topology. These measures helped identify critical genes and proteins in biomolecular networks. The proposed centrality measure, DiffSLC, combines the number of interactions of a protein and the gene coexpression values of genes from which those proteins were translated, as a weighting factor to bias the identification of essential proteins in a protein interaction network. Potentially essential proteins with low node degree are promoted through eigenvector centrality. Thus, the gene coexpression values are used in conjunction with the eigenvector of the network’s adjacency matrix and edge clustering coefficient to improve essentiality prediction. The outcome of this prediction is shown using three variations: (1) inclusion or exclusion of gene co-expression data, (2) impact of different coexpression measures, and (3) impact of different gene expression data sets. For a total of seven networks, DiffSLC is compared to other centrality measures using Saccharomyces cerevisiae protein interaction networks and gene expression data. Comparisons are also performed for the top ranked proteins against the known essential genes from the Saccharomyces Gene Deletion Project, which show that DiffSLC detects more essential proteins and has a higher area under the ROC curve than other compared methods. This makes DiffSLC a stronger alternative to other centrality methods for detecting essential genes using a protein-protein interaction network that obeys centrality-lethality principle. DiffSLC is implemented using the igraph package in R, and networkx package in Python. The python package can

  15. Developing an intelligence analysis process through social network analysis

    Science.gov (United States)

    Waskiewicz, Todd; LaMonica, Peter

    2008-04-01

    Intelligence analysts are tasked with making sense of enormous amounts of data and gaining an awareness of a situation that can be acted upon. This process can be extremely difficult and time consuming. Trying to differentiate between important pieces of information and extraneous data only complicates the problem. When dealing with data containing entities and relationships, social network analysis (SNA) techniques can be employed to make this job easier. Applying network measures to social network graphs can identify the most significant nodes (entities) and edges (relationships) and help the analyst further focus on key areas of concern. Strange developed a model that identifies high value targets such as centers of gravity and critical vulnerabilities. SNA lends itself to the discovery of these high value targets and the Air Force Research Laboratory (AFRL) has investigated several network measures such as centrality, betweenness, and grouping to identify centers of gravity and critical vulnerabilities. Using these network measures, a process for the intelligence analyst has been developed to aid analysts in identifying points of tactical emphasis. Organizational Risk Analyzer (ORA) and Terrorist Modus Operandi Discovery System (TMODS) are the two applications used to compute the network measures and identify the points to be acted upon. Therefore, the result of leveraging social network analysis techniques and applications will provide the analyst and the intelligence community with more focused and concentrated analysis results allowing them to more easily exploit key attributes of a network, thus saving time, money, and manpower.

  16. Social network analysis of public health programs to measure partnership.

    Science.gov (United States)

    Schoen, Martin W; Moreland-Russell, Sarah; Prewitt, Kim; Carothers, Bobbi J

    2014-12-01

    In order to prevent chronic diseases, community-based programs are encouraged to take an ecological approach to public health promotion and involve many diverse partners. Little is known about measuring partnership in implementing public health strategies. We collected data from 23 Missouri communities in early 2012 that received funding from three separate programs to prevent obesity and/or reduce tobacco use. While all of these funding programs encourage partnership, only the Social Innovation for Missouri (SIM) program included a focus on building community capacity and enhancing collaboration. Social network analysis techniques were used to understand contact and collaboration networks in community organizations. Measurements of average degree, density, degree centralization, and betweenness centralization were calculated for each network. Because of the various sizes of the networks, we conducted comparative analyses with and without adjustment for network size. SIM programs had increased measurements of average degree for partner collaboration and larger networks. When controlling for network size, SIM groups had higher measures of network density and lower measures of degree centralization and betweenness centralization. SIM collaboration networks were more dense and less centralized, indicating increased partnership. The methods described in this paper can be used to compare partnership in community networks of various sizes. Further research is necessary to define causal mechanisms of partnership development and their relationship to public health outcomes. Copyright © 2014 Elsevier Ltd. All rights reserved.

  17. Social network analysis of study environment

    Directory of Open Access Journals (Sweden)

    Blaženka Divjak

    2010-06-01

    Full Text Available Student working environment influences student learning and achievement level. In this respect social aspects of students’ formal and non-formal learning play special role in learning environment. The main research problem of this paper is to find out if students' academic performance influences their position in different students' social networks. Further, there is a need to identify other predictors of this position. In the process of problem solving we use the Social Network Analysis (SNA that is based on the data we collected from the students at the Faculty of Organization and Informatics, University of Zagreb. There are two data samples: in the basic sample N=27 and in the extended sample N=52. We collected data on social-demographic position, academic performance, learning and motivation styles, student status (full-time/part-time, attitudes towards individual and teamwork as well as informal cooperation. Afterwards five different networks (exchange of learning materials, teamwork, informal communication, basic and aggregated social network were constructed. These networks were analyzed with different metrics and the most important were betweenness, closeness and degree centrality. The main result is, firstly, that the position in a social network cannot be forecast only by academic success and, secondly, that part-time students tend to form separate groups that are poorly connected with full-time students. In general, position of a student in social networks in study environment can influence student learning as well as her/his future employability and therefore it is worthwhile to be investigated.

  18. Social Network Analysis with sna

    Directory of Open Access Journals (Sweden)

    Carter T. Butts

    2007-12-01

    Full Text Available Modern social network analysis---the analysis of relational data arising from social systems---is a computationally intensive area of research. Here, we provide an overview of a software package which provides support for a range of network analytic functionality within the R statistical computing environment. General categories of currently supported functionality are described, and brief examples of package syntax and usage are shown.

  19. Network analysis of eight industrial symbiosis systems

    Science.gov (United States)

    Zhang, Yan; Zheng, Hongmei; Shi, Han; Yu, Xiangyi; Liu, Gengyuan; Su, Meirong; Li, Yating; Chai, Yingying

    2016-06-01

    Industrial symbiosis is the quintessential characteristic of an eco-industrial park. To divide parks into different types, previous studies mostly focused on qualitative judgments, and failed to use metrics to conduct quantitative research on the internal structural or functional characteristics of a park. To analyze a park's structural attributes, a range of metrics from network analysis have been applied, but few researchers have compared two or more symbioses using multiple metrics. In this study, we used two metrics (density and network degree centralization) to compare the degrees of completeness and dependence of eight diverse but representative industrial symbiosis networks. Through the combination of the two metrics, we divided the networks into three types: weak completeness, and two forms of strong completeness, namely "anchor tenant" mutualism and "equality-oriented" mutualism. The results showed that the networks with a weak degree of completeness were sparse and had few connections among nodes; for "anchor tenant" mutualism, the degree of completeness was relatively high, but the affiliated members were too dependent on core members; and the members in "equality-oriented" mutualism had equal roles, with diverse and flexible symbiotic paths. These results revealed some of the systems' internal structure and how different structures influenced the exchanges of materials, energy, and knowledge among members of a system, thereby providing insights into threats that may destabilize the network. Based on this analysis, we provide examples of the advantages and effectiveness of recent improvement projects in a typical Chinese eco-industrial park (Shandong Lubei).

  20. The Moderating Effect of Network Centrality on the Relationship Between Work Experience Variables and Organizational Commitment

    Science.gov (United States)

    2013-03-01

    network can be personal or social ( Ibarra & Andrews, 1993). While formal networks show the official rules and workings of an organization...THE MODERATING EFFECT OF NETWORK CENTRALITY ON THE RELATIONSHIP BETWEEN WORK EXPERIENCE VARIABLES...

  1. Computational Social Network Analysis

    CERN Document Server

    Hassanien, Aboul-Ella

    2010-01-01

    Presents insight into the social behaviour of animals (including the study of animal tracks and learning by members of the same species). This book provides web-based evidence of social interaction, perceptual learning, information granulation and the behaviour of humans and affinities between web-based social networks

  2. Network analysis applications in hydrology

    Science.gov (United States)

    Price, Katie

    2017-04-01

    Applied network theory has seen pronounced expansion in recent years, in fields such as epidemiology, computer science, and sociology. Concurrent development of analytical methods and frameworks has increased possibilities and tools available to researchers seeking to apply network theory to a variety of problems. While water and nutrient fluxes through stream systems clearly demonstrate a directional network structure, the hydrological applications of network theory remain under­explored. This presentation covers a review of network applications in hydrology, followed by an overview of promising network analytical tools that potentially offer new insights into conceptual modeling of hydrologic systems, identifying behavioral transition zones in stream networks and thresholds of dynamical system response. Network applications were tested along an urbanization gradient in Atlanta, Georgia, USA. Peachtree Creek and Proctor Creek. Peachtree Creek contains a nest of five long­term USGS streamflow and water quality gages, allowing network application of long­term flow statistics. The watershed spans a range of suburban and heavily urbanized conditions. Summary flow statistics and water quality metrics were analyzed using a suite of network analysis techniques, to test the conceptual modeling and predictive potential of the methodologies. Storm events and low flow dynamics during Summer 2016 were analyzed using multiple network approaches, with an emphasis on tomogravity methods. Results indicate that network theory approaches offer novel perspectives for understanding long­ term and event­based hydrological data. Key future directions for network applications include 1) optimizing data collection, 2) identifying "hotspots" of contaminant and overland flow influx to stream systems, 3) defining process domains, and 4) analyzing dynamic connectivity of various system components, including groundwater­surface water interactions.

  3. Sustained Effects of Acupuncture Stimulation Investigated with Centrality Mapping Analysis.

    Science.gov (United States)

    Long, Xiangyu; Huang, Wenjing; Napadow, Vitaly; Liang, Fanrong; Pleger, Burkhard; Villringer, Arno; Witt, Claudia M; Nierhaus, Till; Pach, Daniel

    2016-01-01

    Acupuncture can have instant and sustained effects, however, its mechanisms of action are still unclear. Here, we investigated the sustained effect of acupuncture by evaluating centrality changes in resting-state functional magnetic resonance imaging after manually stimulating the acupuncture point ST36 at the lower leg or two control point locations (CP1 same dermatome, CP2 different dermatome). Data from a previously published experiment evaluating instant BOLD effects and S2-seed-based resting state connectivity was re-analyzed using eigenvector centrality mapping and degree centrality mapping. These data-driven methods might add new insights into sustained acupuncture effects on both global and local inter-region connectivity (centrality) by evaluating the summary of connections of every voxel. We found higher centrality in parahippocampal gyrus and middle temporal gyrus after ST36 stimulation in comparison to the two control points. These regions are positively correlated to major hubs of the default mode network, which might be the primary network affected by chronic pain. The stronger integration of both regions within the whole-brain connectome after stimulation of ST36 might be a potential contributor to pain modulation by acupuncture. These findings highlight centrality mapping as a valuable analysis for future imaging studies investigating clinically relevant outcomes associated with physiological response to acupuncture stimulation. NCT01079689, ClinicalTrials.gov.

  4. Sustained effects of acupuncture stimulation investigated with centrality mapping analysis

    Directory of Open Access Journals (Sweden)

    Xiangyu Long

    2016-10-01

    Full Text Available Acupuncture can have instant and sustained effects, however its mechanisms of action are still unclear. Here we investigated the sustained effect of acupuncture by evaluating centrality changes in resting-state functional magnetic resonance imaging after manually stimulating the acupuncture point ST36 at the lower leg or two control point locations (CP1 same dermatome, CP2 different dermatome. Data from a previously published experiment evaluating instant BOLD effects and S2-seed-based resting state connectivity was re-analyzed using eigenvector centrality mapping (ECM and degree centrality mapping (DCM. These data-driven methods might add new insights into sustained acupuncture effects on both global and local inter-region connectivity (centrality by evaluating the summary of connections of every voxel. We found higher centrality in parahippocampal gyrus and middle temporal gyrus after ST36 stimulation in comparison to the two control points. These regions are positively correlated to major hubs of the default mode network, which might be the primary network affected by chronic pain. The stronger integration of both regions within the whole-brain connectome after stimulation of ST36 might be a potential contributor to pain modulation by acupuncture. These findings highlight centrality mapping as a valuable analysis for future imaging studies investigating clinically-relevant outcomes associated with physiological response to acupuncture stimulation.

  5. Topological analysis of telecommunications networks

    Directory of Open Access Journals (Sweden)

    Milojko V. Jevtović

    2011-01-01

    Full Text Available A topological analysis of the structure of telecommunications networks is a very interesting topic in the network research, but also a key issue in their design and planning. Satisfying multiple criteria in terms of locations of switching nodes as well as their connectivity with respect to the requests for capacity, transmission speed, reliability, availability and cost are the main research objectives. There are three ways of presenting the topology of telecommunications networks: table, matrix or graph method. The table method is suitable for a network of a relatively small number of nodes in relation to the number of links. The matrix method involves the formation of a connection matrix in which its columns present source traffic nodes and its rows are the switching systems that belong to the destination. The method of the topology graph means that the network nodes are connected via directional or unidirectional links. We can thus easily analyze the structural parameters of telecommunications networks. This paper presents the mathematical analysis of the star-, ring-, fully connected loop- and grid (matrix-shaped topology as well as the topology based on the shortest path tree. For each of these topologies, the expressions for determining the number of branches, the middle level of reliability, the medium length and the average length of the link are given in tables. For the fully connected loop network with five nodes the values of all topological parameters are calculated. Based on the topological parameters, the relationships that represent integral and distributed indicators of reliability are given in this work as well as the values of the particular network. The main objectives of the topology optimization of telecommunications networks are: achieving the minimum complexity, maximum capacity, the shortest path message transfer, the maximum speed of communication and maximum economy. The performance of telecommunications networks is

  6. Identification of milestone papers through time-balanced network centrality

    CERN Document Server

    Mariani, Manuel Sebastian; Zhang, Yi-Cheng

    2016-01-01

    Citations between scientific papers and related bibliometric indices, such as the $h$-index for authors and the impact factor for journals, are being increasingly used -- often in controversial ways -- as quantitative tools for research evaluation. Yet, a fundamental research question remains still open: to which extent do quantitative metrics capture the significance of scientific works? We analyze the network of citations among the $449,935$ papers published by the American Physical Society (APS) journals between $1893$ and $2009$, and focus on the comparison of metrics built on the citation count with network-based metrics. We contrast five article-level metrics with respect to the rankings that they assign to a set of fundamental papers, called Milestone Letters, carefully selected by the APS editors for "making long-lived contributions to physics, either by announcing significant discoveries, or by initiating new areas of research". A new metric, which combines PageRank centrality with the explicit requi...

  7. Vertex Reconstructing Neural Network at the ZEUS Central Tracking Detector

    CERN Document Server

    Dror, G; Dror, Gideon; Etzion, Erez

    2001-01-01

    An unconventional solution for finding the location of event creation is presented. It is based on two feed-forward neural networks with fixed architecture, whose parameters are chosen so as to reach a high accuracy. The interaction point location is a parameter that can be used to select events of interest from the very high rate of events created at the current experiments in High Energy Physics. The system suggested here is tested on simulated data sets of the ZEUS Central Tracking Detector, and is shown to perform better than conventional algorithms.

  8. Shifted intrinsic connectivity of central executive and salience network in borderline personality disorder

    Science.gov (United States)

    Doll, Anselm; Sorg, Christian; Manoliu, Andrei; Wöller, Andreas; Meng, Chun; Förstl, Hans; Zimmer, Claus; Wohlschläger, Afra M.; Riedl, Valentin

    2013-01-01

    Borderline personality disorder (BPD) is characterized by “stable instability” of emotions and behavior and their regulation. This emotional and behavioral instability corresponds with a neurocognitive triple network model of psychopathology, which suggests that aberrant emotional saliency and cognitive control is associated with aberrant interaction across three intrinsic connectivity networks [i.e., the salience network (SN), default mode network (DMN), and central executive network (CEN)]. The objective of the current study was to investigate whether and how such triple network intrinsic functional connectivity (iFC) is changed in patients with BPD. We acquired resting-state functional magnetic resonance imaging (rs-fMRI) data from 14 patients with BPD and 16 healthy controls. High-model order independent component analysis was used to extract spatiotemporal patterns of ongoing, coherent blood-oxygen-level-dependent signal fluctuations from rs-fMRI data. Main outcome measures were iFC within networks (intra-iFC) and between networks (i.e., network time course correlation inter-iFC). Aberrant intra-iFC was found in patients’ DMN, SN, and CEN, consistent with previous findings. While patients’ inter-iFC of the CEN was decreased, inter-iFC of the SN was increased. In particular, a balance index reflecting the relationship of CEN- and SN-inter-iFC across networks was strongly shifted from CEN to SN connectivity in patients. Results provide first preliminary evidence for aberrant triple network iFC in BPD. Our data suggest a shift of inter-network iFC from networks involved in cognitive control to those of emotion-related activity in BPD, potentially reflecting the persistent instability of emotion regulation in patients. PMID:24198777

  9. The Central American Network for Disaster and Health Information.

    Science.gov (United States)

    Arnesen, Stacey J; Cid, Victor H; Scott, John C; Perez, Ricardo; Zervaas, Dave

    2007-07-01

    This paper describes an international outreach program to support rebuilding Central America's health information infrastructure after several natural disasters in the region, including Hurricane Mitch in 1998 and two major earthquakes in 2001. The National Library of Medicine joined forces with the Pan American Health Organization/World Health Organization, the United Nations International Strategy for Disaster Reduction, and the Regional Center of Disaster Information for Latin America and the Caribbean (CRID) to strengthen libraries and information centers in Central America and improve the availability of and access to health and disaster information in the region by developing the Central American Network for Disaster and Health Information (CANDHI). Through CRID, the program created ten disaster health information centers in medical libraries and disaster-related organizations in six countries. This project served as a catalyst for the modernization of several medical libraries in Central America. The resulting CANDHI provides much needed electronic access to public health "gray literature" on disasters, as well as access to numerous health information resources. CANDHI members assist their institutions and countries in a variety of disaster preparedness activities through collecting and disseminating information.

  10. Social Network Analysis of a Supply Network Structural Investigation of the South Korean Automotive Industry

    OpenAIRE

    Kim, Jin-Baek

    2015-01-01

    Part 3: Knowledge Based Production Management; International audience; In this paper, we analyzed the structure of the South Korean automotive industry using social network analysis (SNA) metrics. Based on the data collected from 275 companies, a social network model of the supply network was constructed. Centrality measures in the SNA field were used to interpret the result and identify key companies. The results show that SNA metrics can be useful to understand the structure of a supply net...

  11. A network centrality measure framework for analyzing urban traffic flow: A case study of Wuhan, China

    Science.gov (United States)

    Zhao, Shuangming; Zhao, Pengxiang; Cui, Yunfan

    2017-07-01

    In this paper, we propose an improved network centrality measure framework that takes into account both the topological characteristics and the geometric properties of a road network in order to analyze urban traffic flow in relation to different modes: intersection, road, and community, which correspond to point mode, line mode, and area mode respectively. Degree, betweenness, and PageRank centralities are selected as the analysis measures, and GPS-enabled taxi trajectory data is used to evaluate urban traffic flow. The results show that the mean value of the correlation coefficients between the modified degree, the betweenness, and the PageRank centralities and the traffic flow in all periods are higher than the mean value of the correlation coefficients between the conventional degree, the betweenness, the PageRank centralities and the traffic flow at different modes; this indicates that the modified measurements, for analyzing traffic flow, are superior to conventional centrality measurements. This study helps to shed light into the understanding of urban traffic flow in relation to different modes from the perspective of complex networks.

  12. Analysis of neural networks

    CERN Document Server

    Heiden, Uwe

    1980-01-01

    The purpose of this work is a unified and general treatment of activity in neural networks from a mathematical pOint of view. Possible applications of the theory presented are indica­ ted throughout the text. However, they are not explored in de­ tail for two reasons : first, the universal character of n- ral activity in nearly all animals requires some type of a general approach~ secondly, the mathematical perspicuity would suffer if too many experimental details and empirical peculiarities were interspersed among the mathematical investigation. A guide to many applications is supplied by the references concerning a variety of specific issues. Of course the theory does not aim at covering all individual problems. Moreover there are other approaches to neural network theory (see e.g. Poggio-Torre, 1978) based on the different lev­ els at which the nervous system may be viewed. The theory is a deterministic one reflecting the average be­ havior of neurons or neuron pools. In this respect the essay is writt...

  13. Statistical network analysis for analyzing policy networks

    DEFF Research Database (Denmark)

    Robins, Garry; Lewis, Jenny; Wang, Peng

    2012-01-01

    To analyze social network data using standard statistical approaches is to risk incorrect inference. The dependencies among observations implied in a network conceptualization undermine standard assumptions of the usual general linear models. One of the most quickly expanding areas of social...... and policy network methodology is the development of statistical modeling approaches that can accommodate such dependent data. In this article, we review three network statistical methods commonly used in the current literature: quadratic assignment procedures, exponential random graph models (ERGMs...

  14. Applying temporal network analysis to the venture capital market

    Science.gov (United States)

    Zhang, Xin; Feng, Ling; Zhu, Rongqian; Stanley, H. Eugene

    2015-10-01

    Using complex network theory to study the investment relationships of venture capital firms has produced a number of significant results. However, previous studies have often neglected the temporal properties of those relationships, which in real-world scenarios play a pivotal role. Here we examine the time-evolving dynamics of venture capital investment in China by constructing temporal networks to represent (i) investment relationships between venture capital firms and portfolio companies and (ii) the syndication ties between venture capital investors. The evolution of the networks exhibits rich variations in centrality, connectivity and local topology. We demonstrate that a temporal network approach provides a dynamic and comprehensive analysis of real-world networks.

  15. Shared and distinct intrinsic functional network centrality in autism and attention-deficit/hyperactivity disorder.

    Science.gov (United States)

    Di Martino, Adriana; Zuo, Xi-Nian; Kelly, Clare; Grzadzinski, Rebecca; Mennes, Maarten; Schvarcz, Ariel; Rodman, Jennifer; Lord, Catherine; Castellanos, F Xavier; Milham, Michael P

    2013-10-15

    Individuals with autism spectrum disorders (ASD) often exhibit symptoms of attention-deficit/hyperactivity disorder (ADHD). Across both disorders, observations of distributed functional abnormalities suggest aberrant large-scale brain network connectivity. Yet, common and distinct network correlates of ASD and ADHD remain unidentified. Here, we aimed to examine patterns of dysconnection in school-age children with ASD and ADHD and typically developing children who completed a resting state functional magnetic resonance imaging scan. We measured voxelwise network centrality, functional connectivity metrics indexing local (degree centrality [DC]) and global (eigenvector centrality) functional relationships across the entire brain connectome, in resting state functional magnetic resonance imaging data from 56 children with ASD, 45 children with ADHD, and 50 typically developing children. A one-way analysis of covariance, with group as fixed factor (whole-brain corrected), was followed by post hoc pairwise comparisons. Cortical and subcortical areas exhibited centrality abnormalities, some common to both ADHD and ASD, such as in precuneus. Others were disorder-specific and included ADHD-related increases in DC in right striatum/pallidum, in contrast with ASD-related increases in bilateral temporolimbic areas. Secondary analyses differentiating children with ASD into those with or without ADHD-like comorbidity (ASD(+) and ASD(-), respectively) revealed that the ASD(+) group shared ADHD-specific abnormalities in basal ganglia. By contrast, centrality increases in temporolimbic areas characterized children with ASD regardless of ADHD-like comorbidity. At the cluster level, eigenvector centrality group patterns were similar to DC. ADHD and ASD are neurodevelopmental disorders with distinct and overlapping clinical presentations. This work provides evidence for both shared and distinct underlying mechanisms at the large-scale network level. Copyright © 2013 Society of

  16. Computing network centrality measures on fMRI data using fully weighted adjacency matrices

    OpenAIRE

    Bränberg, Stefan

    2016-01-01

    A lot of interesting research is currently being done in the field of neuroscience, a recent subject being the effort to analyse the the human brain connectome and its functional connectivity. One way this is done is by applying graph-theory based network analysis, such as centrality, on data from fMRI measurements. This involves creating a graph representation from a correlation matrix containing the correlations over time between all measured voxels. Since the input data can be very big, th...

  17. Cumulative Evidence of Randomized Controlled and Observational Studies on Catheter-Related Infection Risk of Central Venous Catheter Insertion Site in ICU Patients: A Pairwise and Network Meta-Analysis.

    Science.gov (United States)

    Arvaniti, Kostoula; Lathyris, Dimitrios; Blot, Stijn; Apostolidou-Kiouti, Fani; Koulenti, Despoina; Haidich, Anna-Bettina

    2017-04-01

    Selection of central venous catheter insertion site in ICU patients could help reduce catheter-related infections. Although subclavian was considered the most appropriate site, its preferential use in ICU patients is not generalized and questioned by contradicted meta-analysis results. In addition, conflicting data exist on alternative site selection whenever subclavian is contraindicated. To compare catheter-related bloodstream infection and colonization risk between the three sites (subclavian, internal jugular, and femoral) in adult ICU patients. We searched MEDLINE, EMBASE, Cochrane Central Register of Controlled trials, CINAHL, and ClinicalTrials.gov. Eligible studies were randomized controlled trials and observational ones. Extracted data were analyzed by pairwise and network meta-analysis. Twenty studies were included; 11 were observational, seven were randomized controlled trials for other outcomes, and two were randomized controlled trials for sites. We evaluated 18,554 central venous catheters: 9,331 from observational studies, 5,482 from randomized controlled trials for other outcomes, and 3,741 from randomized controlled trials for sites. Colonization risk was higher for internal jugular (relative risk, 2.25 [95% CI, 1.84-2.75]; I = 0%) and femoral (relative risk, 2.92 [95% CI, 2.11-4.04]; I = 24%), compared with subclavian. Catheter-related bloodstream infection risk was comparable for internal jugular and subclavian, higher for femoral than subclavian (relative risk, 2.44 [95% CI, 1.25-4.75]; I = 61%), and lower for internal jugular than femoral (relative risk, 0.55 [95% CI, 0.34-0.89]; I = 61%). When observational studies that did not control for baseline characteristics were excluded, catheter-related bloodstream infection risk was comparable between the sites. In ICU patients, internal jugular and subclavian may, similarly, decrease catheter-related bloodstream infection risk, when compared with femoral. Subclavian could be suggested as the most

  18. Identifying gene-disease associations using centrality on a literature mined gene-interaction network.

    Science.gov (United States)

    Ozgür, Arzucan; Vu, Thuy; Erkan, Günes; Radev, Dragomir R

    2008-07-01

    Understanding the role of genetics in diseases is one of the most important aims of the biological sciences. The completion of the Human Genome Project has led to a rapid increase in the number of publications in this area. However, the coverage of curated databases that provide information manually extracted from the literature is limited. Another challenge is that determining disease-related genes requires laborious experiments. Therefore, predicting good candidate genes before experimental analysis will save time and effort. We introduce an automatic approach based on text mining and network analysis to predict gene-disease associations. We collected an initial set of known disease-related genes and built an interaction network by automatic literature mining based on dependency parsing and support vector machines. Our hypothesis is that the central genes in this disease-specific network are likely to be related to the disease. We used the degree, eigenvector, betweenness and closeness centrality metrics to rank the genes in the network. The proposed approach can be used to extract known and to infer unknown gene-disease associations. We evaluated the approach for prostate cancer. Eigenvector and degree centrality achieved high accuracy. A total of 95% of the top 20 genes ranked by these methods are confirmed to be related to prostate cancer. On the other hand, betweenness and closeness centrality predicted more genes whose relation to the disease is currently unknown and are candidates for experimental study. A web-based system for browsing the disease-specific gene-interaction networks is available at: http://gin.ncibi.org.

  19. Dim Networks: The Utility of Social Network Analysis for Illuminating Partner Security Force Networks

    Science.gov (United States)

    2015-12-01

    Eigenvector centrality ......................................................88 xii THIS PAGE INTENTIONALLY LEFT BLANK xiii LIST OF ACRONYMS AND...should be engaged. This determination will be based on simple SNA centrality measures, total degree,9 betweenness,10 closeness,11 and Eigenvector ...11 Closeness centrality measures how close each node is to all the other nodes in a network by their path distance. 12 Eigenvector centrality

  20. Spatial Distribution Characteristics of Healthcare Facilities in Nanjing: Network Point Pattern Analysis and Correlation Analysis.

    Science.gov (United States)

    Ni, Jianhua; Qian, Tianlu; Xi, Changbai; Rui, Yikang; Wang, Jiechen

    2016-08-18

    The spatial distribution of urban service facilities is largely constrained by the road network. In this study, network point pattern analysis and correlation analysis were used to analyze the relationship between road network and healthcare facility distribution. The weighted network kernel density estimation method proposed in this study identifies significant differences between the outside and inside areas of the Ming city wall. The results of network K-function analysis show that private hospitals are more evenly distributed than public hospitals, and pharmacy stores tend to cluster around hospitals along the road network. After computing the correlation analysis between different categorized hospitals and street centrality, we find that the distribution of these hospitals correlates highly with the street centralities, and that the correlations are higher with private and small hospitals than with public and large hospitals. The comprehensive analysis results could help examine the reasonability of existing urban healthcare facility distribution and optimize the location of new healthcare facilities.

  1. NEAT : an efficient network enrichment analysis test

    NARCIS (Netherlands)

    Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

    2016-01-01

    BACKGROUND: Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be

  2. A Study on International Multimodal Transport Networks from Korea to Central Asia: Focus on Secondhand Vehicles

    Directory of Open Access Journals (Sweden)

    Ying Wang

    2016-03-01

    Full Text Available Currently, high-income countries use multimodal transportation to export large quantities of secondhand vehicles to low-income countries. Secondhand vehicle export has shown its highest growth in recent years, especially in Korea. The problem of transporting secondhand vehicles from Korea to Central Asia is becoming an important issue, but few researchers are interested in it. The objective of this research is to determine the optimal transport network for exporting secondhand vehicles from Korea to Central Asian countries by combining experts’ opinions and real data from existing transport networks. The fuzzy Delphi method was applied to obtain factors to evaluate alternative multimodal transport networks for moving secondhand vehicles from Korea to Central Asian countries by judgment from experts. The analysis shows that among the five factors (total cost, total time, reliability, security, and transportation capability, total cost is the most considerable factor, followed by reliability, transportation capability, total time, and security. Additionally, in the mainly three multimodal transport networks, the sea+Trans-China Railway route is ranked first, followed by the sea+Trans-Siberian Railway and sea+truck routes.

  3. Shifted intrinsic connectivity of central executive and salience network in borderline personality disorder

    Directory of Open Access Journals (Sweden)

    Anselm eDoll

    2013-10-01

    Full Text Available Borderline personality disorder (BPD is characterized by stable instability of emotions and behavior and their regulation. This emotional and behavioral instability corresponds with a neurocognitive triple network model of psychopathology, which suggests that aberrant emotional saliency and cognitive control is associated with aberrant interaction across three intrinsic connectivity networks (ICN (i.e. the salience, default mode, and central executive network, SN, DMN, CEN. The objective of the current study was to investigate whether and how such triple network intrinsic functional connectivity (iFC is changed in patients with BPD. We acquired resting-state functional magnetic resonance imaging (rs-fMRI data from fourteen patients with BPD and sixteen healthy controls (HC. High-model order independent component analysis (ICA was used to extract spatiotemporal patterns of ongoing, coherent blood-oxygen-level-dependent (BOLD signal fluctuations from rs-fMRI data. Main outcome measures were iFC within networks (intra-iFC and between networks (i.e. network time course correlation inter-iFC.Aberrant intra-iFC was found in patients’ DMN, SN, and CEN, consistent with previous findings. While patients’ inter-iFC of the CEN was decreased, inter-iFC of the SN was increased. In particular, a balance index reflecting the relationship of CEN-and SN-inter-iFC across networks was strongly shifted from CEN to SN connectivity in patients. Results provide first preliminary evidence for aberrant triple network intrinsic functional connectivity in BPD. Our data suggest a shift of inter-network iFC from networks involved in cognitive control to those of emotion-related activity in BPD, potentially reflecting the persistent instability of emotion regulation in patients.

  4. Analysis of cascading failure in gene networks

    Directory of Open Access Journals (Sweden)

    Shudong eWang

    2012-12-01

    Full Text Available It is an important subject to research the functional mechanism of cancer-related genes make in formation and development of cancers. The modern methodology of data analysis plays a very important role for deducing the relationship between cancers and cancer-related genes and analyzing functional mechanism of genome. In this research, we construct mutual information networks using gene expression profiles of glioblast and renal in normal condition and cancer conditions. We investigate the relationship between structure and robustness in gene networks of the two tissues using a cascading failure model based on betweenness centrality. Define some important parameters such as the percentage of failure nodes of the network, the average size-ratio of cascading failure and the cumulative probability of size-ratio of cascading failure to measure the robustness of the networks. By comparing control group and experiment groups, we find that the networks of experiment groups are more robust than that of control group. The gene that can cause large scale failure is called structural key gene (SKG. Some of them have been confirmed to be closely related to the formation and development of glioma and renal cancer respectively. Most of them are predicted to play important roles during the formation of glioma and renal cancer, maybe the oncogenes, suppressor genes, and other cancer candidate genes in the glioma and renal cancer cells. However, these studies provide little information about the detailed roles of identified cancer genes.

  5. Study of co-authorship network of papers in the Journal of Research in Medical Sciences using social network analysis

    Directory of Open Access Journals (Sweden)

    Firoozeh Zare-Farashbandi

    2014-01-01

    Full Text Available Background: Co-authorship is one of the most tangible forms of research collaboration. A co-authorship network is a social network in which the authors through participation in one or more publication through an indirect path have linked to each other. The present research using the social network analysis studied co-authorship network of 681 articles published in Journal of Research in Medical Sciences (JRMS during 2008-2012. Materials and Methods: The study was carried out with the scientometrics approach and using co-authorship network analysis of authors. The topology of the co-authorship network of 681 published articles in JRMS between 2008 and 2012 was analyzed using macro-level metrics indicators of network analysis such as density, clustering coefficient, components and mean distance. In addition, in order to evaluate the performance of each authors and countries in the network, the micro-level indicators such as degree centrality, closeness centrality and betweenness centrality as well as productivity index were used. The UCINET and NetDraw softwares were used to draw and analyze the co-authorship network of the papers. Results: The assessment of the authors productivity in this journal showed that the first ranks were belonged to only five authors, respectively. Furthermore, analysis of the co-authorship of the authors in the network demonstrated that in the betweenness centrality index, three authors of them had the good position in the network. They can be considered as the network leaders able to control the flow of information in the network compared with the other members based on the shortest paths. On the other hand, the key role of the network according to the productivity and centrality indexes was belonged to Iran, Malaysia and United States of America. Conclusion: Co-authorship network of JRMS has the characteristics of a small world network. In addition, the theory of 6° separation is valid in this network was also true.

  6. Analysis of Layered Social Networks

    Science.gov (United States)

    2006-09-01

    xiii List of Abbreviations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xv I. Introduction ...Islamiya JP Joint Publication JTC Joint Targeting Cycle KPP Key Player Problem MCDM Multi-Criteria Decision Making MP Mathematical Programming MST...ANALYSIS OF LAYERED SOCIAL NETWORKS I. Introduction “To know them means to eliminate them” - Colonel Mathieu in the movie, Battle of Algiers

  7. Percolation centrality: quantifying graph-theoretic impact of nodes during percolation in networks.

    Directory of Open Access Journals (Sweden)

    Mahendra Piraveenan

    Full Text Available A number of centrality measures are available to determine the relative importance of a node in a complex network, and betweenness is prominent among them. However, the existing centrality measures are not adequate in network percolation scenarios (such as during infection transmission in a social network of individuals, spreading of computer viruses on computer networks, or transmission of disease over a network of towns because they do not account for the changing percolation states of individual nodes. We propose a new measure, percolation centrality, that quantifies relative impact of nodes based on their topological connectivity, as well as their percolation states. The measure can be extended to include random walk based definitions, and its computational complexity is shown to be of the same order as that of betweenness centrality. We demonstrate the usage of percolation centrality by applying it to a canonical network as well as simulated and real world scale-free and random networks.

  8. Incremental Centrality Algorithms for Dynamic Network Analysis

    Science.gov (United States)

    2013-08-01

    Directorate for Information Operations and Reports, 1215 Jefferson Davis Highway, Suite 1204, Arlington VA 22202-4302. Respondents should be aware...run-time of O(m + nlogn) can be achieved by implementing the priority queue using a Fibonacci heap [127]. When Dijsktra’s algorithm is invoked

  9. Host centrality in food web networks determines parasite diversity.

    Science.gov (United States)

    Anderson, Tavis K; Sukhdeo, Michael V K

    2011-01-01

    Parasites significantly alter topological metrics describing food web structure, yet few studies have explored the relationship between food web topology and parasite diversity. This study uses quantitative metrics describing network structure to investigate the relationship between the topology of the host food web and parasite diversity. Food webs were constructed for four restored brackish marshes that vary in species diversity, time post restoration and levels of parasitism. Our results show that the topology of the food web in each brackish marsh is highly nested, with clusters of generalists forming a distinct modular structure. The most consistent predictors of parasite diversity within a host were: trophic generality, and eigenvector centrality. These metrics indicate that parasites preferentially colonise host species that are highly connected, and within modules of tightly interacting species in the food web network. These results suggest that highly connected free-living species within the food web may represent stable trophic relationships that allow for the persistence of complex parasite life cycles. Our data demonstrate that the structure of host food webs can have a significant effect on the establishment of parasites, and on the potential for evolution of complex parasite life cycles.

  10. Host centrality in food web networks determines parasite diversity.

    Directory of Open Access Journals (Sweden)

    Tavis K Anderson

    Full Text Available BACKGROUND: Parasites significantly alter topological metrics describing food web structure, yet few studies have explored the relationship between food web topology and parasite diversity. METHODS/PRINCIPAL FINDINGS: This study uses quantitative metrics describing network structure to investigate the relationship between the topology of the host food web and parasite diversity. Food webs were constructed for four restored brackish marshes that vary in species diversity, time post restoration and levels of parasitism. Our results show that the topology of the food web in each brackish marsh is highly nested, with clusters of generalists forming a distinct modular structure. The most consistent predictors of parasite diversity within a host were: trophic generality, and eigenvector centrality. These metrics indicate that parasites preferentially colonise host species that are highly connected, and within modules of tightly interacting species in the food web network. CONCLUSIONS/SIGNIFICANCE: These results suggest that highly connected free-living species within the food web may represent stable trophic relationships that allow for the persistence of complex parasite life cycles. Our data demonstrate that the structure of host food webs can have a significant effect on the establishment of parasites, and on the potential for evolution of complex parasite life cycles.

  11. Altered brain network centrality in depressed Parkinson's disease patients.

    Science.gov (United States)

    Lou, Yuting; Huang, Peiyu; Li, Dan; Cen, Zhidong; Wang, Bo; Gao, Jixiang; Xuan, Min; Yu, Hualiang; Zhang, Minming; Luo, Wei

    2015-11-01

    Depression is a relatively common and serious nonmotor symptom of Parkinson's disease (PD), which reduces the quality of patients' life. Although disturbances in some related brain networks have been reported, the pathophysiology of depression in PD is still unclear. Here, we aim to investigate whole-brain functional connectivity patterns in depressed PD patients. We recruited 17 PD patients diagnosed with major depressive disorder, 17 PD patients without depression, and 17 healthy control subjects. Resting-state functional MRI and eigenvector centrality mapping were used to identify functional connectivity alterations among these groups. Results showed that depressed PD patients had decreased functional connectivity in the left dorsolateral prefrontal cortex and right superior temporal gyrus and increased functional connectivity in the right posterior cingulate cortex, compared to nondepressed patients. In addition, there was a significant negative correlation between functional connectivity and depression scores in the posterior cingulate cortex. This study suggests that functional connectivity changes in certain nodes of brain networks might contribute to depression in patients with PD. © 2015 International Parkinson and Movement Disorder Society.

  12. Location of diversions from the surface-water network of the Central Valley Hydrologic Model (CVHM)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This digital dataset contains the name and location for the diversions from the surface-water network for the Central Valley Hydrologic Model (CVHM). The Central...

  13. Surface-Water Network for the Central Valley Hydrologic Model (CVHM)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This digital dataset contains the surface-water network for the Central Valley Hydrologic Model (CVHM). The Central Valley encompasses an approximate...

  14. Monthly Diversions from the Surface-Water Network of the Central Valley Hydrologic Model (CVHM)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This digital dataset contains the monthly diversions from the surface-water network for the Central Valley Hydrologic Model (CVHM). The Central Valley encompasses an...

  15. Grid cells used for Surface-Water Network for the Central Valley Hydrologic Model

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This digital dataset contains the segment and reaches for the surface-water network by model cell for the Central Valley Hydrologic Model (CVHM). The Central Valley...

  16. Monthly inflows to the surface-water network for the Central Valley Hydrologic Model (CVHM)

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This digital dataset contains the monthly inflows to the surface-water network for the Central Valley Hydrologic Model (CVHM). The Central Valley encompasses an...

  17. Structure of Stockmen Collaboration Networks Under Two Contrasting Touristic Regimes in the Spanish Central Pyrenees

    National Research Council Canada - National Science Library

    Saiz, Hugo; Gartzia, Maite; Errea, Paz; Fillat, Federico; Alados, Concepción L

    .... This study examined the collaboration networks among stockmen within two traditionally agropastoral regions in the Spanish Central Pyrenees, which in the past 30 yr included touristic activities...

  18. Transmission analysis in WDM networks

    DEFF Research Database (Denmark)

    Rasmussen, Christian Jørgen

    1999-01-01

    This thesis describes the development of a computer-based simulator for transmission analysis in optical wavelength division multiplexing networks. A great part of the work concerns fundamental optical network simulator issues. Among these issues are identification of the versatility and user......-friendliness demands which such a simulator must meet, development of the "spectral window representation" for representation of the optical signals and finding an effective way of handling the optical signals in the computer memory. One important issue more is the rules for the determination of the order in which...... the different component models are invoked during the simulation of a system. A simple set of rules which makes it possible to simulate any network architectures is laid down. The modelling of the nonlinear fibre and the optical receiver is also treated. The work on the fibre concerns the numerical solution...

  19. A biplex approach to PageRank centrality: From classic to multiplex networks

    Science.gov (United States)

    Pedroche, Francisco; Romance, Miguel; Criado, Regino

    2016-06-01

    In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks.

  20. A biplex approach to PageRank centrality: From classic to multiplex networks.

    Science.gov (United States)

    Pedroche, Francisco; Romance, Miguel; Criado, Regino

    2016-06-01

    In this paper, we present a new view of the PageRank algorithm inspired by multiplex networks. This new approach allows to introduce a new centrality measure for classic complex networks and a new proposal to extend the usual PageRank algorithm to multiplex networks. We give some analytical relations between these new approaches and the classic PageRank centrality measure, and we illustrate the new parameters presented by computing them on real underground networks.

  1. SBEToolbox: A Matlab Toolbox for Biological Network Analysis.

    Science.gov (United States)

    Konganti, Kranti; Wang, Gang; Yang, Ence; Cai, James J

    2013-01-01

    We present SBEToolbox (Systems Biology and Evolution Toolbox), an open-source Matlab toolbox for biological network analysis. It takes a network file as input, calculates a variety of centralities and topological metrics, clusters nodes into modules, and displays the network using different graph layout algorithms. Straightforward implementation and the inclusion of high-level functions allow the functionality to be easily extended or tailored through developing custom plugins. SBEGUI, a menu-driven graphical user interface (GUI) of SBEToolbox, enables easy access to various network and graph algorithms for programmers and non-programmers alike. All source code and sample data are freely available at https://github.com/biocoder/SBEToolbox/releases.

  2. "Us and them": a social network analysis of physicians' professional networks and their attitudes towards EBM.

    Science.gov (United States)

    Mascia, Daniele; Cicchetti, Americo; Damiani, Gianfranco

    2013-10-22

    Extant research suggests that there is a strong social component to Evidence-Based Medicine (EBM) adoption since professional networks amongst physicians are strongly associated with their attitudes towards EBM. Despite this evidence, it is still unknown whether individual attitudes to use scientific evidence in clinical decision-making influence the position that physicians hold in their professional network. This paper explores how physicians' attitudes towards EBM is related to the network position they occupy within healthcare organizations. Data pertain to a sample of Italian physicians, whose professional network relationships, demographics and work-profile characteristics were collected. A social network analysis was performed to capture the structural importance of physicians in the collaboration network by the means of a core-periphery analysis and the computation of network centrality indicators. Then, regression analysis was used to test the association between the network position of individual clinicians and their attitudes towards EBM. Findings documented that the overall network structure is made up of a dense cohesive core of physicians and of less connected clinicians who occupy the periphery. A negative association between the physicians' attitudes towards EBM and the coreness they exhibited in the professional network was also found. Network centrality indicators confirmed these results documenting a negative association between physicians' propensity to use EBM and their structural importance in the professional network. Attitudes that physicians show towards EBM are related to the part (core or periphery) of the professional networks to which they belong as well as to their structural importance. By identifying virtuous attitudes and behaviors of professionals within their organizations, policymakers and executives may avoid marginalization and stimulate integration and continuity of care, both within and across the boundaries of healthcare

  3. Spectral Analysis of Rich Network Topology in Social Networks

    Science.gov (United States)

    Wu, Leting

    2013-01-01

    Social networks have received much attention these days. Researchers have developed different methods to study the structure and characteristics of the network topology. Our focus is on spectral analysis of the adjacency matrix of the underlying network. Recent work showed good properties in the adjacency spectral space but there are few…

  4. Biological oscillations for learning walking coordination: dynamic recurrent neural network functionally models physiological central pattern generator.

    Science.gov (United States)

    Hoellinger, Thomas; Petieau, Mathieu; Duvinage, Matthieu; Castermans, Thierry; Seetharaman, Karthik; Cebolla, Ana-Maria; Bengoetxea, Ana; Ivanenko, Yuri; Dan, Bernard; Cheron, Guy

    2013-01-01

    The existence of dedicated neuronal modules such as those organized in the cerebral cortex, thalamus, basal ganglia, cerebellum, or spinal cord raises the question of how these functional modules are coordinated for appropriate motor behavior. Study of human locomotion offers an interesting field for addressing this central question. The coordination of the elevation of the 3 leg segments under a planar covariation rule (Borghese et al., 1996) was recently modeled (Barliya et al., 2009) by phase-adjusted simple oscillators shedding new light on the understanding of the central pattern generator (CPG) processing relevant oscillation signals. We describe the use of a dynamic recurrent neural network (DRNN) mimicking the natural oscillatory behavior of human locomotion for reproducing the planar covariation rule in both legs at different walking speeds. Neural network learning was based on sinusoid signals integrating frequency and amplitude features of the first three harmonics of the sagittal elevation angles of the thigh, shank, and foot of each lower limb. We verified the biological plausibility of the neural networks. Best results were obtained with oscillations extracted from the first three harmonics in comparison to oscillations outside the harmonic frequency peaks. Physiological replication steadily increased with the number of neuronal units from 1 to 80, where similarity index reached 0.99. Analysis of synaptic weighting showed that the proportion of inhibitory connections consistently increased with the number of neuronal units in the DRNN. This emerging property in the artificial neural networks resonates with recent advances in neurophysiology of inhibitory neurons that are involved in central nervous system oscillatory activities. The main message of this study is that this type of DRNN may offer a useful model of physiological central pattern generator for gaining insights in basic research and developing clinical applications.

  5. Why a Central Network Position Isn't Enough

    DEFF Research Database (Denmark)

    Reinholt, Mia; Pedersen, Torben; Foss, Nicolai Juul

    2011-01-01

    Contrasting views exist on how network characteristics predict knowledge sharing. While large, open egocentric networks foster network positions that provide access to non-redundant knowledge, critics highlight that they impair knowledge sharing, because trust and reciprocity do not thrive...

  6. Analysis of Semantic Networks using Complex Networks Concepts

    DEFF Research Database (Denmark)

    Ortiz-Arroyo, Daniel

    2013-01-01

    In this paper we perform a preliminary analysis of semantic networks to determine the most important terms that could be used to optimize a summarization task. In our experiments, we measure how the properties of a semantic network change, when the terms in the network are removed. Our preliminar...... results indicate that this approach provides good results on the semantic network analyzed in this paper....

  7. Connectomic Insights into Topologically Centralized Network Edges and Relevant Motifs in the Human Brain

    Directory of Open Access Journals (Sweden)

    Mingrui eXia

    2016-04-01

    Full Text Available White matter (WM tracts serve as important material substrates for information transfer across brain regions. However, the topological roles of WM tracts in global brain communications and their underlying microstructural basis remain poorly understood. Here, we employed diffusion magnetic resonance imaging and graph-theoretical approaches to identify the pivotal WM connections in human whole-brain networks and further investigated their wiring substrates (including WM microstructural organization and physical consumption and topological contributions to the brain’s network backbone. We found that the pivotal WM connections with highly topological-edge centrality were primarily distributed in several long-range cortico-cortical connections (including the corpus callosum, cingulum and inferior fronto-occipital fasciculus and some projection tracts linking subcortical regions. These pivotal WM connections exhibited high levels of microstructural organization indicated by diffusion measures (the fractional anisotropy, the mean diffusivity and the axial diffusivity and greater physical consumption indicated by streamline lengths, and contributed significantly to the brain’s hubs and the rich-club structure. Network motif analysis further revealed their heavy participations in the organization of communication blocks, especially in routes involving inter-hemispheric heterotopic and extremely remote intra-hemispheric systems. Computational simulation models indicated the sharp decrease of global network integrity when attacking these highly centralized edges. Together, our results demonstrated high building-cost consumption and substantial communication capacity contributions for pivotal WM connections, which deepens our understanding of the topological mechanisms that govern the organization of human connectomes.

  8. COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks

    NARCIS (Netherlands)

    Sie, Rory

    2012-01-01

    Sie, R. L. L. (2012). COalitions in COOperation Networks (COCOON): Social Network Analysis and Game Theory to Enhance Cooperation Networks (Unpublished doctoral dissertation). September, 28, 2012, Open Universiteit in the Netherlands (CELSTEC), Heerlen, The Netherlands.

  9. Salience, central executive, and sensorimotor network functional connectivity alterations in failed back surgery syndrome.

    Science.gov (United States)

    Kolesar, Tiffany A; Bilevicius, Elena; Kornelsen, Jennifer

    2017-07-01

    This study examined the altered patterns of functional connectivity in task-positive resting state networks in failed back surgery syndrome (FBSS) patients compared to healthy controls using functional magnetic resonance imaging (fMRI). This work stems from a previous study in which alterations in the task-negative default mode network were investigated. Participants underwent a 7-minute resting state fMRI scan in which they lay still, with eyes closed, in the absence of a task. Scanning took place at the National Research Council's 3Tesla MRI magnet in Winnipeg, Canada. Fourteen patients with FBSS and age- and gender-matched controls participated in this study. Three patients were removed from the analyses due to image artefact (n=1) and effective pain treatment (n=2). Eleven patients (5 female, mean age 52.7 years) and their matched controls were included in the final analyses. Resting state fMRI data were analyzed using an independent component analysis, yielding three resting state networks of interest: the salience network (SN), involved in detection of external stimuli, central executive network (CEN), involved in cognitions, and sensorimotor network (SeN), involved in sensory and motor integration. Analysis of Variance contrasts were performed for each network, comparing functional connectivity differences between FBSS patients and healthy controls. Alterations were observed in all three resting state networks, primarily relating to pain and its processing in the FBSS group. Specifically, compared to healthy controls, FBSS patients demonstrated increased functional connectivity in the anterior cingulate cortex within the SN, medial frontal gyrus in the CEN, and precentral gyrus within the SeN. FBSS patients also demonstrated decreased functional connectivity in the medial frontal gyrus in the SeN compared to healthy controls. Interestingly, we also observed internetwork functional connectivity in the SN and SeN. FBSS is associated with altered patterns of

  10. Spectral Analysis Methods of Social Networks

    Directory of Open Access Journals (Sweden)

    P. G. Klyucharev

    2017-01-01

    Full Text Available Online social networks (such as Facebook, Twitter, VKontakte, etc. being an important channel for disseminating information are often used to arrange an impact on the social consciousness for various purposes - from advertising products or services to the full-scale information war thereby making them to be a very relevant object of research. The paper reviewed the analysis methods of social networks (primarily, online, based on the spectral theory of graphs. Such methods use the spectrum of the social graph, i.e. a set of eigenvalues of its adjacency matrix, and also the eigenvectors of the adjacency matrix.Described measures of centrality (in particular, centrality based on the eigenvector and PageRank, which reflect a degree of impact one or another user of the social network has. A very popular PageRank measure uses, as a measure of centrality, the graph vertices, the final probabilities of the Markov chain, whose matrix of transition probabilities is calculated on the basis of the adjacency matrix of the social graph. The vector of final probabilities is an eigenvector of the matrix of transition probabilities.Presented a method of dividing the graph vertices into two groups. It is based on maximizing the network modularity by computing the eigenvector of the modularity matrix.Considered a method for detecting bots based on the non-randomness measure of a graph to be computed using the spectral coordinates of vertices - sets of eigenvector components of the adjacency matrix of a social graph.In general, there are a number of algorithms to analyse social networks based on the spectral theory of graphs. These algorithms show very good results, but their disadvantage is the relatively high (albeit polynomial computational complexity for large graphs.At the same time it is obvious that the practical application capacity of the spectral graph theory methods is still underestimated, and it may be used as a basis to develop new methods.The work

  11. Cognitively Central Actors and Their Personal Networks in an Energy Efficiency Training Program

    Science.gov (United States)

    Hytönen, Kaisa; Palonen, Tuire; Hakkarainen, Kai

    2014-01-01

    This article aims to examine cognitively central actors and their personal networks in the emerging field of energy efficiency. Cognitively central actors are frequently sought for professional advice by other actors and, therefore, they are positioned in the middle of a social network. They often are important knowledge resources, especially in…

  12. Centrality of regions in RD networks: a new measurement approach using the concept of bridging paths

    NARCIS (Netherlands)

    Bergé, Laurent R; Wanzenböck, Iris; Scherngell, Thomas

    2017-01-01

    Centrality of regions in R&D networks: a new measurement approach using the concept of bridging paths. Regional Studies. This paper introduces a novel measure of regional centrality in the context of research and development (R&D) networks. It first demonstrates some substantial problems of social

  13. Networks and network analysis for defence and security

    CERN Document Server

    Masys, Anthony J

    2014-01-01

    Networks and Network Analysis for Defence and Security discusses relevant theoretical frameworks and applications of network analysis in support of the defence and security domains. This book details real world applications of network analysis to support defence and security. Shocks to regional, national and global systems stemming from natural hazards, acts of armed violence, terrorism and serious and organized crime have significant defence and security implications. Today, nations face an uncertain and complex security landscape in which threats impact/target the physical, social, economic

  14. Central Institutional Review Board Review for an Academic Trial Network

    Science.gov (United States)

    Kaufmann, Petra; O’Rourke, P. Pearl

    2016-01-01

    Problem Translating discoveries into therapeutics is often delayed by lengthy start-up periods for multicenter clinical trials. One cause of delay can be multiple institutional review board (IRB) reviews of the same protocol. Approach When developing the Network for Excellence in Neuroscience Clinical Trials (NeuroNEXT; hereafter, NN), the National Institute of Neurological Disorders and Stroke (NINDS) established a central IRB (CIRB) based at Massachusetts General Hospital, the academic medical center that received the NN clinical coordinating center grant. The 25 NN sites, located at U.S. academic institutions, agreed to required CIRB use for NN trials. Outcomes To delineate roles and establish legal relationships between the NN sites and the CIRB, the CIRB executed reliance agreements with the sites and their affiliates that hold federalwide assurance for the protection of human subjects (FWA); this took, on average, 84 days. The first NN protocol reviewed by the CIRB achieved full approval to allow participant enrollment within 56 days and went from grant award to the first patient visit in less than four months. The authors describe anticipated challenges related to institutional oversight responsibilities versus regulatory CIRB review as well as unanticipated challenges related to working with complex organizations that include multiple FWA-holding affiliates. Next Steps The authors anticipate that CIRB use will decrease NN trial start-up time and thus promote efficient trial implementation. They plan to collect data on timelines and costs associated with CIRB use. The NINDS plans to promote CIRB use in future initiatives. PMID:25406606

  15. Measuring Long-Term Impact Based on Network Centrality: Unraveling Cinematic Citations

    Science.gov (United States)

    Spitz, Andreas; Horvát, Emőke-Ágnes

    2014-01-01

    Traditional measures of success for film, such as box-office revenue and critical acclaim, lack the ability to quantify long-lasting impact and depend on factors that are largely external to the craft itself. With the growing number of films that are being created and large-scale data becoming available through crowd-sourced online platforms, an endogenous measure of success that is not reliant on manual appraisal is of increasing importance. In this article we propose such a ranking method based on a combination of centrality indices. We apply the method to a network that contains several types of citations between more than 40,000 international feature films. From this network we derive a list of milestone films, which can be considered to constitute the foundations of cinema. In a comparison to various existing lists of ‘greatest’ films, such as personal favourite lists, voting lists, lists of individual experts, and lists deduced from expert polls, the selection of milestone films is more diverse in terms of genres, actors, and main creators. Our results shed light on the potential of a systematic quantitative investigation based on cinematic influences in identifying the most inspiring creations in world cinema. In a broader perspective, we introduce a novel research question to large-scale citation analysis, one of the most intriguing topics that have been at the forefront of scientific enquiries for the past fifty years and have led to the development of various network analytic methods. In doing so, we transfer widely studied approaches from citation analysis to the the newly emerging field of quantification efforts in the arts. The specific contribution of this paper consists in modelling the multidimensional cinematic references as a growing multiplex network and in developing a methodology for the identification of central films in this network. PMID:25295877

  16. Measuring long-term impact based on network centrality: unraveling cinematic citations.

    Directory of Open Access Journals (Sweden)

    Andreas Spitz

    Full Text Available Traditional measures of success for film, such as box-office revenue and critical acclaim, lack the ability to quantify long-lasting impact and depend on factors that are largely external to the craft itself. With the growing number of films that are being created and large-scale data becoming available through crowd-sourced online platforms, an endogenous measure of success that is not reliant on manual appraisal is of increasing importance. In this article we propose such a ranking method based on a combination of centrality indices. We apply the method to a network that contains several types of citations between more than 40,000 international feature films. From this network we derive a list of milestone films, which can be considered to constitute the foundations of cinema. In a comparison to various existing lists of 'greatest' films, such as personal favourite lists, voting lists, lists of individual experts, and lists deduced from expert polls, the selection of milestone films is more diverse in terms of genres, actors, and main creators. Our results shed light on the potential of a systematic quantitative investigation based on cinematic influences in identifying the most inspiring creations in world cinema. In a broader perspective, we introduce a novel research question to large-scale citation analysis, one of the most intriguing topics that have been at the forefront of scientific enquiries for the past fifty years and have led to the development of various network analytic methods. In doing so, we transfer widely studied approaches from citation analysis to the the newly emerging field of quantification efforts in the arts. The specific contribution of this paper consists in modelling the multidimensional cinematic references as a growing multiplex network and in developing a methodology for the identification of central films in this network.

  17. Unraveling protein networks with power graph analysis.

    Science.gov (United States)

    Royer, Loïc; Reimann, Matthias; Andreopoulos, Bill; Schroeder, Michael

    2008-07-11

    Networks play a crucial role in computational biology, yet their analysis and representation is still an open problem. Power Graph Analysis is a lossless transformation of biological networks into a compact, less redundant representation, exploiting the abundance of cliques and bicliques as elementary topological motifs. We demonstrate with five examples the advantages of Power Graph Analysis. Investigating protein-protein interaction networks, we show how the catalytic subunits of the casein kinase II complex are distinguishable from the regulatory subunits, how interaction profiles and sequence phylogeny of SH3 domains correlate, and how false positive interactions among high-throughput interactions are spotted. Additionally, we demonstrate the generality of Power Graph Analysis by applying it to two other types of networks. We show how power graphs induce a clustering of both transcription factors and target genes in bipartite transcription networks, and how the erosion of a phosphatase domain in type 22 non-receptor tyrosine phosphatases is detected. We apply Power Graph Analysis to high-throughput protein interaction networks and show that up to 85% (56% on average) of the information is redundant. Experimental networks are more compressible than rewired ones of same degree distribution, indicating that experimental networks are rich in cliques and bicliques. Power Graphs are a novel representation of networks, which reduces network complexity by explicitly representing re-occurring network motifs. Power Graphs compress up to 85% of the edges in protein interaction networks and are applicable to all types of networks such as protein interactions, regulatory networks, or homology networks.

  18. A new betweenness centrality measure based on an algorithm for ranking the nodes of a network

    OpenAIRE

    Agryzkov, Taras; Oliver, Jose L.; Tortosa Grau, Leandro; Vicent, Jose F.

    2014-01-01

    We propose and discuss a new centrality index for urban street patterns represented as networks in geographical space. This centrality measure, that we call ranking-betweenness centrality, combines the idea behind the random-walk betweenness centrality measure and the idea of ranking the nodes of a network produced by an adapted PageRank algorithm. We initially use a PageRank algorithm in which we are able to transform some information of the network that we want to analyze into numerical val...

  19. Signed Link Analysis in Social Media Networks

    OpenAIRE

    Beigi, Ghazaleh; Tang, Jiliang; Liu, Huan

    2016-01-01

    Numerous real-world relations can be represented by signed networks with positive links (e.g., trust) and negative links (e.g., distrust). Link analysis plays a crucial role in understanding the link formation and can advance various tasks in social network analysis such as link prediction. The majority of existing works on link analysis have focused on unsigned social networks. The existence of negative links determines that properties and principles of signed networks are substantially dist...

  20. Social network analysis in medical education

    OpenAIRE

    Isba, Rachel; Woolf, Katherine; Hanneman, Robert

    2016-01-01

    Content\\ud Humans are fundamentally social beings. The social systems within which we live our lives (families, schools, workplaces, professions, friendship groups) have a significant influence on our health, success and well-being. These groups can be characterised as networks and analysed using social network analysis.\\ud \\ud Social Network Analysis\\ud Social network analysis is a mainly quantitative method for analysing how relationships between individuals form and affect those individual...

  1. Complex Network Analysis of Pakistan Railways

    Directory of Open Access Journals (Sweden)

    Yasir Tariq Mohmand

    2014-01-01

    Full Text Available We study the structural properties of Pakistan railway network (PRN, where railway stations are considered as nodes while edges are represented by trains directly linking two stations. The network displays small world properties and is assortative in nature. Based on betweenness and closeness centralities of the nodes, the most important cities are identified with respect to connectivity as this could help in identifying the potential congestion points in the network.

  2. Student network centrality and academic performance: evidence from United Nations University

    OpenAIRE

    Zhang, Y.; Rajabzadeh, I.; Lauterbach, R

    2009-01-01

    In this paper we empirically studied the relationship between network centrality and academic performance among a group of 47 PhD students from UNU-MERIT institute. We conducted an independent email survey and relied on social networks theory as well as standard econometric procedures to analyse the data. We found a significant reversed U-shaped relation between network centrality and students' academic performance. We controlled our results by several node's characteristics such as age, acad...

  3. Network analysis of wildfire transmission and implications for risk governance

    Science.gov (United States)

    Alan A. Ager; Cody R. Evers; Michelle A. Day; Haiganoush K. Preisler; Ana M. G. Barros; Max. Nielsen-Pincus

    2017-01-01

    We characterized wildfire transmission and exposure within a matrix of large land tenures (federal, state, and private) surrounding 56 communities within a 3.3 million ha fire prone region of central Oregon US. Wildfire simulation and network analysis were used to quantify the exchange of fire among land tenures and communities and analyze the relative contributions of...

  4. Using Social Network Analysis to Assess Mentorship and Collaboration in a Public Health Network.

    Science.gov (United States)

    Petrescu-Prahova, Miruna; Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B; Anderson, Lynda A

    2015-08-20

    Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level.

  5. Structural Analysis of Complex Networks

    CERN Document Server

    Dehmer, Matthias

    2011-01-01

    Filling a gap in literature, this self-contained book presents theoretical and application-oriented results that allow for a structural exploration of complex networks. The work focuses not only on classical graph-theoretic methods, but also demonstrates the usefulness of structural graph theory as a tool for solving interdisciplinary problems. Applications to biology, chemistry, linguistics, and data analysis are emphasized. The book is suitable for a broad, interdisciplinary readership of researchers, practitioners, and graduate students in discrete mathematics, statistics, computer science,

  6. Topological Analysis of Wireless Networks (TAWN)

    Science.gov (United States)

    2016-05-31

    19b. TELEPHONE NUMBER (Include area code) 31-05-2016 FINAL REPORT 12-02-2015 -- 31-05-2016 Topological Analysis of Wireless Networks (TAWN) Robinson...mathematical literature on sheaves that describes how to draw global ( network -wide) inferences from them. Wireless network , local homology, sheaf...topology U U U UU 32 Michael Robinson 202-885-3681 Final Report: May 2016 Topological Analysis of Wireless Networks Principal Investigator: Prof. Michael

  7. statnet: Software Tools for the Representation, Visualization, Analysis and Simulation of Network Data

    Directory of Open Access Journals (Sweden)

    Mark S. Handcock

    2007-12-01

    Full Text Available statnet is a suite of software packages for statistical network analysis. The packages implement recent advances in network modeling based on exponential-family random graph models (ERGM. The components of the package provide a comprehensive framework for ERGM-based network modeling, including tools for model estimation, model evaluation, model-based network simulation, and network visualization. This broad functionality is powered by a central Markov chain Monte Carlo (MCMC algorithm. The coding is optimized for speed and robustness.

  8. Review Essay: Does Qualitative Network Analysis Exist?

    Directory of Open Access Journals (Sweden)

    Rainer Diaz-Bone

    2007-01-01

    Full Text Available Social network analysis was formed and established in the 1970s as a way of analyzing systems of social relations. In this review the theoretical-methodological standpoint of social network analysis ("structural analysis" is introduced and the different forms of social network analysis are presented. Structural analysis argues that social actors and social relations are embedded in social networks, meaning that action and perception of actors as well as the performance of social relations are influenced by the network structure. Since the 1990s structural analysis has integrated concepts such as agency, discourse and symbolic orientation and in this way structural analysis has opened itself. Since then there has been increasing use of qualitative methods in network analysis. They are used to include the perspective of the analyzed actors, to explore networks, and to understand network dynamics. In the reviewed book, edited by Betina HOLLSTEIN and Florian STRAUS, the twenty predominantly empirically orientated contributions demonstrate the possibilities of combining quantitative and qualitative methods in network analyses in different research fields. In this review we examine how the contributions succeed in applying and developing the structural analysis perspective, and the self-positioning of "qualitative network analysis" is evaluated. URN: urn:nbn:de:0114-fqs0701287

  9. Google matrix analysis of directed networks

    Science.gov (United States)

    Ermann, Leonardo; Frahm, Klaus M.; Shepelyansky, Dima L.

    2015-10-01

    In the past decade modern societies have developed enormous communication and social networks. Their classification and information retrieval processing has become a formidable task for the society. Because of the rapid growth of the World Wide Web, and social and communication networks, new mathematical methods have been invented to characterize the properties of these networks in a more detailed and precise way. Various search engines extensively use such methods. It is highly important to develop new tools to classify and rank a massive amount of network information in a way that is adapted to internal network structures and characteristics. This review describes the Google matrix analysis of directed complex networks demonstrating its efficiency using various examples including the World Wide Web, Wikipedia, software architectures, world trade, social and citation networks, brain neural networks, DNA sequences, and Ulam networks. The analytical and numerical matrix methods used in this analysis originate from the fields of Markov chains, quantum chaos, and random matrix theory.

  10. Social network analysis community detection and evolution

    CERN Document Server

    Missaoui, Rokia

    2015-01-01

    This book is devoted to recent progress in social network analysis with a high focus on community detection and evolution. The eleven chapters cover the identification of cohesive groups, core components and key players either in static or dynamic networks of different kinds and levels of heterogeneity. Other important topics in social network analysis such as influential detection and maximization, information propagation, user behavior analysis, as well as network modeling and visualization are also presented. Many studies are validated through real social networks such as Twitter. This edit

  11. Network analysis literacy a practical approach to the analysis of networks

    CERN Document Server

    Zweig, Katharina A

    2014-01-01

    Network Analysis Literacy focuses on design principles for network analytics projects. The text enables readers to: pose a defined network analytic question; build a network to answer the question; choose or design the right network analytic methods for a particular purpose, and more.

  12. Identifying the Critical Links in Road Transportation Networks: Centrality-based approach utilizing structural properties

    Energy Technology Data Exchange (ETDEWEB)

    Chinthavali, Supriya [Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)

    2016-04-01

    Surface transportation road networks share structural properties similar to other complex networks (e.g., social networks, information networks, biological networks, and so on). This research investigates the structural properties of road networks for any possible correlation with the traffic characteristics such as link flows those determined independently. Additionally, we define a criticality index for the links of the road network that identifies the relative importance in the network. We tested our hypotheses with two sample road networks. Results show that, correlation exists between the link flows and centrality measures of a link of the road (dual graph approach is followed) and the criticality index is found to be effective for one test network to identify the vulnerable nodes.

  13. Applications of Social Network Analysis

    Science.gov (United States)

    Thilagam, P. Santhi

    A social network [2] is a description of the social structure between actors, mostly persons, groups or organizations. It indicates the ways in which they are connected with each other by some relationship such as friendship, kinship, finance exchange etc. In a nutshell, when the person uses already known/unknown people to create new contacts, it forms social networking. The social network is not a new concept rather it can be formed when similar people interact with each other directly or indirectly to perform particular task. Examples of social networks include a friendship networks, collaboration networks, co-authorship networks, and co-employees networks which depict the direct interaction among the people. There are also other forms of social networks, such as entertainment networks, business Networks, citation networks, and hyperlink networks, in which interaction among the people is indirect. Generally, social networks operate on many levels, from families up to the level of nations and assists in improving interactive knowledge sharing, interoperability and collaboration.

  14. Network meta-analysis, electrical networks and graph theory.

    Science.gov (United States)

    Rücker, Gerta

    2012-12-01

    Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.

  15. Sovereign public debt crisis in Europe. A network analysis

    Science.gov (United States)

    Matesanz, David; Ortega, Guillermo J.

    2015-10-01

    In this paper we analyse the evolving network structure of the quarterly public debt-to-GDP ratio from 2000 to 2014. By applying tools and concepts coming from complex systems we study the effects of the global financial crisis over public debt network connections and communities. Two main results arise from this analysis: firstly, countries public debts tend to synchronize their evolution, increasing global connectivity in the network and dramatically decreasing the number of communities. Secondly, a disruption in previous structure is observed at the time of the shock, emerging a more centralized and less diversify network topological organization which might be more prone to suffer contagion effects. This last fact is evidenced by an increasing tendency in countries of similar level of public debt to be connected between them, which we have quantified by the network assortativity.

  16. Empathy and well-being correlate with centrality in different social networks.

    Science.gov (United States)

    Morelli, Sylvia A; Ong, Desmond C; Makati, Rucha; Jackson, Matthew O; Zaki, Jamil

    2017-09-12

    Individuals benefit from occupying central roles in social networks, but little is known about the psychological traits that predict centrality. Across four college freshman dorms (n = 193), we characterized individuals with a battery of personality questionnaires and also asked them to nominate dorm members with whom they had different types of relationships. This revealed several social networks within dorm communities with differing characteristics. In particular, additional data showed that networks varied in the degree to which nominations depend on (i) trust and (ii) shared fun and excitement. Networks more dependent upon trust were further defined by fewer connections than those more dependent on fun. Crucially, network and personality features interacted to predict individuals' centrality: people high in well-being (i.e., life satisfaction and positive emotion) were central to networks characterized by fun, whereas people high in empathy were central to networks characterized by trust. Together, these findings provide network-based corroboration of psychological evidence that well-being is socially attractive, whereas empathy supports close relationships. More broadly, these data highlight how an individual's personality relates to the roles that they play in sustaining their community.

  17. Multiplex network analysis of employee performance and employee social relationships

    Science.gov (United States)

    Cai, Meng; Wang, Wei; Cui, Ying; Stanley, H. Eugene

    2018-01-01

    In human resource management, employee performance is strongly affected by both formal and informal employee networks. Most previous research on employee performance has focused on monolayer networks that can represent only single categories of employee social relationships. We study employee performance by taking into account the entire multiplex structure of underlying employee social networks. We collect three datasets consisting of five different employee relationship categories in three firms, and predict employee performance using degree centrality and eigenvector centrality in a superimposed multiplex network (SMN) and an unfolded multiplex network (UMN). We use a quadratic assignment procedure (QAP) analysis and a regression analysis to demonstrate that the different categories of relationship are mutually embedded and that the strength of their impact on employee performance differs. We also use weighted/unweighted SMN/UMN to measure the predictive accuracy of this approach and find that employees with high centrality in a weighted UMN are more likely to perform well. Our results shed new light on how social structures affect employee performance.

  18. Cohesion network analysis of CSCL participation.

    Science.gov (United States)

    Dascalu, Mihai; McNamara, Danielle S; Trausan-Matu, Stefan; Allen, Laura K

    2017-04-13

    The broad use of computer-supported collaborative-learning (CSCL) environments (e.g., instant messenger-chats, forums, blogs in online communities, and massive open online courses) calls for automated tools to support tutors in the time-consuming process of analyzing collaborative conversations. In this article, the authors propose and validate the cohesion network analysis (CNA) model, housed within the ReaderBench platform. CNA, grounded in theories of cohesion, dialogism, and polyphony, is similar to social network analysis (SNA), but it also considers text content and discourse structure and, uniquely, uses automated cohesion indices to generate the underlying discourse representation. Thus, CNA enhances the power of SNA by explicitly considering semantic cohesion while modeling interactions between participants. The primary purpose of this article is to describe CNA analysis and to provide a proof of concept, by using ten chat conversations in which multiple participants debated the advantages of CSCL technologies. Each participant's contributions were human-scored on the basis of their relevance in terms of covering the central concepts of the conversation. SNA metrics, applied to the CNA sociogram, were then used to assess the quality of each member's degree of participation. The results revealed that the CNA indices were strongly correlated to the human evaluations of the conversations. Furthermore, a stepwise regression analysis indicated that the CNA indices collectively predicted 54% of the variance in the human ratings of participation. The results provide promising support for the use of automated computational assessments of collaborative participation and of individuals' degrees of active involvement in CSCL environments.

  19. Statistical Analysis of Bus Networks in India

    CERN Document Server

    Chatterjee, Atanu; Ramadurai, Gitakrishnan

    2015-01-01

    Through the past decade the field of network science has established itself as a common ground for the cross-fertilization of exciting inter-disciplinary studies which has motivated researchers to model almost every physical system as an interacting network consisting of nodes and links. Although public transport networks such as airline and railway networks have been extensively studied, the status of bus networks still remains in obscurity. In developing countries like India, where bus networks play an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer some of the basic questions on its evolution, growth, robustness and resiliency. In this paper, we model the bus networks of major Indian cities as graphs in \\textit{L}-space, and evaluate their various statistical properties using concepts from network science. Our analysis reveals a wide spectrum of network topology with the common underlying feature of small-world property. We observe tha...

  20. Centralized electricity generation in offshore wind farms using hydraulic networks

    NARCIS (Netherlands)

    Jarquin Laguna, A.

    2017-01-01

    The work presented in this thesis explores a new way of generation, collection and transmission of wind energy inside a wind farm, in which the electrical conversion does not occur during any intermediate conversion step before the energy has reached the offshore central platform. A centralized

  1. Egocentric social network analysis of pathological gambling.

    Science.gov (United States)

    Meisel, Matthew K; Clifton, Allan D; Mackillop, James; Miller, Joshua D; Campbell, W Keith; Goodie, Adam S

    2013-03-01

    To apply social network analysis (SNA) to investigate whether frequency and severity of gambling problems were associated with different network characteristics among friends, family and co-workers is an innovative way to look at relationships among individuals; the current study was the first, to our knowledge, to apply SNA to gambling behaviors. Egocentric social network analysis was used to characterize formally the relationships between social network characteristics and gambling pathology. Laboratory-based questionnaire and interview administration. Forty frequent gamblers (22 non-pathological gamblers, 18 pathological gamblers) were recruited from the community. The SNA revealed significant social network compositional differences between the two groups: pathological gamblers (PGs) had more gamblers, smokers and drinkers in their social networks than did non-pathological gamblers (NPGs). PGs had more individuals in their network with whom they personally gambled, smoked and drank than those with who were NPG. Network ties were closer to individuals in their networks who gambled, smoked and drank more frequently. Associations between gambling severity and structural network characteristics were not significant. Pathological gambling is associated with compositional but not structural differences in social networks. Pathological gamblers differ from non-pathological gamblers in the number of gamblers, smokers and drinkers in their social networks. Homophily within the networks also indicates that gamblers tend to be closer with other gamblers. This homophily may serve to reinforce addictive behaviors, and may suggest avenues for future study or intervention. © 2012 The Authors, Addiction © 2012 Society for the Study of Addiction.

  2. Power indices of influence games and new centrality measures for agent societies and social networks

    OpenAIRE

    Molinero Albareda, Xavier; Riquelme Csori, Fabián; Serna Iglesias, María José

    2014-01-01

    The final publication is available on http://link.springer.com We propose as centrality measures for social networks two classical power indices, Banzhaf and Shapley-Shubik, and two new measures, effort and satisfaction, related to the spread of influence process that emerge from the subjacent influence game. We perform a comparison of these measures with three well known centrality measures, degree, closeness and betweenness, applied to three simple social networks. Peer Reviewed

  3. From Social Network (Centralized vs. Decentralized) to Collective Decision-Making (Unshared vs. Shared Consensus)

    Science.gov (United States)

    Sueur, Cédric; Deneubourg, Jean-Louis; Petit, Odile

    2012-01-01

    Relationships we have with our friends, family, or colleagues influence our personal decisions, as well as decisions we make together with others. As in human beings, despotism and egalitarian societies seem to also exist in animals. While studies have shown that social networks constrain many phenomena from amoebae to primates, we still do not know how consensus emerges from the properties of social networks in many biological systems. We created artificial social networks that represent the continuum from centralized to decentralized organization and used an agent-based model to make predictions about the patterns of consensus and collective movements we observed according to the social network. These theoretical results showed that different social networks and especially contrasted ones – star network vs. equal network - led to totally different patterns. Our model showed that, by moving from a centralized network to a decentralized one, the central individual seemed to lose its leadership in the collective movement's decisions. We, therefore, showed a link between the type of social network and the resulting consensus. By comparing our theoretical data with data on five groups of primates, we confirmed that this relationship between social network and consensus also appears to exist in animal societies. PMID:22393416

  4. Satellite image analysis using neural networks

    Science.gov (United States)

    Sheldon, Roger A.

    1990-01-01

    The tremendous backlog of unanalyzed satellite data necessitates the development of improved methods for data cataloging and analysis. Ford Aerospace has developed an image analysis system, SIANN (Satellite Image Analysis using Neural Networks) that integrates the technologies necessary to satisfy NASA's science data analysis requirements for the next generation of satellites. SIANN will enable scientists to train a neural network to recognize image data containing scenes of interest and then rapidly search data archives for all such images. The approach combines conventional image processing technology with recent advances in neural networks to provide improved classification capabilities. SIANN allows users to proceed through a four step process of image classification: filtering and enhancement, creation of neural network training data via application of feature extraction algorithms, configuring and training a neural network model, and classification of images by application of the trained neural network. A prototype experimentation testbed was completed and applied to climatological data.

  5. Changes in functional network centrality underlie cognitive dysfunction and physical disability in multiple sclerosis.

    Science.gov (United States)

    Schoonheim, M M; Geurts, Jjg; Wiebenga, O T; De Munck, J C; Polman, C H; Stam, C J; Barkhof, F; Wink, A M

    2014-07-01

    Cognitive dysfunction in multiple sclerosis (MS) has a large impact on the quality of life and is poorly understood. The aim of this study was to investigate functional network integrity in MS, and relate this to cognitive dysfunction and physical disability. Resting state fMRI scans were included of 128 MS patients and 50 controls. Eigenvector centrality mapping (ECM) was applied, a graph analysis technique that ranks the importance of brain regions based on their connectivity patterns. Significant ECM changes were related to physical disability and cognitive dysfunction. In MS patients, ECM values were increased in bilateral thalamus and posterior cingulate (PCC) areas, and decreased in sensorimotor and ventral stream areas. Sensorimotor ECM decreases were related to higher EDSS (rho = -0.24, p = 0.007), while ventral stream decreases were related to poorer average cognition (rho = 0.23, p = 0.009). The thalamus displayed increased connectivity to sensorimotor and ventral stream areas. In MS, areas in the ventral stream and sensorimotor cortex appear to become less central in the entire functional network of the brain, which is associated with clinico-cognitive dysfunction. The thalamus, however, displays increased connectivity with these areas. These findings may aid in further elucidating the function of functional reorganization processes in MS. © The Author(s) 2013.

  6. Social Network Analysis and informal trade

    DEFF Research Database (Denmark)

    Walther, Olivier

    networks can be applied to better understand informal trade in developing countries, with a particular focus on Africa. The paper starts by discussing some of the fundamental concepts developed by social network analysis. Through a number of case studies, we show how social network analysis can...... illuminate the relevant causes of social patterns, the impact of social ties on economic performance, the diffusion of resources and information, and the exercise of power. The paper then examines some of the methodological challenges of social network analysis and how it can be combined with other...

  7. Dynamic Network Centrality Summarizes Learning in the Human Brain

    OpenAIRE

    Mantzaris, Alexander V.; Bassett, Danielle S.; Wymbs, Nicholas F.; Estrada, Ernesto; Porter, Mason A.; Mucha, Peter J; Grafton, Scott T.; Higham, Desmond J.

    2012-01-01

    We study functional activity in the human brain using functional Magnetic Resonance Imaging and recently developed tools from network science. The data arise from the performance of a simple behavioural motor learning task. Unsupervised clustering of subjects with respect to similarity of network activity measured over three days of practice produces significant evidence of `learning', in the sense that subjects typically move between clusters (of subjects whose dynamics are similar) as time ...

  8. Brief communication: Co-seismic displacement on 26 and 30 October 2016 (Mw = 5.9 and 6.5) - earthquakes in central Italy from the analysis of a local GNSS network

    Science.gov (United States)

    De Guidi, Giorgio; Vecchio, Alessia; Brighenti, Fabio; Caputo, Riccardo; Carnemolla, Francesco; Di Pietro, Adriano; Lupo, Marco; Maggini, Massimiliano; Marchese, Salvatore; Messina, Danilo; Monaco, Carmelo; Naso, Salvatore

    2017-11-01

    On 24 August 2016 a strong earthquake (Mw = 6.0) affected central Italy and an intense seismic sequence started. Field observations, DInSAR (Differential INterferometry Synthetic-Aperture Radar) analyses and preliminary focal mechanisms, as well as the distribution of aftershocks, suggested the reactivation of the northern sector of the Laga fault, the southern part of which was already rebooted during the 2009 L'Aquila sequence, and of the southern segment of the Mt Vettore fault system (MVFS). Based on this preliminary information and following the stress-triggering concept (Stein, 1999; Steacy et al., 2005), we tentatively identified a potential fault zone that is very vulnerable to future seismic events just north of the earlier epicentral area. Accordingly, we planned a local geodetic network consisting of five new GNSS (Global Navigation Satellite System) stations located a few kilometres away from both sides of the MVFS. This network was devoted to working out, at least partially but in some detail, the possible northward propagation of the crustal network ruptures. The building of the stations and a first set of measurements were carried out during a first campaign (30 September and 2 October 2016). On 26 October 2016, immediately north of the epicentral area of the 24 August event, another earthquake (Mw = 5.9) occurred, followed 4 days later (30 October) by the main shock (Mw = 6.5) of the whole 2016 summer-autumn seismic sequence. Our local geodetic network was fully affected by the new events and therefore we performed a second campaign soon after (11-13 November 2016). In this brief note, we provide the results of our geodetic measurements that registered the co-seismic and immediately post-seismic deformation of the two major October shocks, documenting in some detail the surface deformation close to the fault trace. We also compare our results with the available surface deformation field of the broader area, obtained on the basis of the DIn

  9. Social network analysis and supply chain management

    Directory of Open Access Journals (Sweden)

    Raúl Rodríguez Rodríguez

    2016-01-01

    Full Text Available This paper deals with social network analysis and how it could be integrated within supply chain management from a decision-making point of view. Even though the benefits of using social analysis have are widely accepted at both academic and industry/services context, there is still a lack of solid frameworks that allow decision-makers to connect the usage and obtained results of social network analysis – mainly both information and knowledge flows and derived results- with supply chain management objectives and goals. This paper gives an overview of social network analysis, the main social network analysis metrics, supply chain performance and, finally, it identifies how future frameworks could close the gap and link the results of social network analysis with the supply chain management decision-making processes.

  10. Fluid power network for centralized electricity generation in offshore wind farms

    NARCIS (Netherlands)

    Jarquin-Laguna, A.

    2014-01-01

    An innovative and completely different wind-energy conversion system is studied where a centralized electricity generation within a wind farm is proposed by means of a hydraulic network. This paper presents the dynamic interaction of two turbines when they are coupled to the same hydraulic network.

  11. Indicators of opinion leadership in customer networks : self-reports and degree centrality

    NARCIS (Netherlands)

    Risselada, Hans; Verhoef, Pieter; Bijmolt, Tammo

    In this paper, we assess two alternative indicators of opinion leadership, self-reported opinion leadership and degree centrality, on the same dataset. We also investigate the interaction effect of these two indicators and the social network environment on opinion leadership. We use social network

  12. A centralized feedback control model for resource management in wireless networks

    NARCIS (Netherlands)

    Yang, Y.; Haverkort, Boudewijn R.H.M.; Heijenk, Geert; Cloth, L.; Hiltunen, M.; van Moorsel, A.

    2007-01-01

    In a wireless environment, guaranteeing QoS constraints is challenging because applications at multiple devices share the same limited radio bandwidth in the network. In this paper we introduce and study a resource management model for centralized wireless networks, using feedback control theory.

  13. Status Struggles: Network Centrality and Gender Segregation in Same- and Cross-Gender Aggression

    Science.gov (United States)

    Faris, Robert; Felmlee, Diane

    2011-01-01

    Literature on aggression often suggests that individual deficiencies, such as social incompetence, psychological difficulties, or troublesome home environments, are responsible for aggressive behavior. In this article, by contrast, we examine aggression from a social network perspective, arguing that social network centrality, our primary measure…

  14. Communities of Practice for Local Capacity in Central Asia : The Community Empowerment Network

    OpenAIRE

    Caldwell Johnson, Erik

    2005-01-01

    In 2002 the World Bank Institute and Europe and Central Asia Region (ECA) launched the Community Empowerment Network (CEN): four national networks linked through regional activities that would build the capacity of communities and development partners to implement community-driven development (CDD) projects. CEN has to date had clear successes as well as difficulties-particularly in linkag...

  15. Explaining How Political Actors Gain Strategic Positions: Predictors of Centrality in State Reading Policy Issue Networks

    Science.gov (United States)

    Young, Tamara V.; Wang, Yuling; Lewis, Wayne D.

    2016-01-01

    Using data from interviews with 111 reading policy actors from California, Connecticut, Michigan, and Utah, this study explains how individuals acquire central positions in issue networks. Regression analyses showed that the greater a policy actor's reputed influence was and the more similar their preferences were to other members in the network,…

  16. Knowledge Contribution in Knowledge Networks: Effects of Participants’ Central Positions on Contribution Quality

    OpenAIRE

    Sedighi, M; Hamedi, Mohsen

    2016-01-01

    Knowledge networks play a crucial role in contemporary organisations to improve participation for knowledge sharing. Examining employees’ knowledge contribution play an important role for in success implementing knowledgenetworks. Whereas most part of studies emphasis on the quantity aspect of knowledge contributions, the success of knowledge networks also depends strongly on quality aspect of participants’ voluntarily contributions. Further, employees’ central positions can make a strategic ...

  17. A Network Text Analysis of David Ayer’s Fury

    Directory of Open Access Journals (Sweden)

    Starling David Hunter

    2015-12-01

    Full Text Available Network Text Analysis (NTA involves the creation of networks of words and/or concepts from linguistic data. Its key insight is that the position of words and concepts in a text network provides vital clues to the central and underlying themes of the text as a whole. Recent research has relied on inductive approaches to identify these themes. In this study we demonstrate a deductive approach that we apply to the screenplay of the 2014 World War II-era film Fury. Specifically, we first use genre expectations theory to establish prior expectations as to the key themes associated with war films. We then empirically test whether words and concepts associated with the most influentially-positioned nodes are consistent with themes common to the war-film genre. As predicted, we find that words and concepts associated with the least constrained nodes in the text network were significantly more likely to be associated with the war, action, and biography genres and significantly less likely to be associated with the mystery, science-fiction, fantasy, and film-noir genres. Keywords: content analysis, text analysis, network text analysis, semantic network analysis, film studies, screenplay, screenwriting, war movies, World War II, tanks

  18. 4th International Conference in Network Analysis

    CERN Document Server

    Koldanov, Petr; Pardalos, Panos

    2016-01-01

    The contributions in this volume cover a broad range of topics including maximum cliques, graph coloring, data mining, brain networks, Steiner forest, logistic and supply chain networks. Network algorithms and their applications to market graphs, manufacturing problems, internet networks and social networks are highlighted. The "Fourth International Conference in Network Analysis," held at the Higher School of Economics, Nizhny Novgorod in May 2014, initiated joint research between scientists, engineers and researchers from academia, industry and government; the major results of conference participants have been reviewed and collected in this Work. Researchers and students in mathematics, economics, statistics, computer science and engineering will find this collection a valuable resource filled with the latest research in network analysis.

  19. Centrality and charisma: comparing how leader networks and attributions affect team performance.

    Science.gov (United States)

    Balkundi, Prasad; Kilduff, Martin; Harrison, David A

    2011-11-01

    When leaders interact in teams with their subordinates, they build social capital that can have positive effects on team performance. Does this social capital affect team performance because subordinates come to see the leader as charismatic? We answered this question by examining 2 models. First, we tested the charisma-to-centrality model according to which the leader's charisma facilitates the occupation of a central position in the informal advice network. From this central position, the leader positively influences team performance. Second, we examined the centrality-to-charisma model according to which charisma is attributed to those leaders who are socially active in terms of giving and receiving advice. Attributed charisma facilitates increased team performance. We tested these 2 models in 2 different studies. In the first study, based on time-separated, multisource data emanating from members of 56 work teams, we found support for the centrality-to-charisma model. Formal leaders who were central within team advice networks were seen as charismatic by subordinates, and this charisma was associated with high team performance. To clarify how leader network centrality affected the emergence of charismatic leadership, we designed Study 2 in which, for 79 student teams, we measured leader networking activity and leader charisma at 2 different points in time and related these variables to team performance measured at a third point in time. On the basis of this temporally separated data set, we again found support for the centrality-to-charisma model. (c) 2011 APA, all rights reserved.

  20. METHODOLOGY OF MATHEMATICAL ANALYSIS IN POWER NETWORK

    OpenAIRE

    Jerzy Szkutnik; Mariusz Kawecki

    2008-01-01

    Power distribution network analysis is taken into account. Based on correlation coefficient authors establish methodology of mathematical analysis useful in finding substations bear responsibility for power stoppage. Also methodology of risk assessment will be carried out.

  1. Measuring Road Network Vulnerability with Sensitivity Analysis

    Science.gov (United States)

    Jun-qiang, Leng; Long-hai, Yang; Liu, Wei-yi; Zhao, Lin

    2017-01-01

    This paper focuses on the development of a method for road network vulnerability analysis, from the perspective of capacity degradation, which seeks to identify the critical infrastructures in the road network and the operational performance of the whole traffic system. This research involves defining the traffic utility index and modeling vulnerability of road segment, route, OD (Origin Destination) pair and road network. Meanwhile, sensitivity analysis method is utilized to calculate the change of traffic utility index due to capacity degradation. This method, compared to traditional traffic assignment, can improve calculation efficiency and make the application of vulnerability analysis to large actual road network possible. Finally, all the above models and calculation method is applied to actual road network evaluation to verify its efficiency and utility. This approach can be used as a decision-supporting tool for evaluating the performance of road network and identifying critical infrastructures in transportation planning and management, especially in the resource allocation for mitigation and recovery. PMID:28125706

  2. Constructing an Intelligent Patent Network Analysis Method

    OpenAIRE

    Chao-Chan Wu; Ching-Bang Yao

    2012-01-01

    Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks...

  3. Place and identity: networks of Neolithic communities in Central Europe

    Directory of Open Access Journals (Sweden)

    Roderick B. Salisbury

    2012-12-01

    Full Text Available The multi-layered and multi-scalar nature of the term ‘community’ makes it a useful tool for both particularistic studies and cross-cultural comparisons, connecting scales of community to regional scales of settlement, exchange and mobility. This paper explores three general themes of community: community as place, as identity and as network. A case study of Neolithic communities in eastern Hungary and Lower Austria demonstrates a spatial and geoarchaeological approach to understanding the relational aspects of places, networks and identity to develop a social archaeology of communities.

  4. SISMIKO: emergency network deployment and data sharing for the 2016 central Italy seismic sequence

    Directory of Open Access Journals (Sweden)

    Milena Moretti

    2016-12-01

    Full Text Available At 01:36 UTC (03:36 local time on August 24th 2016, an earthquake Mw 6.0 struck an extensive sector of the central Apennines (coordinates: latitude 42.70° N, longitude 13.23° E, 8.0 km depth. The earthquake caused about 300 casualties and severe damage to the historical buildings and economic activity in an area located near the borders of the Umbria, Lazio, Abruzzo and Marche regions. The Istituto Nazionale di Geofisica e Vulcanologia (INGV located in few minutes the hypocenter near Accumoli, a small town in the province of Rieti. In the hours after the quake, dozens of events were recorded by the National Seismic Network (Rete Sismica Nazionale, RSN of the INGV, many of which had a ML > 3.0. The density and coverage of the RSN in the epicentral area meant the epicenter and magnitude of the main event and subsequent shocks that followed it in the early hours of the seismic sequence were well constrained. However, in order to better constrain the localizations of the aftershock hypocenters, especially the depths, a denser seismic monitoring network was needed. Just after the mainshock, SISMIKO, the coordinating body of the emergency seismic network at INGV, was activated in order to install a temporary seismic network integrated with the existing permanent network in the epicentral area. From August the 24th to the 30th, SISMIKO deployed eighteen seismic stations, generally six components (equipped with both velocimeter and accelerometer, with thirteen of the seismic station transmitting in real-time to the INGV seismic monitoring room in Rome. The design and geometry of the temporary network was decided in consolation with other groups who were deploying seismic stations in the region, namely EMERSITO (a group studying site-effects, and the emergency Italian strong motion network (RAN managed by the National Civil Protection Department (DPC. Further 25 BB temporary seismic stations were deployed by colleagues of the British Geological Survey

  5. Predictive structural dynamic network analysis.

    Science.gov (United States)

    Chen, Rong; Herskovits, Edward H

    2015-04-30

    Classifying individuals based on magnetic resonance data is an important task in neuroscience. Existing brain network-based methods to classify subjects analyze data from a cross-sectional study and these methods cannot classify subjects based on longitudinal data. We propose a network-based predictive modeling method to classify subjects based on longitudinal magnetic resonance data. Our method generates a dynamic Bayesian network model for each group which represents complex spatiotemporal interactions among brain regions, and then calculates a score representing that subject's deviation from expected network patterns. This network-derived score, along with other candidate predictors, are used to construct predictive models. We validated the proposed method based on simulated data and the Alzheimer's Disease Neuroimaging Initiative study. For the Alzheimer's Disease Neuroimaging Initiative study, we built a predictive model based on the baseline biomarker characterizing the baseline state and the network-based score which was constructed based on the state transition probability matrix. We found that this combined model achieved 0.86 accuracy, 0.85 sensitivity, and 0.87 specificity. For the Alzheimer's Disease Neuroimaging Initiative study, the model based on the baseline biomarkers achieved 0.77 accuracy. The accuracy of our model is significantly better than the model based on the baseline biomarkers (p-value=0.002). We have presented a method to classify subjects based on structural dynamic network model based scores. This method is of great importance to distinguish subjects based on structural network dynamics and the understanding of the network architecture of brain processes and disorders. Copyright © 2015 Elsevier B.V. All rights reserved.

  6. Assessing state-level active living promotion using network analysis.

    Science.gov (United States)

    Buchthal, Opal Vanessa; Taniguchi, Nicole; Iskandar, Livia; Maddock, Jay

    2013-01-01

    Physical inactivity is a growing problem in the United States, one that is being addressed through the development of active living communities. However, active living promotion requires collaboration among organizations that may not have previously shared goals. A network analysis was conducted to assess Hawaii's active living promotion network. Twenty-six organizations playing a significant role in promoting active living in Hawaii were identified and surveyed about their frequency of contact, level of collaboration, and funding flow with other agencies. A communication network was identified linking all agencies. This network had many long pathways, impeding information flow. The Department of Health (DOH) and the State Nutrition and Physical Activity Coalition (NPAC) were central nodes, but DOH connected state agencies while NPAC linked county and voluntary organizations. Within the network, information sharing was common, but collaboration and formal partnership were low. Linkages between county and state agencies, between counties, and between state agencies with different core agendas were particularly low. Results suggest that in the early stages of development, active living networks may be divided by geography and core missions, requiring work to bridge these divides. Network mapping appears helpful in identifying areas for network development.

  7. Comparative analysis of weighted gene co-expression networks in human and mouse.

    Science.gov (United States)

    Eidsaa, Marius; Stubbs, Lisa; Almaas, Eivind

    2017-01-01

    The application of complex network modeling to analyze large co-expression data sets has gained traction during the last decade. In particular, the use of the weighted gene co-expression network analysis framework has allowed an unbiased and systems-level investigation of genotype-phenotype relationships in a wide range of systems. Since mouse is an important model organism for biomedical research on human disease, it is of great interest to identify similarities and differences in the functional roles of human and mouse orthologous genes. Here, we develop a novel network comparison approach which we demonstrate by comparing two gene-expression data sets from a large number of human and mouse tissues. The method uses weighted topological overlap alongside the recently developed network-decomposition method of s-core analysis, which is suitable for making gene-centrality rankings for weighted networks. The aim is to identify globally central genes separately in the human and mouse networks. By comparing the ranked gene lists, we identify genes that display conserved or diverged centrality-characteristics across the networks. This framework only assumes a single threshold value that is chosen from a statistical analysis, and it may be applied to arbitrary network structures and edge-weight distributions, also outside the context of biology. When conducting the comparative network analysis, both within and across the two species, we find a clear pattern of enrichment of transcription factors, for the homeobox domain in particular, among the globally central genes. We also perform gene-ontology term enrichment analysis and look at disease-related genes for the separate networks as well as the network comparisons. We find that gene ontology terms related to regulation and development are generally enriched across the networks. In particular, the genes FOXE3, RHO, RUNX2, ALX3 and RARA, which are disease genes in either human or mouse, are on the top-10 list of globally

  8. A graph theoretic analysis of leverage centrality

    Directory of Open Access Journals (Sweden)

    Roger Vargas, Jr.

    2017-12-01

    Full Text Available In 2010, Joyce et al. defined the leverage centrality of vertices in a graph as a means to analyze functional connections within the human brain. In this metric a degree of a vertex is compared to the degrees of all it neighbors. We investigate this property from a mathematical perspective. We first outline some of the basic properties and then compute leverage centralities of vertices in different families of graphs. In particular, we show there is a surprising connection between the number of distinct leverage centralities in the Cartesian product of paths and the triangle numbers.

  9. Complex network analysis of extreme precipitation over the Indian subcontinent.

    Science.gov (United States)

    Stolbova, Veronika; Kurths, Jürgen

    2013-04-01

    The Indian monsoon is a large scale pattern in the climate system of the Earth. The motivation of our work was to reveal spatial structures in strong precipitation over the Indian subcontinent, and their evolution during the year, because it is crucial as for understanding of monsoon regularities as well for India's agriculture and economy. We present an analysis of extreme rainfall over the Indian peninsula and Sri Lanka. Using the method of event synchronization we constructed networks of extreme rainfall events(heavier than the 90-th percentile) for three time periods: during the Indian summer monsoon (ISM, June-September), the Northeast monsoon (NEM, October - December, so called winter monsoon) and period before the summer monsoon (January - May). Obtained networks show how extreme rainfall for specific areas in India is synchronized with extreme rainfall for other areas in India. Analysis of degree centrality of the networks reveals clusters of extreme rainfall events in India which are strongly connected to maximal number of other areas with extreme rainfall events, e.g., North Pakistan and the Eastern Ghats. Additionally, betweenness centrality shows areas that are important in the sense of water transport in the networks (e.g. the Himalayas, Western Ghats, Eastern Ghats etc.). By comparison of networks before the summer monsoon, during summer and winter monsoon season we determined how spatial patterns of rainfalls synchronization change during the year. These changes play a crucial role in the organization of the rainfall all over the Indian subcontinent.

  10. NEAT: an efficient network enrichment analysis test.

    Science.gov (United States)

    Signorelli, Mirko; Vinciotti, Veronica; Wit, Ernst C

    2016-09-05

    Network enrichment analysis is a powerful method, which allows to integrate gene enrichment analysis with the information on relationships between genes that is provided by gene networks. Existing tests for network enrichment analysis deal only with undirected networks, they can be computationally slow and are based on normality assumptions. We propose NEAT, a test for network enrichment analysis. The test is based on the hypergeometric distribution, which naturally arises as the null distribution in this context. NEAT can be applied not only to undirected, but to directed and partially directed networks as well. Our simulations indicate that NEAT is considerably faster than alternative resampling-based methods, and that its capacity to detect enrichments is at least as good as the one of alternative tests. We discuss applications of NEAT to network analyses in yeast by testing for enrichment of the Environmental Stress Response target gene set with GO Slim and KEGG functional gene sets, and also by inspecting associations between functional sets themselves. NEAT is a flexible and efficient test for network enrichment analysis that aims to overcome some limitations of existing resampling-based tests. The method is implemented in the R package neat, which can be freely downloaded from CRAN ( https://cran.r-project.org/package=neat ).

  11. Reaction network analysis in biochemical signaling pathways

    OpenAIRE

    Martinez-Forero, I. (Iván); Pelaez, A. (Antonio); Villoslada, P. (Pablo)

    2010-01-01

    The aim of this thesis is to improve the understanding of signaling pathways through a theoretical study of chemical reaction networks. The equilibirum solution to the equations derived from chemical networks will be analytically resolved using tools from algebraic geometry. The chapters are organized as follows: 1. An introduction to chemical dynamics in biological systems with a special emphasis on steady state analysis 2. Complete description of the chemical reaction network theor...

  12. Sensitivity Analysis of Centralized Dynamic Cell Selection

    DEFF Research Database (Denmark)

    Lopez, Victor Fernandez; Alvarez, Beatriz Soret; Pedersen, Klaus I.

    2016-01-01

    Centralized architectures with fronthauls can be used to deal with some of the problems inherently associated with dense small cell deployments. This study examines a joint cell assignment and scheduling solution for the downlink to increase the users’ data rates, based on cell switching and a su...... with two different traffic models, and it is not necessary to be able to connect to a large number of cells in order to reap most of the benefits of the centralized dynamic cell selection....

  13. Automatic analysis of attack data from distributed honeypot network

    Science.gov (United States)

    Safarik, Jakub; Voznak, MIroslav; Rezac, Filip; Partila, Pavol; Tomala, Karel

    2013-05-01

    There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.

  14. 3rd International Conference on Network Analysis

    CERN Document Server

    Kalyagin, Valery; Pardalos, Panos

    2014-01-01

    This volume compiles the major results of conference participants from the "Third International Conference in Network Analysis" held at the Higher School of Economics, Nizhny Novgorod in May 2013, with the aim to initiate further joint research among different groups. The contributions in this book cover a broad range of topics relevant to the theory and practice of network analysis, including the reliability of complex networks, software, theory, methodology, and applications.  Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network has brought together researchers, practitioners from numerous fields such as operations research, computer science, transportation, energy, biomedicine, computational neuroscience and social sciences. In addition, new approaches and computer environments such as parallel computing, grid computing, cloud computing, and quantum computing have helped to solve large scale...

  15. Social network analysis in medical education.

    Science.gov (United States)

    Isba, Rachel; Woolf, Katherine; Hanneman, Robert

    2017-01-01

    Humans are fundamentally social beings. The social systems within which we live our lives (families, schools, workplaces, professions, friendship groups) have a significant influence on our health, success and well-being. These groups can be characterised as networks and analysed using social network analysis. Social network analysis is a mainly quantitative method for analysing how relationships between individuals form and affect those individuals, but also how individual relationships build up into wider social structures that influence outcomes at a group level. Recent increases in computational power have increased the accessibility of social network analysis methods for application to medical education research. Social network analysis has been used to explore team-working, social influences on attitudes and behaviours, the influence of social position on individual success, and the relationship between social cohesion and power. This makes social network analysis theories and methods relevant to understanding the social processes underlying academic performance, workplace learning and policy-making and implementation in medical education contexts. Social network analysis is underused in medical education, yet it is a method that could yield significant insights that would improve experiences and outcomes for medical trainees and educators, and ultimately for patients. © 2016 John Wiley & Sons Ltd and The Association for the Study of Medical Education.

  16. Network analysis of Chinese provincial economies

    Science.gov (United States)

    Sun, Xiaoqi; An, Haizhong; Liu, Xiaojia

    2018-02-01

    Global economic system is a huge network formed by national subnetworks that contains the provincial networks. As the second largest world economy, China has "too big to fail" impact on the interconnected global economy. Detecting the critical sectors and vital linkages inside Chinese economic network is meaningful for understanding the origin of this Chinese impact. Different from tradition network research at national level, this paper focuses on the provincial networks and inter-provincial network. Using Chinese inter-regional input-output table to construct 30 provincial input-output networks and one inter-provincial input-output network, we identify central sectors and vital linkages, as well as analyze economic structure similarity. Results show that (1) Communication Devices sector in Guangdong and that in Jiangsu, Transportation and Storage sector in Shanghai play critical roles in Chinese economy. (2) Advanced manufactures and services industry occupy the central positions in eastern provincial economies, while Construction sector, Heavy industry, and Wholesale and Retail Trades sector are influential in middle and western provinces. (3) The critical monetary flow paths in Chinese economy are Communication Devices sector to Communication Devices sector in Guangdong, Metals Mining sector to Iron and Steel Smelting sector in Henan, Communication Devices sector to Communication Devices sector in Jiangsu, as well as Petroleum Mining sector in Heilongjiang to Petroleum Processing sector in Liaoning. (4) Collective influence results suggest that Finance sector, Transportation and Storage sector, Production of Electricity and Heat sector, and Rubber and Plastics sector in Hainan are strategic influencers, despite being weakly connected. These sectors and input-output relations are worthy of close attention for monitoring Chinese economy.

  17. Analysis of complex networks using aggressive abstraction.

    Energy Technology Data Exchange (ETDEWEB)

    Colbaugh, Richard; Glass, Kristin.; Willard, Gerald

    2008-10-01

    This paper presents a new methodology for analyzing complex networks in which the network of interest is first abstracted to a much simpler (but equivalent) representation, the required analysis is performed using the abstraction, and analytic conclusions are then mapped back to the original network and interpreted there. We begin by identifying a broad and important class of complex networks which admit abstractions that are simultaneously dramatically simplifying and property preserving we call these aggressive abstractions -- and which can therefore be analyzed using the proposed approach. We then introduce and develop two forms of aggressive abstraction: 1.) finite state abstraction, in which dynamical networks with uncountable state spaces are modeled using finite state systems, and 2.) onedimensional abstraction, whereby high dimensional network dynamics are captured in a meaningful way using a single scalar variable. In each case, the property preserving nature of the abstraction process is rigorously established and efficient algorithms are presented for computing the abstraction. The considerable potential of the proposed approach to complex networks analysis is illustrated through case studies involving vulnerability analysis of technological networks and predictive analysis for social processes.

  18. Social Network Analysis and Critical Realism

    DEFF Research Database (Denmark)

    Buch-Hansen, Hubert

    2014-01-01

    Social network analysis ( SNA) is an increasingly popular approach that provides researchers with highly developed tools to map and analyze complexes of social relations. Although a number of network scholars have explicated the assumptions that underpin SNA, the approach has yet to be discussed ...

  19. A Network Centrality Method for the Rating Problem

    Science.gov (United States)

    2015-01-01

    We propose a new method for aggregating the information of multiple users rating multiple items. Our approach is based on the network relations induced between items by the rating activity of the users. Our method correlates better than the simple average with respect to the original rankings of the users, and besides, it is computationally more efficient than other methods proposed in the literature. Moreover, our method is able to discount the information that would be obtained adding to the system additional users with a systematically biased rating activity. PMID:25830502

  20. Spectrum-Based and Collaborative Network Topology Analysis and Visualization

    Science.gov (United States)

    Hu, Xianlin

    2013-01-01

    Networks are of significant importance in many application domains, such as World Wide Web and social networks, which often embed rich topological information. Since network topology captures the organization of network nodes and links, studying network topology is very important to network analysis. In this dissertation, we study networks by…

  1. Extending Stochastic Network Calculus to Loss Analysis

    Directory of Open Access Journals (Sweden)

    Chao Luo

    2013-01-01

    Full Text Available Loss is an important parameter of Quality of Service (QoS. Though stochastic network calculus is a very useful tool for performance evaluation of computer networks, existing studies on stochastic service guarantees mainly focused on the delay and backlog. Some efforts have been made to analyse loss by deterministic network calculus, but there are few results to extend stochastic network calculus for loss analysis. In this paper, we introduce a new parameter named loss factor into stochastic network calculus and then derive the loss bound through the existing arrival curve and service curve via this parameter. We then prove that our result is suitable for the networks with multiple input flows. Simulations show the impact of buffer size, arrival traffic, and service on the loss factor.

  2. Social network influences on technology acceptance : A matter of tie strength, centrality and density

    NARCIS (Netherlands)

    Ten Kate, Stephan; Haverkamp, Sophie; Mahmood, Fariha; Feldberg, Frans

    2010-01-01

    This study examines social network influences on the individual technology acceptance. Since it is believed that individuals' trust, opinions and behavior are influenced by their network, an analysis of that network may help to provide some explanations on technology acceptance. However, since

  3. Constructing an Intelligent Patent Network Analysis Method

    Directory of Open Access Journals (Sweden)

    Chao-Chan Wu

    2012-11-01

    Full Text Available Patent network analysis, an advanced method of patent analysis, is a useful tool for technology management. This method visually displays all the relationships among the patents and enables the analysts to intuitively comprehend the overview of a set of patents in the field of the technology being studied. Although patent network analysis possesses relative advantages different from traditional methods of patent analysis, it is subject to several crucial limitations. To overcome the drawbacks of the current method, this study proposes a novel patent analysis method, called the intelligent patent network analysis method, to make a visual network with great precision. Based on artificial intelligence techniques, the proposed method provides an automated procedure for searching patent documents, extracting patent keywords, and determining the weight of each patent keyword in order to generate a sophisticated visualization of the patent network. This study proposes a detailed procedure for generating an intelligent patent network that is helpful for improving the efficiency and quality of patent analysis. Furthermore, patents in the field of Carbon Nanotube Backlight Unit (CNT-BLU were analyzed to verify the utility of the proposed method.

  4. Statistical Analysis of Bus Networks in India.

    Science.gov (United States)

    Chatterjee, Atanu; Manohar, Manju; Ramadurai, Gitakrishnan

    2016-01-01

    In this paper, we model the bus networks of six major Indian cities as graphs in L-space, and evaluate their various statistical properties. While airline and railway networks have been extensively studied, a comprehensive study on the structure and growth of bus networks is lacking. In India, where bus transport plays an important role in day-to-day commutation, it is of significant interest to analyze its topological structure and answer basic questions on its evolution, growth, robustness and resiliency. Although the common feature of small-world property is observed, our analysis reveals a wide spectrum of network topologies arising due to significant variation in the degree-distribution patterns in the networks. We also observe that these networks although, robust and resilient to random attacks are particularly degree-sensitive. Unlike real-world networks, such as Internet, WWW and airline, that are virtual, bus networks are physically constrained. Our findings therefore, throw light on the evolution of such geographically and constrained networks that will help us in designing more efficient bus networks in the future.

  5. Multiple perspective vulnerability analysis of the power network

    Science.gov (United States)

    Wang, Shuliang; Zhang, Jianhua; Duan, Na

    2018-02-01

    To understand the vulnerability of the power network from multiple perspectives, multi-angle and multi-dimensional vulnerability analysis as well as community based vulnerability analysis are proposed in this paper. Taking into account of central China power grid as an example, correlation analysis of different vulnerability models is discussed. Then, vulnerabilities produced by different vulnerability metrics under the given vulnerability models and failure scenarios are analyzed. At last, applying the community detecting approach, critical areas of central China power grid are identified, Vulnerable and robust communities on both topological and functional perspective are acquired and analyzed. The approach introduced in this paper can be used to help decision makers develop optimal protection strategies. It will be also useful to give a multiple vulnerability analysis of the other infrastructure systems.

  6. Social network analysis for program implementation.

    Science.gov (United States)

    Valente, Thomas W; Palinkas, Lawrence A; Czaja, Sara; Chu, Kar-Hai; Brown, C Hendricks

    2015-01-01

    This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach.

  7. Multi-index algorithm of identifying important nodes in complex networks based on linear discriminant analysis

    Science.gov (United States)

    Hu, Fang; Liu, Yuhua

    2015-02-01

    The evaluation of node importance has great significance to complex network, so it is important to seek and protect important nodes to ensure the security and stability of the entire network. At present, most evaluation algorithms of node importance adopt the single-index methods, which are incomplete and limited, and cannot fully reflect the complex situation of network. In this paper, after synthesizing multi-index factors of node importance, including eigenvector centrality, betweenness centrality, closeness centrality, degree centrality, mutual-information, etc., the authors are proposing a new multi-index evaluation algorithm of identifying important nodes in complex networks based on linear discriminant analysis (LDA). In order to verify the validity of this algorithm, a series of simulation experiments have been done. Through comprehensive analysis, the simulation results show that the new algorithm is more rational, effective, integral and accurate.

  8. Social network analysis of Iranian researchers on emergency medicine: a sociogram analysis.

    Science.gov (United States)

    Ghafouri, Hamed Basir; Mohammadhassanzadeh, Hafez; Shokraneh, Farhad; Vakilian, Maryam; Farahmand, Shervin

    2014-08-01

    The purpose of this study was to report interaction patterns among Iranian authors of emergency medicine using social network analysis methodology, focusing on coauthorship network. The bibliographic data of Iranian authors on the 'emergency medicine' field during the years 2001-2011 were retrieved from the Science Citation Index Expanded database. Co-occurrence matrices were made by BibExcel and were imported to Ucinet and NetDraw to delineate coauthorship network. To detect structural patterns among authors, we considered some measures of social network analysis, such as density, centralisation indices, component analysis and cut-points. Lastly, subject experts separately analysed the content of papers. Of 116 papers published, the network was composed of 10 components, with the largest component having 25 authors. Using social network analysis measures, we identified science bottlenecks in knowledge sharing, hub authors and accelerators of information flow. Topic analysis showed 'Wounds and Injuries' as the most recent theme in all components because of existence of national registry for trauma, high burden of road traffic injuries and research priority of injuries in Iran. because of Iranian low productivity in the emergency medicine field, social network analysis seems to be a proper option for bibliometrics to identify central authors and detect knowledge structure in this field. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  9. A network centrality method for the rating problem

    CERN Document Server

    Li, Yongli; Wu, Chong

    2014-01-01

    We propose a new method for aggregating the information of multiple reviewers rating multiple products. Our approach is based on the network relations induced between products by the rating activity of the reviewers. We show that our method is algorithmically implementable even for large numbers of both products and consumers, as is the case for many online sites. Moreover, comparing it with the simple average, which is mostly used in practice, and with other methods previously proposed in the literature, it performs very well under various dimension, proving itself to be an optimal trade--off between computational efficiency, accordance with the reviewers original orderings, and robustness with respect to the inclusion of systematically biased reports.

  10. ChIP-seq analysis of genomic binding regions of five major transcription factors highlights a central role for ZIC2 in the mouse epiblast stem cell gene regulatory network

    Science.gov (United States)

    Matsuda, Kazunari; Oki, Shinya; Iida, Hideaki; Andrabi, Munazah; Yamaguchi, Katsushi

    2017-01-01

    To obtain insight into the transcription factor (TF)-dependent regulation of epiblast stem cells (EpiSCs), we performed ChIP-seq analysis of the genomic binding regions of five major TFs. Analysis of in vivo biotinylated ZIC2, OTX2, SOX2, POU5F1 and POU3F1 binding in EpiSCs identified several new features. (1) Megabase-scale genomic domains rich in ZIC2 peaks and genes alternate with those rich in POU3F1 but sparse in genes, reflecting the clustering of regulatory regions that act at short and long-range, which involve binding of ZIC2 and POU3F1, respectively. (2) The enhancers bound by ZIC2 and OTX2 prominently regulate TF genes in EpiSCs. (3) The binding sites for SOX2 and POU5F1 in mouse embryonic stem cells (ESCs) and EpiSCs are divergent, reflecting the shift in the major acting TFs from SOX2/POU5F1 in ESCs to OTX2/ZIC2 in EpiSCs. (4) This shift in the major acting TFs appears to be primed by binding of ZIC2 in ESCs at relevant genomic positions that later function as enhancers following the disengagement of SOX2/POU5F1 from major regulatory functions and subsequent binding by OTX2. These new insights into EpiSC gene regulatory networks gained from this study are highly relevant to early stage embryogenesis. PMID:28455373

  11. Impact of Nodal Centrality Measures to Robustness in Software-Defined Networking

    Directory of Open Access Journals (Sweden)

    Tomas Hegr

    2014-01-01

    Full Text Available The paper deals with the network robustness from the perspective of nodal centrality measures and its applicability in Software-Defined Networking (SDN. Traditional graph characteristics have been evolving during the last century, and numerous of less-conventional metrics was introduced trying to bring a new view to some particular graph attributes. New control technologies can finally utilize these metrics but simultaneously show new challenges. SDN brings the fine-grained and nearly online view of the underlying network state which allows to implement an advanced routing and forwarding. In such situation, sophisticated algorithms can be applied utilizing pre-computed network measures. Since in recent version of SDN protocol OpenFlow (OF has been revived an idea of the fast link failover, the authors in this paper introduce a novel metric, Quality of Alternative Paths centrality (QAP. The QAP value quantifies node surroundings and can be with an advantage utilized in algorithms to indicate more robust paths. The centrality is evaluated using the node-failure simulation at different network topologies in combination with the Quality of Backup centrality measure.

  12. Central European MetEor NeTwork: Current status and future activities

    Science.gov (United States)

    Srba, J.; Koukal, J.; Ferus, M.; Lenža, L.; Gorková, S.; Civiš, S.; Simon, J.; Csorgei, T.; Jedlièka, M.; Korec, M.; Kaniansky, S.; Polák, J.; Spurný, M.; Brázdil, T.; Mäsiar, J.; Zima, M.; Delinèák, P.; Popek, M.; Bahýl, V.; Piffl, R.; Èechmánek, M.

    2016-06-01

    The Central European video Meteor Network (CEMeNt) established in 2010 is a platform for cross-border cooperation in the field of video meteor observations between Czech Republic and Slovakia. During five years of operation the CEMeNt network went through an extensive development. In total, 37 video systems were working on 20 permanent stations located in Czech Republic and Slovakia during 2015. In this paper we summarize CEMeNt current status and introduce some future activities.

  13. PLUS highway network analysis: Case of in-coming traffic burden in 2013

    Science.gov (United States)

    Asrah, Norhaidah Mohd; Djauhari, Maman Abdurachman; Mohamad, Ismail

    2017-05-01

    PLUS highway is the largest concessionary in Malaysia. The study on PLUS highway development, in order to overcome the demand for efficient road transportation, is crucial. If the highways have better interconnected network, it will help the economic activities such as trade to increase. If economic activities are increasing, the benefit will come to the people and state. In its turn, it will help the leaders to plan and conduct national development program. In this paper, network analysis approach will be used to study the in-coming traffic burden during the year of 2013. The highway network linking all the toll plazas is a dynamic network. The objective of this study is to learn and understand about highway network in terms of the in-coming traffic burden entering to each toll plazas along PLUS highway. For this purpose, the filtered network topology based on the forest of all possible minimum spanning trees is used. The in-coming traffic burden of a city is represented by the number of cars passing through the corresponding toll plaza. To interpret the filtered network, centrality measures such as degree centrality, betweenness centrality, closeness centrality, eigenvector centrality are used. An overall centrality will be proposed if those four measures are assumed to have the same role. Based on the results, some suggestions and recommendations for PLUS highway network development will be delivered to PLUS highway management.

  14. Stream-gage locations where streamflow gains/losses were quantified along the Central Valley surface-water network

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This digital dataset contains the name and location for the diversions from the surface-water network for the Central Valley Hydrologic Model (CVHM). The Central...

  15. The Central and Eastern European Earthquake Research Network - CE3RN

    Science.gov (United States)

    Bragato, Pier Luigi; Costa, Giovanni; Gallo, Antonella; Gosar, Andrej; Horn, Nikolaus; Lenhardt, Wolfgang; Mucciarelli, Marco; Pesaresi, Damiano; Steiner, Rudolf; Suhadolc, Peter; Tiberi, Lara; Živčić, Mladen; Zoppé, Giuliana

    2014-05-01

    The region of the Central and Eastern Europe is an area characterised by a relatively high seismicity. The active seismogenic structures and the related potentially destructive events are located in the proximity of the political boundaries between several countries existing in the area. An example is the seismic region between the NE Italy (FVG, Trentino-Alto Adige and Veneto), Austria (Tyrol, Carinthia) and Slovenia. So when a destructive earthquake occurs in the area, all the three countries are involved. In the year 2001 the Agencija Republike Slovenije za Okolje (ARSO) in Slovenia, the Department of Mathematics and Geoscience of the University of Trieste (DMG), the OGS (Istituto Nazionale di Oceanografia e di Geofisica Sperimentale) in Italy and the Zentralanstalt für Meteorologie und Geodynamik (ZAMG) in Austria signed an agreement for the real-time seismological data exchange in the Southeastern Alps region. Soon after the Interreg IIIa Italia-Austria projects "Trans-National Seismological Networks in the South-Eastern Alps" and "FASTLINK" started. The main goal of these projects was the creation of a transfrontier network for the common seismic monitoring of the region for scientific and civil defense purposes. During these years the high quality data recorded by the transfrontier network has been used, by the involved institutions, for their scientific research, for institutional activities and for the civil defense services. Several common international projects have been realized with success. The instrumentation has been continuously upgraded, the installations quality improved as well as the data transmission efficiency. In the 2013 ARSO, DMG, OGS and ZAMG decided to name the cooperative network "Central and Eastern European Earthquake Research Network - CE3RN". The national/regional seismic networks actually involved in the CE3RN network are: • Austrian national BB network (ZAMG - OE) • Friuli Veneto SP network (OGS - FV) • Friuli VG

  16. 1st International Conference on Network Analysis

    CERN Document Server

    Kalyagin, Valery; Pardalos, Panos

    2013-01-01

    This volume contains a selection of contributions from the "First International Conference in Network Analysis," held at the University of Florida, Gainesville, on December 14-16, 2011. The remarkable diversity of fields that take advantage of Network Analysis makes the endeavor of gathering up-to-date material in a single compilation a useful, yet very difficult, task. The purpose of this volume is to overcome this difficulty by collecting the major results found by the participants and combining them in one easily accessible compilation. Network analysis has become a major research topic over the last several years. The broad range of applications that can be described and analyzed by means of a network is bringing together researchers, practitioners and other scientific communities from numerous fields such as Operations Research, Computer Science, Transportation, Energy, Social Sciences, and more. The contributions not only come from different fields, but also cover a broad range of topics relevant to the...

  17. Wiring of divergent networks in the central auditory system

    Directory of Open Access Journals (Sweden)

    Charles C. Lee

    2011-07-01

    Full Text Available Divergent axonal projections are found throughout the central auditory system. Here, we evaluate these branched projections in terms of their types, distribution, and putative physiological roles. In general, three patterns of axon collateralization are found: intricate local branching, long-distance collaterals, and branched axons involved in feedback-control loops. Local collaterals in the auditory cortex may be involved in local processing and modulation of neuronal firing, while long-range collaterals are optimized for wide-dissemination of information. Rarely do axons branch to both ascending and descending targets. Branched projections to two or more widely separated nuclei or areas are numerically sparse but widespread. Finally, branching to contralateral targets is evident at multiple levels of the auditory pathway and may enhance binaural computations for sound localization. These patterns of axonal branching are comparable to those observed in other modalities. We conclude that the operations served by branched axons are area- and nucleus-specific and may complement the divergent unbranched projections of local neuronal populations.

  18. Centralized cooperative spectrum sensing for ad-hoc disaster relief network clusters

    DEFF Research Database (Denmark)

    Pratas, Nuno; Marchetti, Nicola; Prasad, Neeli R.

    2010-01-01

    Disaster relief networks have to be highly adaptable and resilient. Cognitive radio enhanced ad-hoc architecture have been put forward as a candidate to enable such networks. Spectrum sensing is the cornerstone of the cognitive radio paradigm, and it has been the target of intensive research....... The main common conclusion was that the achievable spectrum sensing accuracy can be greatly enhanced through the use of cooperative sensing schemes. When considering applying Cognitive Radio to ad-hoc disaster relief networks, spectrum sensing cooperative schemes are paramount. A centralized cluster...

  19. Network Anomaly Detection Based on Wavelet Analysis

    Directory of Open Access Journals (Sweden)

    Ali A. Ghorbani

    2008-11-01

    Full Text Available Signal processing techniques have been applied recently for analyzing and detecting network anomalies due to their potential to find novel or unknown intrusions. In this paper, we propose a new network signal modelling technique for detecting network anomalies, combining the wavelet approximation and system identification theory. In order to characterize network traffic behaviors, we present fifteen features and use them as the input signals in our system. We then evaluate our approach with the 1999 DARPA intrusion detection dataset and conduct a comprehensive analysis of the intrusions in the dataset. Evaluation results show that the approach achieves high-detection rates in terms of both attack instances and attack types. Furthermore, we conduct a full day's evaluation in a real large-scale WiFi ISP network where five attack types are successfully detected from over 30 millions flows.

  20. Combining network analysis with Cognitive Work Analysis: insights into social organisational and cooperation analysis.

    Science.gov (United States)

    Houghton, Robert J; Baber, Chris; Stanton, Neville A; Jenkins, Daniel P; Revell, Kirsten

    2015-01-01

    Cognitive Work Analysis (CWA) allows complex, sociotechnical systems to be explored in terms of their potential configurations. However, CWA does not explicitly analyse the manner in which person-to-person communication is performed in these configurations. Consequently, the combination of CWA with Social Network Analysis provides a means by which CWA output can be analysed to consider communication structure. The approach is illustrated through a case study of a military planning team. The case study shows how actor-to-actor and actor-to-function mapping can be analysed, in terms of centrality, to produce metrics of system structure under different operating conditions. In this paper, a technique for building social network diagrams from CWA is demonstrated.The approach allows analysts to appreciate the potential impact of organisational structure on a command system.

  1. Trimming of mammalian transcriptional networks using network component analysis

    Directory of Open Access Journals (Sweden)

    Liao James C

    2010-10-01

    Full Text Available Abstract Background Network Component Analysis (NCA has been used to deduce the activities of transcription factors (TFs from gene expression data and the TF-gene binding relationship. However, the TF-gene interaction varies in different environmental conditions and tissues, but such information is rarely available and cannot be predicted simply by motif analysis. Thus, it is beneficial to identify key TF-gene interactions under the experimental condition based on transcriptome data. Such information would be useful in identifying key regulatory pathways and gene markers of TFs in further studies. Results We developed an algorithm to trim network connectivity such that the important regulatory interactions between the TFs and the genes were retained and the regulatory signals were deduced. Theoretical studies demonstrated that the regulatory signals were accurately reconstructed even in the case where only three independent transcriptome datasets were available. At least 80% of the main target genes were correctly predicted in the extreme condition of high noise level and small number of datasets. Our algorithm was tested with transcriptome data taken from mice under rapamycin treatment. The initial network topology from the literature contains 70 TFs, 778 genes, and 1423 edges between the TFs and genes. Our method retained 1074 edges (i.e. 75% of the original edge number and identified 17 TFs as being significantly perturbed under the experimental condition. Twelve of these TFs are involved in MAPK signaling or myeloid leukemia pathways defined in the KEGG database, or are known to physically interact with each other. Additionally, four of these TFs, which are Hif1a, Cebpb, Nfkb1, and Atf1, are known targets of rapamycin. Furthermore, the trimmed network was able to predict Eno1 as an important target of Hif1a; this key interaction could not be detected without trimming the regulatory network. Conclusions The advantage of our new algorithm

  2. A P2P Query Algorithm for Opportunistic Networks Utilizing betweenness Centrality Forwarding

    Directory of Open Access Journals (Sweden)

    Jianwei Niu

    2013-01-01

    Full Text Available With the proliferation of high-end mobile devices that feature wireless interfaces, many promising applications are enabled in opportunistic networks. In contrary to traditional networks, opportunistic networks utilize the mobility of nodes to relay messages in a store-carry-forward paradigm. Thus, the relay process in opportunistic networks faces several practical challenges in terms of delay and delivery rate. In this paper, we propose a novel P2P Query algorithm, namely Betweenness Centrality Forwarding (PQBCF, for opportunistic networking. PQBCF adopts a forwarding metric called Betweenness Centrality (BC, which is borrowed from social network, to quantify the active degree of nodes in the networks. In PQBCF, nodes with a higher BC are preferable to serve as relays, leading to higher query success rate and lower query delay. A comparison with the state-of-the-art algorithms reveals that PQBCF can provide better performance on both the query success Ratio and query delay, and approaches the performance of Epidemic Routing (ER with much less resource consumption.

  3. Increased default-mode network centrality in cognitively impaired multiple sclerosis patients.

    Science.gov (United States)

    Eijlers, Anand J C; Meijer, Kim A; Wassenaar, Thomas M; Steenwijk, Martijn D; Uitdehaag, Bernard M J; Barkhof, Frederik; Wink, Alle M; Geurts, Jeroen J G; Schoonheim, Menno M

    2017-03-07

    To investigate how changes in functional network hierarchy determine cognitive impairment in multiple sclerosis (MS). A cohort consisting of 332 patients with MS (age 48.1 ± 11.0 years, symptom duration 14.6 ± 8.4 years) and 96 healthy controls (HCs; age 45.9 ± 10.4 years) underwent structural MRI, fMRI, and extensive neuropsychological testing. Patients were divided into 3 groups: cognitively impaired (CI; n = 87), mildly cognitively impaired (MCI; n = 65), and cognitively preserved (CP; n = 180). The functional importance of brain regions was quantified with degree centrality, the average strength of the functional connections of a brain region with the rest of the brain, and eigenvector centrality, which adds to this concept by adding additional weight to connections with brain hubs because these are known to be especially important. Centrality values were calculated for each gray matter voxel based on resting-state fMRI data, registered to standard space. Group differences were assessed with a cluster-wise permutation-based method corrected for age, sex, and education. CI patients demonstrated widespread centrality increases compared to both HCs and CP patients, mainly in regions making up the default-mode network. Centrality decreases were similar in all patient groups compared to HCs, mainly in occipital and sensorimotor areas. Results were robust across centrality measures. Patients with MS with cognitive impairment show hallmark alterations in functional network hierarchy with increased relative importance (centrality) of the default-mode network. © 2017 American Academy of Neurology.

  4. ChIP-seq analysis of genomic binding regions of five major transcription factors highlights a central role for ZIC2 in the mouse epiblast stem cell gene regulatory network.

    Science.gov (United States)

    Matsuda, Kazunari; Mikami, Tomoyuki; Oki, Shinya; Iida, Hideaki; Andrabi, Munazah; Boss, Jeremy M; Yamaguchi, Katsushi; Shigenobu, Shuji; Kondoh, Hisato

    2017-06-01

    To obtain insight into the transcription factor (TF)-dependent regulation of epiblast stem cells (EpiSCs), we performed ChIP-seq analysis of the genomic binding regions of five major TFs. Analysis of in vivo biotinylated ZIC2, OTX2, SOX2, POU5F1 and POU3F1 binding in EpiSCs identified several new features. (1) Megabase-scale genomic domains rich in ZIC2 peaks and genes alternate with those rich in POU3F1 but sparse in genes, reflecting the clustering of regulatory regions that act at short and long-range, which involve binding of ZIC2 and POU3F1, respectively. (2) The enhancers bound by ZIC2 and OTX2 prominently regulate TF genes in EpiSCs. (3) The binding sites for SOX2 and POU5F1 in mouse embryonic stem cells (ESCs) and EpiSCs are divergent, reflecting the shift in the major acting TFs from SOX2/POU5F1 in ESCs to OTX2/ZIC2 in EpiSCs. (4) This shift in the major acting TFs appears to be primed by binding of ZIC2 in ESCs at relevant genomic positions that later function as enhancers following the disengagement of SOX2/POU5F1 from major regulatory functions and subsequent binding by OTX2. These new insights into EpiSC gene regulatory networks gained from this study are highly relevant to early stage embryogenesis. © 2017. Published by The Company of Biologists Ltd.

  5. Network graph analysis of category fluency testing.

    Science.gov (United States)

    Lerner, Alan J; Ogrocki, Paula K; Thomas, Peter J

    2009-03-01

    Category fluency is impaired early in Alzheimer disease (AD). Graph theory is a technique to analyze complex relationships in networks. Features of interest in network analysis include the number of nodes and edges, and variables related to their interconnectedness. Other properties important in network analysis are "small world properties" and "scale-free" properties. The small world property (popularized as the so-called "6 degrees of separation") arises when the majority of connections are local, but a number of connections are to distant nodes. Scale-free networks are characterized by the presence of a few nodes with many connections, and many more nodes with fewer connections. To determine if category fluency data can be analyzed using graph theory. To compare normal elderly, mild cognitive impairment (MCI) and AD network graphs, and characterize changes seen with increasing cognitive impairment. Category fluency results ("animals" recorded over 60 s) from normals (n=38), MCI (n=33), and AD (n=40) completing uniform data set evaluations were converted to network graphs of all unique cooccurring neighbors, and compared for network variables. For Normal, MCI and AD, mean clustering coefficients were 0.21, 0.22, 0.30; characteristic path lengths were 3.27, 3.17, and 2.65; small world properties decreased with increasing cognitive impairment, and all graphs showed scale-free properties. Rank correlations of the 25 commonest items ranged from 0.75 to 0.83. Filtering of low-degree nodes in normal and MCI graphs resulted in properties similar to the AD network graph. Network graph analysis is a promising technique for analyzing changes in category fluency. Our technique results in nonrandom graphs consistent with well-characterized properties for these types of graphs.

  6. Performance Analysis of 3G Communication Network

    Directory of Open Access Journals (Sweden)

    Toni Anwar

    2013-09-01

    Full Text Available In this project, third generation (3G technologies research had been carried out to design and optimization conditions for 3G network. The 3G wireless mobile communication networks are growing at an ever faster rate, and this is likely to continue in the foreseeable future. Some services such as e-mail, web browsing etc allow the transition of the network from circuit switched to packet switched operation, resulting in increased overall network performance. Higher reliability, better coverage and services, higher capacity, mobility management, and wireless multimedia are all parts of the network performance. Throughput and spectral efficiency are fundamental parameters in capacity planning for 3G cellular network deployments. This project investigates also the downlink (DL and uplink (UL throughput and spectral efficiency performance of the standard Universal Mobile Telecommunications system (UMTS system for different scenarios of user and different technologies. Power consumption comparison for different mobile technology is also discussed. The analysis can significantly help system engineers to obtain crucial performance characteristics of 3G network. At the end of the paper, coverage area of 3G from one of the mobile network in Malaysia is presented.

  7. Medical image analysis with artificial neural networks.

    Science.gov (United States)

    Jiang, J; Trundle, P; Ren, J

    2010-12-01

    Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

  8. Kinetic analysis of complex metabolic networks

    Energy Technology Data Exchange (ETDEWEB)

    Stephanopoulos, G. [MIT, Cambridge, MA (United States)

    1996-12-31

    A new methodology is presented for the analysis of complex metabolic networks with the goal of metabolite overproduction. The objective is to locate a small number of reaction steps in a network that have maximum impact on network flux amplification and whose rate can also be increased without functional network derangement. This method extends the concepts of Metabolic Control Analysis to groups of reactions and offers the means for calculating group control coefficients as measures of the control exercised by groups of reactions on the overall network fluxes and intracellular metabolite pools. It is further demonstrated that the optimal strategy for the effective increase of network fluxes, while maintaining an uninterrupted supply of intermediate metabolites, is through the coordinated amplification of multiple (as opposed to a single) reaction steps. Satisfying this requirement invokes the concept of the concentration control to coefficient, which emerges as a critical parameter in the identification of feasible enzymatic modifications with maximal impact on the network flux. A case study of aromatic aminoacid production is provided to illustrate these concepts.

  9. A centralized feedback control model for resource management in wireless networks

    NARCIS (Netherlands)

    Yang, Y.; Haverkort, Boudewijn R.H.M.; Heijenk, Geert

    In a wireless environment, guaranteeing QoS is challenging because applications at multiple devices share the same limited radio bandwidth. In this paper we introduce and study a resource management model for centralized wireless networks, using feedback control theory. Before applying in practice,

  10. Network Centrality in a Virtual Brand Community: Exploring an Antecedent and Some Consequences

    National Research Council Canada - National Science Library

    Yan, Bing-Sheng; Jing, Feng-Jie; Yang, Yan; Wang, Xing-Dong

    2014-01-01

    ... of the findings are discussed. Keywords: citizenship behavior, network centrality, psychological ownership, social enhancement motivation, virtual brand community. In recent years, managers of enterprises have increasingly focused on the use of a brand community in the establishment of long-term relationships with customers. An important reason for thi...

  11. Structural covariance network centrality in maltreated youth with posttraumatic stress disorder.

    Science.gov (United States)

    Sun, Delin; Peverill, Matthew R; Swanson, Chelsea S; McLaughlin, Katie A; Morey, Rajendra A

    2017-12-25

    Childhood maltreatment is associated with posttraumatic stress disorder (PTSD) and elevated rates of adolescent and adult psychopathology including major depression, bipolar disorder, substance use disorders, and other medical comorbidities. Gray matter volume changes have been found in maltreated youth with (versus without) PTSD. However, little is known about the alterations of brain structural covariance network topology derived from cortical thickness in maltreated youth with PTSD. High-resolution T1-weighted magnetic resonance imaging scans were from demographically matched maltreated youth with PTSD (N = 24), without PTSD (N = 64), and non-maltreated healthy controls (n = 67). Cortical thickness data from 148 cortical regions was entered into interregional partial correlation analyses across participants. The supra-threshold correlations constituted connections in a structural brain network derived from four types of centrality measures (degree, betweenness, closeness, and eigenvector) estimated network topology and the importance of nodes. Between-group differences were determined by permutation testing. Maltreated youth with PTSD exhibited larger centrality in left anterior cingulate cortex than the other two groups, suggesting cortical network topology specific to maltreated youth with PTSD. Moreover, maltreated youth with versus without PTSD showed smaller centrality in right orbitofrontal cortex, suggesting that this may represent a vulnerability factor to PTSD following maltreatment. Longitudinal follow-up of the present results will help characterize the role that altered centrality plays in vulnerability and resilience to PTSD following childhood maltreatment. Copyright © 2017. Published by Elsevier Ltd.

  12. Polycentric city networks in Central-Eastern Europe: existing concepts and empirical findings

    NARCIS (Netherlands)

    Kramar, H.; Kadi, J.

    2013-01-01

    The concept of polycentricity has gained significance in discussions on spatial development in Europe in recent years. This paper presents new evidence on polycentric city networks in Central-Eastern Europe based on selected results of the ESPON project POLYCE (Metropolisation and Polycentric

  13. Exploration Knowledge Sharing Networks Using Social Network Analysis Methods

    Directory of Open Access Journals (Sweden)

    Győző Attila Szilágyi

    2017-10-01

    Full Text Available Knowledge sharing within organization is one of the key factor for success. The organization, where knowledge sharing takes place faster and more efficiently, is able to adapt to changes in the market environment more successfully, and as a result, it may obtain a competitive advantage. Knowledge sharing in an organization is carried out through formal and informal human communication contacts during work. This forms a multi-level complex network whose quantitative and topological characteristics largely determine how quickly and to what extent the knowledge travels within organization. The study presents how different networks of knowledge sharing in the organization can be explored by means of network analysis methods through a case study, and which role play the properties of these networks in fast and sufficient spread of knowledge in organizations. The study also demonstrates the practical applications of our research results. Namely, on the basis of knowledge sharing educational strategies can be developed in an organization, and further, competitiveness of an organization may increase due to those strategies’ application.

  14. Using Granular-Evidence-Based Adaptive Networks for Sensitivity Analysis

    OpenAIRE

    Vališevskis, A.

    2002-01-01

    This paper considers the possibility of using adaptive networks for sensitivity analysis. Adaptive network that processes fuzzy granules is described. The adaptive network training algorithm can be used for sensitivity analysis of decision making models. Furthermore, a case study concerning sensitivity analysis is described, which shows in what way the adaptive network can be used for sensitivity analysis.

  15. What do central counterparties default funds really cover? A network-based stress test answer

    CERN Document Server

    Poce, Giulia; Gabrielli, Andrea; Zaccaria, Andrea; Baldacci, Giuditta; Polito, Marco; Rizzo, Mariangela; Sabatini, Silvia

    2016-01-01

    In the last years, increasing efforts have been put into the development of effective stress tests to quantify the resilience of financial institutions. Here we propose a stress test methodology for central counterparties based on a network characterization of clearing members, whose links correspond to direct credits and debits. This network constitutes the ground for the propagation of financial distress: equity losses caused by an initial shock with both exogenous and endogenous components reverberate within the network and are amplified through credit and liquidity contagion channels. At the end of the dynamics, we determine the vulnerability of each clearing member, which represents its potential equity loss. We apply the proposed framework to the Fixed Income asset class of CC&G, the central counterparty operating in Italy whose main cleared securities are Italian Government Bonds. We consider two different scenarios: a distributed, plausible initial shock, as well as a shock corresponding to the co...

  16. Use of Network Centrality Measures to Explain Individual Levels of Herbal Remedy Cultural Competence among the Yucatec Maya in Tabi, Mexico.

    Science.gov (United States)

    Hopkins, Allison

    2011-08-01

    Common herbal remedy knowledge varies and is transmitted among individuals who are connected through a social network. Thus, social relationships have the potential to account for some of the variation in knowledge. Cultural consensus analysis (CCA) and social network analysis (SNA) were used together to study the association between intracultural variation in botanical remedy knowledge and social relationships in Tabi, Yucatan, Mexico. CCA, a theory of culture as agreement, was used to assess the competence of individuals in a domain of herbal remedies by measuring individual competence scores within that domain. There was a weak but positive association between these competence scores and network centrality scores. This association disappeared when age was included in the model. People in Tabi, who have higher competence in herbal remedies tend to be older and more centrally located in the herbal remedy inquiry network. The larger implication of the application of CCA and SNA for understanding the acquisition and transmission of cultural knowledge is also explored.

  17. Development of the default mode and central executive networks across early adolescence: a longitudinal study.

    Science.gov (United States)

    Sherman, Lauren E; Rudie, Jeffrey D; Pfeifer, Jennifer H; Masten, Carrie L; McNealy, Kristin; Dapretto, Mirella

    2014-10-01

    The mature brain is organized into distinct neural networks defined by regions demonstrating correlated activity during task performance as well as rest. While research has begun to examine differences in these networks between children and adults, little is known about developmental changes during early adolescence. Using functional magnetic resonance imaging (fMRI), we examined the Default Mode Network (DMN) and the Central Executive Network (CEN) at ages 10 and 13 in a longitudinal sample of 45 participants. In the DMN, participants showed increasing integration (i.e., stronger within-network correlations) between the posterior cingulate cortex (PCC) and the medial prefrontal cortex. During this time frame participants also showed increased segregation (i.e., weaker between-network correlations) between the PCC and the CEN. Similarly, from age 10 to 13, participants showed increased connectivity between the dorsolateral prefrontal cortex and other CEN nodes, as well as increasing DMN segregation. IQ was significantly positively related to CEN integration at age 10, and between-network segregation at both ages. These findings highlight early adolescence as a period of significant maturation for the brain's functional architecture and demonstrate the utility of longitudinal designs to investigate neural network development. Copyright © 2014 The Authors. Published by Elsevier Ltd.. All rights reserved.

  18. Social network analysis of international scientific collaboration on psychiatry research.

    Science.gov (United States)

    Wu, Ying; Duan, Zhiguang

    2015-01-01

    Mental disorder is harmful to human health, effects social life seriously and still brings a heavy burden for countries all over the world. Scientific collaboration has become the indispensable choice for progress in the field of biomedicine. However, there have been few scientific publications on scientific collaboration in psychiatry research so far. The aim of this study was to measure the activities of scientific collaboration in psychiatry research at the level of authors, institutions and countries. We retrieved 36557 papers about psychiatry from Science Ciation Index Expanded (SCI-Expanded) in web of science. Additionally, some methods such as social network analysis (SNA), K-plex analysis and Core-Periphery were used in this study. Collaboration has been increasing at the level of authors, institutions and countries in psychiatry in the last ten years. We selected the top 100 prolific authors, institutions and 30 countries to construct collaborative map respectively. Freedman, R and Seidman, LJ were the central authors, Harvard university was the central institution and the USA was the central country of the whole network. Notably, the rate of economic development of countries affected collaborative behavior. The results show that we should encourage multiple collaboration types in psychiatry research as they not only help researchers to master the current research hotspots but also provide scientific basis for clinical research on psychiatry and suggest policies to promote the development of this area.

  19. Tensor Fusion Network for Multimodal Sentiment Analysis

    OpenAIRE

    Zadeh, Amir; Chen, Minghai; Poria, Soujanya; Cambria, Erik; Morency, Louis-Philippe

    2017-01-01

    Multimodal sentiment analysis is an increasingly popular research area, which extends the conventional language-based definition of sentiment analysis to a multimodal setup where other relevant modalities accompany language. In this paper, we pose the problem of multimodal sentiment analysis as modeling intra-modality and inter-modality dynamics. We introduce a novel model, termed Tensor Fusion Network, which learns both such dynamics end-to-end. The proposed approach is tailored for the vola...

  20. Central Calorimeter Support Cradle Jack Failure Analysis

    Energy Technology Data Exchange (ETDEWEB)

    Rudland, D.L.; /Fermilab

    1987-04-10

    The Central Calorimeter and its support cradle are to be supported by either hydraulic or mechanical jacks. If hydraulics are used, each support will use two hydraulically coupled jacks with two out of the four supports hydraulically coupled giving the effect of a three point support system. If mechanical jacks are used, all four points are used for support. Figure 2 shows two examples of jack placement on a 3.5 inch support plate. These two support scenarios lead to five jack failure cases to be studied. This report deals with the way in which a 0.25 inch drop (failed jack) at one support affects the stresses in the cradle. The stresses from each failure case were analyzed in two ways. First, stress factors, defined as quotients of stress intensities of the failed case with respect to the static case, were generated and then, hand calculations similar to those in Engineering Note 3740.215-EN-14 were done using the reaction forces from the failed case.

  1. Automated Analysis of Security in Networking Systems

    DEFF Research Database (Denmark)

    Buchholtz, Mikael

    2004-01-01

    It has for a long time been a challenge to built secure networking systems. One way to counter this problem is to provide developers of software applications for networking systems with easy-to-use tools that can check security properties before the applications ever reach the marked. These tools...... will both help raise the general level of awareness of the problems and prevent the most basic flaws from occurring. This thesis contributes to the development of such tools. Networking systems typically try to attain secure communication by applying standard cryptographic techniques. In this thesis...... attacks, and attacks launched by insiders. Finally, the perspectives for the application of the analysis techniques are discussed, thereby, coming a small step closer to providing developers with easy- to-use tools for validating the security of networking applications....

  2. Functional stoichiometric analysis of metabolic networks.

    Science.gov (United States)

    Urbanczik, R; Wagner, C

    2005-11-15

    An important tool in Systems Biology is the stoichiometric modeling of metabolic networks, where the stationary states of the network are described by a high-dimensional polyhedral cone, the so-called flux cone. Exhaustive descriptions of the metabolism can be obtained by computing the elementary vectors of this cone but, owing to a combinatorial explosion of the number of elementary vectors, this approach becomes computationally intractable for genome scale networks. Hence, we propose to instead focus on the conversion cone, a projection of the flux cone, which describes the interaction of the metabolism with its external chemical environment. We present a direct method for calculating the elementary vectors of this cone and, by studying the metabolism of Saccharomyces cerevisiae, we demonstrate that such an analysis is computationally feasible even for genome scale networks.

  3. A statistical analysis of UK financial networks

    Science.gov (United States)

    Chu, J.; Nadarajah, S.

    2017-04-01

    In recent years, with a growing interest in big or large datasets, there has been a rise in the application of large graphs and networks to financial big data. Much of this research has focused on the construction and analysis of the network structure of stock markets, based on the relationships between stock prices. Motivated by Boginski et al. (2005), who studied the characteristics of a network structure of the US stock market, we construct network graphs of the UK stock market using same method. We fit four distributions to the degree density of the vertices from these graphs, the Pareto I, Fréchet, lognormal, and generalised Pareto distributions, and assess the goodness of fit. Our results show that the degree density of the complements of the market graphs, constructed using a negative threshold value close to zero, can be fitted well with the Fréchet and lognormal distributions.

  4. Visualization and Analysis of Complex Covert Networks

    DEFF Research Database (Denmark)

    Memon, Bisharat

    This report discusses and summarize the results of my work so far in relation to my Ph.D. project entitled "Visualization and Analysis of Complex Covert Networks". The focus of my research is primarily on development of methods and supporting tools for visualization and analysis of networked...... systems that are covert and hence inherently complex. My Ph.D. is positioned within the wider framework of CrimeFighter project. The framework envisions a number of key knowledge management processes that are involved in the workflow, and the toolbox provides supporting tools to assist human end...

  5. In silico Biochemical Reaction Network Analysis (IBRENA): a package for simulation and analysis of reaction networks.

    Science.gov (United States)

    Liu, Gang; Neelamegham, Sriram

    2008-04-15

    We present In silico Biochemical Reaction Network Analysis (IBRENA), a software package which facilitates multiple functions including cellular reaction network simulation and sensitivity analysis (both forward and adjoint methods), coupled with principal component analysis, singular-value decomposition and model reduction. The software features a graphical user interface that aids simulation and plotting of in silico results. While the primary focus is to aid formulation, testing and reduction of theoretical biochemical reaction networks, the program can also be used for analysis of high-throughput genomic and proteomic data. The software package, manual and examples are available at http://www.eng.buffalo.edu/~neel/ibrena

  6. Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience.

    Science.gov (United States)

    Gong, Diankun; He, Hui; Ma, Weiyi; Liu, Dongbo; Huang, Mengting; Dong, Li; Gong, Jinnan; Li, Jianfu; Luo, Cheng; Yao, Dezhong

    2016-01-01

    Action video games (AVGs) have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN) and Central Executive Network (CEN), which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts' and amateurs' resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

  7. Functional Integration between Salience and Central Executive Networks: A Role for Action Video Game Experience

    Directory of Open Access Journals (Sweden)

    Diankun Gong

    2016-01-01

    Full Text Available Action video games (AVGs have attracted increasing research attention as they offer a unique perspective into the relation between active learning and neural plasticity. However, little research has examined the relation between AVG experience and the plasticity of neural network mechanisms. It has been proposed that AVG experience is related to the integration between Salience Network (SN and Central Executive Network (CEN, which are responsible for attention and working memory, respectively, two cognitive functions essential for AVG playing. This study initiated a systematic investigation of this proposition by analyzing AVG experts’ and amateurs’ resting-state brain functions through graph theoretical analyses and functional connectivity. Results reveal enhanced intra- and internetwork functional integrations in AVG experts compared to amateurs. The findings support the possible relation between AVG experience and the neural network plasticity.

  8. Organizational network analysis for two networks in the Washington State Department of Transportation.

    Science.gov (United States)

    2010-10-01

    Organizational network analysis (ONA) consists of gathering data on information sharing and : connectivity in a group, calculating network measures, creating network maps, and using this : information to analyze and improve the functionality of the g...

  9. NETWORK ANALYSIS OF PORTUGUESE TEAM ON FIFA WORLD CUP 2014

    Directory of Open Access Journals (Sweden)

    Rui Sousa Mendes,

    2015-05-01

    Full Text Available Match analysis has been using in football case to identify properties and patterns of teams (Sarmento et al., 2014. From the regular notational analysis until the most recent computational tactical metrics, a lot of different outcomes can be possible to extract from a single match (Clemente, Couceiro, Martins, & Mendes, 2015. In the specific case of football, the cooperation among team-members is one of the main factors that contribute for a better performance (Grund, 2012. Thus, to analyse such cooperation the Social Network Analysis have been used to identify how team-members are connected and if there are cooperation tendencies inside the team (Clemente et al., 2015. The prominent players have been also analysed in order to identify the central players in the team (Clemente, Couceiro, Martins, & Mendes, 2014.Objectives: Therefore, using the social network analysis approach the aim of this study was to analyse the centrality levels of Portuguese positional roles during the FIFA World Cup 2014 and to identify the prominent tactical positions that determined the moments with ball.

  10. The efficacy of centralized flow rate control in 802.11-based wireless mesh networks

    KAUST Repository

    Jamshaid, K.

    2013-06-13

    Commodity WiFi-based wireless mesh networks (WMNs) can be used to provide last mile Internet access. These networks exhibit extreme unfairness with backlogged traffic sources. Current solutions propose distributed source-rate control algorithms requiring link-layer or transport-layer changes on all mesh nodes. This is often infeasible in large practical deployments. In wireline networks, router-assisted rate control techniques have been proposed for use alongside end-to-end mechanisms. We wish to evaluate the feasibility of establishing similar centralized control via gateways in WMNs. In this paper, we focus on the efficacy of this control rather than the specifics of the controller design mechanism. We answer the question: Given sources that react predictably to congestion notification, can we enforce a desired rate allocation through a single centralized controller? The answer is not obvious because flows experience varying contention levels, and transmissions are scheduled by a node using imperfect local knowledge. We find that common router-assisted flow control schemes used in wired networks fail in WMNs because they assume that (1) links are independent, and (2) router queue buildups are sufficient for detecting congestion. We show that non-work-conserving, rate-based centralized scheduling can effectively enforce rate allocation. It can achieve results comparable to source rate limiting, without requiring any modifications to mesh routers or client devices. 2013 Jamshaid et al.; licensee Springer.

  11. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks.

    Science.gov (United States)

    Kelman, Ilan; Luthe, Tobias; Wyss, Romano; Tørnblad, Silje H; Evers, Yvette; Curran, Marina Martin; Williams, Richard J; Berlow, Eric L

    2016-01-01

    This study integrates quantitative social network analysis (SNA) and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics. Two case studies are used, the Surselva-Gotthard region in the Swiss Alps and Longyearbyen in the Arctic archipelago of Svalbard, to test the spatial characteristics of physical proximity, isolation, and smallness for understanding tourism business links. In the larger Surselva-Gotthard region, we found a strong relationship between geographic separation of the three communities on compartmentalization of the collaboration network. A small set of businesses played a central role in steering collaborative decisions for this community, while a group of structurally 'peripheral' actors were less influential. By contrast, the business community in Svalbard showed compartmentalization that was independent of geographic distance between actors. Within towns of similar size and governance scale, Svalbard is more compartmentalized, and those compartments are not driven by geographic separation of the collaboration clusters. This compartmentalization in Svalbard was reflected in a lower density of formal business collaboration ties compared to the communities of the Alps. We infer that the difference is due to Svalbard having higher cultural diversity and population turnover than the Alps communities. We propose that integrating quantitative network analysis from simple surveys with qualitative interviews targeted from the network results is an efficient general approach to identify regionally specific constraints and opportunities for effective governance.

  12. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks.

    Directory of Open Access Journals (Sweden)

    Ilan Kelman

    Full Text Available This study integrates quantitative social network analysis (SNA and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics. Two case studies are used, the Surselva-Gotthard region in the Swiss Alps and Longyearbyen in the Arctic archipelago of Svalbard, to test the spatial characteristics of physical proximity, isolation, and smallness for understanding tourism business links. In the larger Surselva-Gotthard region, we found a strong relationship between geographic separation of the three communities on compartmentalization of the collaboration network. A small set of businesses played a central role in steering collaborative decisions for this community, while a group of structurally 'peripheral' actors were less influential. By contrast, the business community in Svalbard showed compartmentalization that was independent of geographic distance between actors. Within towns of similar size and governance scale, Svalbard is more compartmentalized, and those compartments are not driven by geographic separation of the collaboration clusters. This compartmentalization in Svalbard was reflected in a lower density of formal business collaboration ties compared to the communities of the Alps. We infer that the difference is due to Svalbard having higher cultural diversity and population turnover than the Alps communities. We propose that integrating quantitative network analysis from simple surveys with qualitative interviews targeted from the network results is an efficient general approach to identify regionally specific constraints and opportunities for effective governance.

  13. Phylodynamic analysis of a viral infection network

    Directory of Open Access Journals (Sweden)

    Teiichiro eShiino

    2012-07-01

    Full Text Available Viral infections by sexual and droplet transmission routes typically spread through a complex host-to-host contact network. Clarifying the transmission network and epidemiological parameters affecting the variations and dynamics of a specific pathogen is a major issue in the control of infectious diseases. However, conventional methods such as interview and/or classical phylogenetic analysis of viral gene sequences have inherent limitations and often fail to detect infectious clusters and transmission connections. Recent improvements in computational environments now permit the analysis of large datasets. In addition, novel analytical methods have been developed that serve to infer the evolutionary dynamics of virus genetic diversity using sample date information and sequence data. This type of framework, termed phylodynamics, helps connect some of the missing links on viral transmission networks, which are often hard to detect by conventional methods of epidemiology. With sufficient number of sequences available, one can use this new inference method to estimate theoretical epidemiological parameters such as temporal distributions of the primary infection, fluctuation of the pathogen population size, basic reproductive number, and the mean time span of disease infectiousness. Transmission networks estimated by this framework often have the properties of a scale-free network, which are characteristic of infectious and social communication processes. Network analysis based on phylodynamics has alluded to various suggestions concerning the infection dynamics associated with a given community and/or risk behavior. In this review, I will summarize the current methods available for identifying the transmission network using phylogeny, and present an argument on the possibilities of applying the scale-free properties to these existing frameworks.

  14. Bridging centrality: A new indicator to measure the positioning of actors in R&D networks

    Energy Technology Data Exchange (ETDEWEB)

    Scherngell, T.; Wanzenboeck, I.; Berge, L.

    2016-07-01

    In the recent past, we can observe growing interest in the STI community in the notion of positioning indicators, shifting emphasis to actors in the innovation process and their R&D inter-linkages with other actors. In relation to this, we suggest a new approach for assessing the positioning of actors relying on the notion of bridging centrality (BC). Based on the concept of bridging paths, i.e. a set of two links connecting three actors across three different aggregate nodes (e.g. organisations, regions or countries), we argue that triangulation in networks is a key issue for knowledge recombinations and the extension of an actor's knowledge base. As bridges are most often not empirically observable at the individual level of research teams, we propose an approximated BC measure that provides a flexible framework for dealing with the aggregation problem in positioning actors. Hereby, BC is viewed as a function of an aggregate node's (i) participation intensity in the network, (ii) its openness to other nodes (i.e. the relative outward orientation of network links), and iii) the diversification of links to other nodes. In doing so, we provide an integrative perspective that enables us to achieve a better understanding of the positioning of certain actors in R&D networks. An illustrative example on the co-patent network of European regions demonstrates the performance and usefulness of our BC measure for networks constructed at the aggregated level, i.e. regions in our example. A region's outward orientation and the diversification of its network links moderates the influence of regional scale on network centrality. This is a major strength of the measure, and it paves the way for future studies to examine the role of certain aggregate node's, and, by this, contributes to the debate on positioning indicators in the STI context. (Author)

  15. A GPU-based solution for fast calculation of the betweenness centrality in large weighted networks

    Directory of Open Access Journals (Sweden)

    Rui Fan

    2017-12-01

    Full Text Available Betweenness, a widely employed centrality measure in network science, is a decent proxy for investigating network loads and rankings. However, its extremely high computational cost greatly hinders its applicability in large networks. Although several parallel algorithms have been presented to reduce its calculation cost for unweighted networks, a fast solution for weighted networks, which are commonly encountered in many realistic applications, is still lacking. In this study, we develop an efficient parallel GPU-based approach to boost the calculation of the betweenness centrality (BC for large weighted networks. We parallelize the traditional Dijkstra algorithm by selecting more than one frontier vertex each time and then inspecting the frontier vertices simultaneously. By combining the parallel SSSP algorithm with the parallel BC framework, our GPU-based betweenness algorithm achieves much better performance than its CPU counterparts. Moreover, to further improve performance, we integrate the work-efficient strategy, and to address the load-imbalance problem, we introduce a warp-centric technique, which assigns many threads rather than one to a single frontier vertex. Experiments on both realistic and synthetic networks demonstrate the efficiency of our solution, which achieves 2.9× to 8.44× speedups over the parallel CPU implementation. Our algorithm is open-source and free to the community; it is publicly available through https://dx.doi.org/10.6084/m9.figshare.4542405. Considering the pervasive deployment and declining price of GPUs in personal computers and servers, our solution will offer unprecedented opportunities for exploring betweenness-related problems and will motivate follow-up efforts in network science.

  16. Network centrality based team formation: A case study on T-20 cricket

    Directory of Open Access Journals (Sweden)

    Paramita Dey

    2017-07-01

    Full Text Available This paper proposes and evaluates the novel utilization of small world network properties for the formation of team of players with both best performances and best belongingness within the team network. To verify this concept, this methodology is applied to T-20 cricket teams. The players are treated as nodes of the network, whereas the number of interactions between team members is denoted as the edges between those nodes. All intra country networks form the cricket network for this case study. Analysis of the networks depicts that T-20 cricket network inherits all characteristics of small world network. Making a quantitative measure for an individual performance in the team sports is important with respect to the fact that for team selection of an International match, from pool of best players, only eleven players can be selected for the team. The statistical record of each player considered as a traditional way of quantifying the performance of a player. But the other criteria such as performing against a strong opponent or performance as an effective team member such as fielding, running between the wickets, good partnership deserves more credential. In this paper a revised method based on social networking is presented to quantify the quality of team belongingness and efficiency of each player. The application of Social Network Analysis (SNA is explored to measure performances and the rank of the players. A bidirectional weighted network of players is generated using the information collected from T-20 cricket (2014–2016 and used for network analysis. Thus team was formed based on that ranking and compared with their IPL (Indian Premier League performances of 2016.

  17. Temporal stability of network centrality in control and default mode networks: Specific associations with externalizing psychopathology in children and adolescents.

    Science.gov (United States)

    Sato, João Ricardo; Biazoli, Claudinei Eduardo; Salum, Giovanni Abrahão; Gadelha, Ary; Crossley, Nicolas; Satterthwaite, Theodore D; Vieira, Gilson; Zugman, André; Picon, Felipe Almeida; Pan, Pedro Mario; Hoexter, Marcelo Queiroz; Anés, Mauricio; Moura, Luciana Monteiro; Del'aquilla, Marco Antonio Gomes; Amaro, Edson; McGuire, Philip; Lacerda, Acioly L T; Rohde, Luis Augusto; Miguel, Euripedes Constantino; Jackowski, Andrea Parolin; Bressan, Rodrigo Affonseca

    2015-12-01

    Abnormal connectivity patterns have frequently been reported as involved in pathological mental states. However, most studies focus on "static," stationary patterns of connectivity, which may miss crucial biological information. Recent methodological advances have allowed the investigation of dynamic functional connectivity patterns that describe non-stationary properties of brain networks. Here, we introduce a novel graphical measure of dynamic connectivity, called time-varying eigenvector centrality (tv-EVC). In a sample 655 children and adolescents (7-15 years old) from the Brazilian "High Risk Cohort Study for Psychiatric Disorders" who were imaged using resting-state fMRI, we used this measure to investigate age effects in the temporal in control and default-mode networks (CN/DMN). Using support vector regression, we propose a network maturation index based on the temporal stability of tv-EVC. Moreover, we investigated whether the network maturation is associated with the overall presence of behavioral and emotional problems with the Child Behavior Checklist. As hypothesized, we found that the tv-EVC at each node of CN/DMN become more stable with increasing age (P < 0.001 for all nodes). In addition, the maturity index for this particular network is indeed associated with general psychopathology in children assessed by the total score of Child Behavior Checklist (P = 0.027). Moreover, immaturity of the network was mainly correlated with externalizing behavior dimensions. Taken together, these results suggest that changes in functional network dynamics during neurodevelopment may provide unique insights regarding pathophysiology. © 2015 Wiley Periodicals, Inc.

  18. Network analysis: A new way of understanding psychopathology?

    Science.gov (United States)

    Fonseca-Pedrero, Eduardo

    Current taxonomic systems are based on a descriptive and categorical approach where psychopathological symptoms and signs are caused by a hypothetical underlying mental disorder. In order to circumvent the limitations of classification systems, it is necessary to incorporate new conceptual and psychometric models that allow to understand, analyze and intervene in psychopathological phenomena from another perspective. The main goal was to present a new approach called network analysis for its application in the field of psychopathology. First of all, a brief introduction where psychopathological disorders are conceived as complex dynamic systems was carried out. Key concepts, as well as the different types of networks and the procedures for their estimation, are discussed. Following this, centrality measures, important for the understanding of the network as well as to examine the relevance of the variables within the network were addressed. These factors were then exemplified by estimating a network of self-reported psychopathological symptoms in a representative sample of adolescents. Finally, a brief recapitulation is made and future lines of research are discussed. Copyright © 2017 SEP y SEPB. Publicado por Elsevier España, S.L.U. All rights reserved.

  19. Multifractal analysis of mobile social networks

    Science.gov (United States)

    Zheng, Wei; Zhang, Zifeng; Deng, Yufan

    2017-09-01

    As Wireless Fidelity (Wi-Fi)-enabled handheld devices have been widely used, the mobile social networks (MSNs) has been attracting extensive attention. Fractal approaches have also been widely applied to characterierize natural networks as useful tools to depict their spatial distribution and scaling properties. Moreover, when the complexity of the spatial distribution of MSNs cannot be properly charaterized by single fractal dimension, multifractal analysis is required. For further research, we introduced a multifractal analysis method based on box-covering algorithm to describe the structure of MSNs. Using this method, we find that the networks are multifractal at different time interval. The simulation results demonstrate that the proposed method is efficient for analyzing the multifractal characteristic of MSNs, which provides a distribution of singularities adequately describing both the heterogeneity of fractal patterns and the statistics of measurements across spatial scales in MSNs.

  20. Classification and Analysis of Computer Network Traffic

    DEFF Research Database (Denmark)

    Bujlow, Tomasz

    2014-01-01

    for traffic classification, which can be used for nearly real-time processing of big amounts of data using affordable CPU and memory resources. Other questions are related to methods for real-time estimation of the application Quality of Service (QoS) level based on the results obtained by the traffic......Traffic monitoring and analysis can be done for multiple different reasons: to investigate the usage of network resources, assess the performance of network applications, adjust Quality of Service (QoS) policies in the network, log the traffic to comply with the law, or create realistic models...... classifier. This thesis is focused on topics connected with traffic classification and analysis, while the work on methods for QoS assessment is limited to defining the connections with the traffic classification and proposing a general algorithm. We introduced the already known methods for traffic...

  1. Bandwidth Analysis of Smart Meter Network Infrastructure

    DEFF Research Database (Denmark)

    Balachandran, Kardi; Olsen, Rasmus Løvenstein; Pedersen, Jens Myrup

    2014-01-01

    Advanced Metering Infrastructure (AMI) is a net-work infrastructure in Smart Grid, which links the electricity customers to the utility company. This network enables smart services by making it possible for the utility company to get an overview of their customers power consumption and also control...... to utilize smart meters and which existing broadband network technologies can facilitate this smart meter service. Initially, scenarios for smart meter infrastructure are identified. The paper defines abstraction models which cover the AMI scenarios. When the scenario has been identified a general overview...... of the bandwidth requirements are analysed. For this analysis the assumptions and limitations are defined. The results obtained by the analysis show, that the amount of data collected and transferred by a smart meter is very low compared to the available bandwidth of most internet connections. The results show...

  2. Analysis of complex network performance and heuristic node removal strategies

    Science.gov (United States)

    Jahanpour, Ehsan; Chen, Xin

    2013-12-01

    Removing important nodes from complex networks is a great challenge in fighting against criminal organizations and preventing disease outbreaks. Six network performance metrics, including four new metrics, are applied to quantify networks' diffusion speed, diffusion scale, homogeneity, and diameter. In order to efficiently identify nodes whose removal maximally destroys a network, i.e., minimizes network performance, ten structured heuristic node removal strategies are designed using different node centrality metrics including degree, betweenness, reciprocal closeness, complement-derived closeness, and eigenvector centrality. These strategies are applied to remove nodes from the September 11, 2001 hijackers' network, and their performance are compared to that of a random strategy, which removes randomly selected nodes, and the locally optimal solution (LOS), which removes nodes to minimize network performance at each step. The computational complexity of the 11 strategies and LOS is also analyzed. Results show that the node removal strategies using degree and betweenness centralities are more efficient than other strategies.

  3. Positive Selection and Centrality in the Yeast and Fly Protein-Protein Interaction Networks

    Directory of Open Access Journals (Sweden)

    Sandip Chakraborty

    2016-01-01

    Full Text Available Proteins within a molecular network are expected to be subject to different selective pressures depending on their relative hierarchical positions. However, it is not obvious what genes within a network should be more likely to evolve under positive selection. On one hand, only mutations at genes with a relatively high degree of control over adaptive phenotypes (such as those encoding highly connected proteins are expected to be “seen” by natural selection. On the other hand, a high degree of pleiotropy at these genes is expected to hinder adaptation. Previous analyses of the human protein-protein interaction network have shown that genes under long-term, recurrent positive selection (as inferred from interspecific comparisons tend to act at the periphery of the network. It is unknown, however, whether these trends apply to other organisms. Here, we show that long-term positive selection has preferentially targeted the periphery of the yeast interactome. Conversely, in flies, genes under positive selection encode significantly more connected and central proteins. These observations are not due to covariation of genes’ adaptability and centrality with confounding factors. Therefore, the distribution of proteins encoded by genes under recurrent positive selection across protein-protein interaction networks varies from one species to another.

  4. The Central Italy Electromagnetic Network and the 2009 L'Aquila Earthquake: Observed Electric Activity

    Directory of Open Access Journals (Sweden)

    Cristiano Fidani

    2011-12-01

    Full Text Available A network of low frequency electromagnetic detectors has been operating in Central Italy for more than three years, consisting of identical instruments that continuously record the electrical components of the electromagnetic field, ranging from a few Hz to tens of kHz. These signals are analyzed in real time and their power spectrum contents and time/frequency data are available online. To date, specific interest has been devoted to searching for any possible electromagnetic features which correlate with seismic activity in the same region. In this study, spectral analysis has evidenced very distinct power spectrum signatures that increased in intensity when strong seismic activity occurred near the stations of the 2009 L'Aquila earthquake. These signatures have revealed horizontally oriented electric fields, between 20 Hz to 400 Hz, lasting from several minutes to up to two hours. Their power intensities have been found to be about 1 μV/m. Moreover, a large number of man-made signals and meteorologic electric perturbations were recorded. Anthropogenic signatures have come from power line disturbances at 50 Hz and higher harmonics up to several kHz, while radio transmissions have influenced the higher kHz spectrum. Reception from low frequency transmitters is also provided in relation to seismic activity. Meteorologic signatures cover the lower frequency band through phenomena such as spherics, Schumann resonances and rain electrical perturbations. All of these phenomena are useful teaching tools for introducing students to this invisible electromagnetic world.

  5. Diversity Performance Analysis on Multiple HAP Networks

    Directory of Open Access Journals (Sweden)

    Feihong Dong

    2015-06-01

    Full Text Available One of the main design challenges in wireless sensor networks (WSNs is achieving a high-data-rate transmission for individual sensor devices. The high altitude platform (HAP is an important communication relay platform for WSNs and next-generation wireless networks. Multiple-input multiple-output (MIMO techniques provide the diversity and multiplexing gain, which can improve the network performance effectively. In this paper, a virtual MIMO (V-MIMO model is proposed by networking multiple HAPs with the concept of multiple assets in view (MAV. In a shadowed Rician fading channel, the diversity performance is investigated. The probability density function (PDF and cumulative distribution function (CDF of the received signal-to-noise ratio (SNR are derived. In addition, the average symbol error rate (ASER with BPSK and QPSK is given for the V-MIMO model. The system capacity is studied for both perfect channel state information (CSI and unknown CSI individually. The ergodic capacity with various SNR and Rician factors for different network configurations is also analyzed. The simulation results validate the effectiveness of the performance analysis. It is shown that the performance of the HAPs network in WSNs can be significantly improved by utilizing the MAV to achieve overlapping coverage, with the help of the V-MIMO techniques.

  6. Mixed Methods Analysis of Enterprise Social Networks

    DEFF Research Database (Denmark)

    Behrendt, Sebastian; Richter, Alexander; Trier, Matthias

    2014-01-01

    The increasing use of enterprise social networks (ESN) generates vast amounts of data, giving researchers and managerial decision makers unprecedented opportunities for analysis. However, more transparency about the available data dimensions and how these can be combined is needed to yield accurate...

  7. Nonlinear Time Series Analysis via Neural Networks

    Science.gov (United States)

    Volná, Eva; Janošek, Michal; Kocian, Václav; Kotyrba, Martin

    This article deals with a time series analysis based on neural networks in order to make an effective forex market [Moore and Roche, J. Int. Econ. 58, 387-411 (2002)] pattern recognition. Our goal is to find and recognize important patterns which repeatedly appear in the market history to adapt our trading system behaviour based on them.

  8. Combining morphological analysis and Bayesian networks for ...

    African Journals Online (AJOL)

    Morphological analysis (MA) and Bayesian networks (BN) are two closely related modelling methods, each of which has its advantages and disadvantages for strategic decision support modelling. MA is a method for defining, linking and evaluating problem spaces. BNs are graphical models which consist of a qualitative ...

  9. Models of network reliability analysis, combinatorics, and Monte Carlo

    CERN Document Server

    Gertsbakh, Ilya B

    2009-01-01

    Unique in its approach, Models of Network Reliability: Analysis, Combinatorics, and Monte Carlo provides a brief introduction to Monte Carlo methods along with a concise exposition of reliability theory ideas. From there, the text investigates a collection of principal network reliability models, such as terminal connectivity for networks with unreliable edges and/or nodes, network lifetime distribution in the process of its destruction, network stationary behavior for renewable components, importance measures of network elements, reliability gradient, and network optimal reliability synthesis

  10. Gene network analysis in plant development by genomic technologies.

    Science.gov (United States)

    Wellmer, Frank; Riechmann, José Luis

    2005-01-01

    The analysis of the gene regulatory networks underlying development is of central importance for a better understanding of the mechanisms that control the formation of the different cell-types, tissues or organs of an organism. The recent invention of genomic technologies has opened the possibility of studying these networks at a global level. In this paper, we summarize some of the recent advances that have been made in the understanding of plant development by the application of genomic technologies. We focus on a few specific processes, namely flower and root development and the control of the cell cycle, but we also highlight landmark studies in other areas that opened new avenues of experimentation or analysis. We describe the methods and the strategies that are currently used for the analysis of plant development by genomic technologies, as well as some of the problems and limitations that hamper their application. Since many genomic technologies and concepts were first developed and tested in organisms other than plants, we make reference to work in non-plant species and compare the current state of network analysis in plants to that in other multicellular organisms.

  11. Large-Scale Road Network Vulnerability Analysis

    OpenAIRE

    Jenelius, Erik

    2010-01-01

    Disruptions in the transport system can have severe impacts for affected individuals, businesses and the society as a whole. In this research, vulnerability is seen as the risk of unplanned system disruptions, with a focus on large, rare events. Vulnerability analysis aims to provide decision support regarding preventive and restorative actions, ideally as an integrated part of the planning process.The thesis specifically develops the methodology for vulnerability analysis of road networks an...

  12. Computer methods in electric network analysis

    Energy Technology Data Exchange (ETDEWEB)

    Saver, P.; Hajj, I.; Pai, M.; Trick, T.

    1983-06-01

    The computational algorithms utilized in power system analysis have more than just a minor overlap with those used in electronic circuit computer aided design. This paper describes the computer methods that are common to both areas and highlights the differences in application through brief examples. Recognizing this commonality has stimulated the exchange of useful techniques in both areas and has the potential of fostering new approaches to electric network analysis through the interchange of ideas.

  13. Time series analysis of temporal networks

    Science.gov (United States)

    Sikdar, Sandipan; Ganguly, Niloy; Mukherjee, Animesh

    2016-01-01

    A common but an important feature of all real-world networks is that they are temporal in nature, i.e., the network structure changes over time. Due to this dynamic nature, it becomes difficult to propose suitable growth models that can explain the various important characteristic properties of these networks. In fact, in many application oriented studies only knowing these properties is sufficient. For instance, if one wishes to launch a targeted attack on a network, this can be done even without the knowledge of the full network structure; rather an estimate of some of the properties is sufficient enough to launch the attack. We, in this paper show that even if the network structure at a future time point is not available one can still manage to estimate its properties. We propose a novel method to map a temporal network to a set of time series instances, analyze them and using a standard forecast model of time series, try to predict the properties of a temporal network at a later time instance. To our aim, we consider eight properties such as number of active nodes, average degree, clustering coefficient etc. and apply our prediction framework on them. We mainly focus on the temporal network of human face-to-face contacts and observe that it represents a stochastic process with memory that can be modeled as Auto-Regressive-Integrated-Moving-Average (ARIMA). We use cross validation techniques to find the percentage accuracy of our predictions. An important observation is that the frequency domain properties of the time series obtained from spectrogram analysis could be used to refine the prediction framework by identifying beforehand the cases where the error in prediction is likely to be high. This leads to an improvement of 7.96% (for error level ≤20%) in prediction accuracy on an average across all datasets. As an application we show how such prediction scheme can be used to launch targeted attacks on temporal networks. Contribution to the Topical Issue

  14. A social network analysis of treatment discoveries in cancer.

    Directory of Open Access Journals (Sweden)

    Athanasios Tsalatsanis

    Full Text Available Controlled clinical trials are widely considered to be the vehicle to treatment discovery in cancer that leads to significant improvements in health outcomes including an increase in life expectancy. We have previously shown that the pattern of therapeutic discovery in randomized controlled trials (RCTs can be described by a power law distribution. However, the mechanism generating this pattern is unknown. Here, we propose an explanation in terms of the social relations between researchers in RCTs. We use social network analysis to study the impact of interactions between RCTs on treatment success. Our dataset consists of 280 phase III RCTs conducted by the NCI from 1955 to 2006. The RCT networks are formed through trial interactions formed i at random, ii based on common characteristics, or iii based on treatment success. We analyze treatment success in terms of survival hazard ratio as a function of the network structures. Our results show that the discovery process displays power law if there are preferential interactions between trials that may stem from researchers' tendency to interact selectively with established and successful peers. Furthermore, the RCT networks are "small worlds": trials are connected through a small number of ties, yet there is much clustering among subsets of trials. We also find that treatment success (improved survival is proportional to the network centrality measures of closeness and betweenness. Negative correlation exists between survival and the extent to which trials operate within a limited scope of information. Finally, the trials testing curative treatments in solid tumors showed the highest centrality and the most influential group was the ECOG. We conclude that the chances of discovering life-saving treatments are directly related to the richness of social interactions between researchers inherent in a preferential interaction model.

  15. Positive and negative forms of replicability in gene network analysis.

    Science.gov (United States)

    Verleyen, W; Ballouz, S; Gillis, J

    2016-04-01

    Gene networks have become a central tool in the analysis of genomic data but are widely regarded as hard to interpret. This has motivated a great deal of comparative evaluation and research into best practices. We explore the possibility that this may lead to overfitting in the field as a whole. We construct a model of 'research communities' sampling from real gene network data and machine learning methods to characterize performance trends. Our analysis reveals an important principle limiting the value of replication, namely that targeting it directly causes 'easy' or uninformative replication to dominate analyses. We find that when sampling across network data and algorithms with similar variability, the relationship between replicability and accuracy is positive (Spearman's correlation, rs ∼0.33) but where no such constraint is imposed, the relationship becomes negative for a given gene function (rs ∼ -0.13). We predict factors driving replicability in some prior analyses of gene networks and show that they are unconnected with the correctness of the original result, instead reflecting replicable biases. Without these biases, the original results also vanish replicably. We show these effects can occur quite far upstream in network data and that there is a strong tendency within protein-protein interaction data for highly replicable interactions to be associated with poor quality control. Algorithms, network data and a guide to the code available at: https://github.com/wimverleyen/AggregateGeneFunctionPrediction jgillis@cshl.edu Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

  16. Capacity analysis of vehicular communication networks

    CERN Document Server

    Lu, Ning

    2013-01-01

    This SpringerBrief focuses on the network capacity analysis of VANETs, a key topic as fundamental guidance on design and deployment of VANETs is very limited. Moreover, unique characteristics of VANETs impose distinguished challenges on such an investigation. This SpringerBrief first introduces capacity scaling laws for wireless networks and briefly reviews the prior arts in deriving the capacity of VANETs. It then studies the unicast capacity considering the socialized mobility model of VANETs. With vehicles communicating based on a two-hop relaying scheme, the unicast capacity bound is deriv

  17. Historical Network Analysis of the Web

    DEFF Research Database (Denmark)

    Brügger, Niels

    2013-01-01

    of the online web has for a number of years gained currency within Internet studies. However, the combination of these two phenomena—historical network analysis of material in web archives—can at best be characterized as an emerging new area of study. Most of the methodological challenges within this new area...... at the Danish parliamentary elections in 2011, 2007, and 2001. As the Internet grows older historical studies of networks on the web will probably become more widespread and therefore it may be about time to begin debating the methodological challenges within this emerging field....

  18. The interseismic velocity field of the central Apennines from a dense GPS network

    Directory of Open Access Journals (Sweden)

    Alessandro Galvani

    2013-02-01

    Full Text Available Since 1999, we have repeatedly surveyed the central Apennines through a dense survey-style geodetic network, the Central Apennines Geodetic Network (CAGeoNet. CAGeoNet consists of 123 benchmarks distributed over an area of ca. 180 km × 130 km, from the Tyrrhenian coast to the Adriatic coast, with an average inter-site distance of 3 km to 5 km. The network is positioned across the main seismogenic structures of the region that are capable of generating destructive earthquakes. Here, we show the horizontal GPS velocity field of both CAGeoNet and continuous GPS stations in this region, as estimated from the position–time series in the time span from 1999 to 2007. We analyzed the data using both the Bernese and GAMIT software, rigorously combining the two solutions to obtain a validated result. Then, we analyzed the strain-rate field, which shows a region of extension along the axis of the Apennine chain, with values from 2 × 10–9 yr–1 to 66·× 10–9 yr–1, and a relative minimum of ca. 20 × 10–9 yr–1 located in the L'Aquila basin area. Our velocity field represents an improved estimation of the ongoing elastic interseismic deformation of the central Apennines, and in particular relating to the area of the L'Aquila earthquake of April 6, 2009.

  19. Instantly Decodable Network Coding: From Centralized to Device-to-Device Communications

    KAUST Repository

    Douik, Ahmed S.

    2015-05-01

    From its introduction to its quindecennial, network coding have built a strong reputation in enhancing packet recovery process and achieving maximum information flow in both wires and wireless networks. Traditional studies focused on optimizing the throughput of the network by proposing complex schemes that achieve optimal delay. With the shift toward distributed computing at mobile devices, throughput and complexity become both critical factors that affect the efficiency of a coding scheme. Instantly decodable network coding imposed itself as a new paradigm in network coding that trades off this two aspects. This paper presents a survey of instantly decodable network coding schemes that are proposed in the literature. The various schemes are identified, categorized and evaluated. Two categories can be distinguished namely the conventional centralized schemes and the distributed or cooperative schemes. For each scheme, the comparison is carried out in terms of reliability, performance, complexity and packet selection methodology. Although the performance is generally inversely proportional to the computation complexity, numerous successful schemes from both the performance and complexity viewpoint are identified.

  20. Mathematical Analysis of Urban Spatial Networks

    CERN Document Server

    Blanchard, Philippe

    2009-01-01

    Cities can be considered to be among the largest and most complex artificial networks created by human beings. Due to the numerous and diverse human-driven activities, urban network topology and dynamics can differ quite substantially from that of natural networks and so call for an alternative method of analysis. The intent of the present monograph is to lay down the theoretical foundations for studying the topology of compact urban patterns, using methods from spectral graph theory and statistical physics. These methods are demonstrated as tools to investigate the structure of a number of real cities with widely differing properties: medieval German cities, the webs of city canals in Amsterdam and Venice, and a modern urban structure such as found in Manhattan. Last but not least, the book concludes by providing a brief overview of possible applications that will eventually lead to a useful body of knowledge for architects, urban planners and civil engineers.

  1. GEOMORPHOLOGIC ANALYSIS OF DRAINAGE NETWORKS ON MARS

    Directory of Open Access Journals (Sweden)

    KERESZTURI ÁKOS

    2012-06-01

    Full Text Available Altogether 327 valleys and their 314 cross-sectional profiles were analyzed on Mars, including width, depth, length, eroded volume, drainage and spatial density, as well as the network structure.According to this systematic analysis, five possible drainage network types were identified such as (a small valleys, (b integrated small valleys, (c individual, medium-sized valleys, (d unconfined,anastomosing outflow valleys, and (e confined outflow valleys. Measuring their various morphometric parameters, these five networks differ from each other in terms of parameters of the eroded volume, drainage density and depth values. This classification is more detailed than those described in the literature previously and correlated to several numerical parameters for the first time.These different types were probably formed during different periods of the evolution of Mars, and sprung from differently localized water sources, and they could be correlated to similar fluvialnetwork types from the Earth.

  2. A network analysis of Sibiu County, Romania

    CERN Document Server

    Grama, Cristina-Nicol

    2013-01-01

    Network science methods have proved to be able to provide useful insights from both a theoretical and a practical point of view in that they can better inform governance policies in complex dynamic environments. The tourism research community has provided an increasing number of works that analyse destinations from a network science perspective. However, most of the studies refer to relatively small samples of actors and linkages. With this note we provide a full network study, although at a preliminary stage, that reports a complete analysis of a Romanian destination (Sibiu). Our intention is to increase the set of similar studies with the aim of supporting the investigations in structural and dynamical characteristics of tourism destinations.

  3. Intentional risk management through complex networks analysis

    CERN Document Server

    Chapela, Victor; Moral, Santiago; Romance, Miguel

    2015-01-01

    This book combines game theory and complex networks to examine intentional technological risk through modeling. As information security risks are in constant evolution,  the methodologies and tools to manage them must evolve to an ever-changing environment. A formal global methodology is explained  in this book, which is able to analyze risks in cyber security based on complex network models and ideas extracted from the Nash equilibrium. A risk management methodology for IT critical infrastructures is introduced which provides guidance and analysis on decision making models and real situations. This model manages the risk of succumbing to a digital attack and assesses an attack from the following three variables: income obtained, expense needed to carry out an attack, and the potential consequences for an attack. Graduate students and researchers interested in cyber security, complex network applications and intentional risk will find this book useful as it is filled with a number of models, methodologies a...

  4. Micro-macro analysis of complex networks.

    Science.gov (United States)

    Marchiori, Massimo; Possamai, Lino

    2015-01-01

    Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a "classic" approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail ("micro") to a different scale level ("macro"), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability.

  5. Weighted brain networks in disease: centrality and entropy in HIV and aging

    Science.gov (United States)

    Thomas, Jewell B.; Brier, Matthew R.; Ortega, Mario; Benzinger, Tammie L.; Ances, Beau M.

    2014-01-01

    Graph theory models can produce simple, biologically informative metrics of the topology of resting-state functional connectivity (FC) networks. However, typical graph theory approaches model FC relationships between regions (nodes) as unweighted edges, complicating their interpretability in studies of disease or aging. We extended existing techniques and constructed fully-connected weighted graphs for groups of age-matched HIV positive (n=67) and HIV negative (n=77) individuals. We compared test-retest reliability of weighted vs. unweighted metrics in an independent study of healthy individuals (n=22) and found weighted measures to be more stable. We quantified two measures of node centrality (closeness centrality and eigenvector centrality) to capture the relative importance of individual nodes. We also quantified one measure of graph entropy (diversity) to measure the variability in connection strength (edge weights) at each node. HIV was primarily associated with differences in measures of centrality, and age was primarily associated with differences in diversity. HIV and age were associated with divergent measures when evaluated at the whole-graph level, within individual functional networks, and at the level of individual nodes. Graph models may allow us to distinguish previously indistinguishable effects related to HIV and age on FC. PMID:25034343

  6. Weighted brain networks in disease: centrality and entropy in human immunodeficiency virus and aging.

    Science.gov (United States)

    Thomas, Jewell B; Brier, Matthew R; Ortega, Mario; Benzinger, Tammie L; Ances, Beau M

    2015-01-01

    Graph theory models can produce simple, biologically informative metrics of the topology of resting-state functional connectivity (FC) networks. However, typical graph theory approaches model FC relationships between regions (nodes) as unweighted edges, complicating their interpretability in studies of disease or aging. We extended existing techniques and constructed fully connected weighted graphs for groups of age-matched human immunodeficiency virus (HIV) positive (n = 67) and HIV negative (n = 77) individuals. We compared test-retest reliability of weighted versus unweighted metrics in an independent study of healthy individuals (n = 22) and found weighted measures to be more stable. We quantified 2 measures of node centrality (closeness centrality and eigenvector centrality) to capture the relative importance of individual nodes. We also quantified 1 measure of graph entropy (diversity) to measure the variability in connection strength (edge weights) at each node. HIV was primarily associated with differences in measures of centrality, and age was primarily associated with differences in diversity. HIV and age were associated with divergent measures when evaluated at the whole graph level, within individual functional networks, and at the level of individual nodes. Graph models may allow us to distinguish previously indistinguishable effects related to HIV and age on FC. Copyright © 2015 Elsevier Inc. All rights reserved.

  7. INTERGEO - Central/East European Collaboration Network on direct application of geothermal energy

    Energy Technology Data Exchange (ETDEWEB)

    Popovski, K. [Central/East European Collaboration Network on Direct Application of Geothermal Energy, Bitola (Yugoslavia); Arpasi, M. [International Geothermal Association - European Branch, Budapest (Hungary)

    1997-12-01

    A proposal for organisation of a Network to be known as INTERGEO is presented, which should extend and reinforce the cooperation for the development of the direct application of geothermal energy between the developed EC countries and the ones of the so called Central/East European region. Unter the term `developed countries` for this particular energy source utilisation mainly Italy, France and Germany should be understood. The Central/East European region consists the following countries: Albania, Bosnia and Herzegovina, Bulgaria, Belarus, Croatia, Czech Republic, Estonia, Hungary, Latvia, Lituania, Macedonia, Moldova, Poland, Roumania, Slovenia, Slovakia, Turkey, Ukraine and Yugoslavia. The idea itself, the need and possibilities for organisation, possible plan of action and expected benefits for the EC and Central/East European countries are elaborated in order to come to the conclusions for the proposal justifiableness and feasibility for realisation. (orig.)

  8. How Did the Information Flow in the #AlphaGo Hashtag Network? A Social Network Analysis of the Large-Scale Information Network on Twitter.

    Science.gov (United States)

    Kim, Jinyoung

    2017-12-01

    As it becomes common for Internet users to use hashtags when posting and searching information on social media, it is important to understand who builds a hashtag network and how information is circulated within the network. This article focused on unlocking the potential of the #AlphaGo hashtag network by addressing the following questions. First, the current study examined whether traditional opinion leadership (i.e., the influentials hypothesis) or grassroot participation by the public (i.e., the interpersonal hypothesis) drove dissemination of information in the hashtag network. Second, several unique patterns of information distribution by key users were identified. Finally, the association between attributes of key users who exerted great influence on information distribution (i.e., the number of followers and follows) and their central status in the network was tested. To answer the proffered research questions, a social network analysis was conducted using a large-scale hashtag network data set from Twitter (n = 21,870). The results showed that the leading actors in the network were actively receiving information from their followers rather than serving as intermediaries between the original information sources and the public. Moreover, the leading actors played several roles (i.e., conversation starters, influencers, and active engagers) in the network. Furthermore, the number of their follows and followers were significantly associated with their central status in the hashtag network. Based on the results, the current research explained how the information was exchanged in the hashtag network by proposing the reciprocal model of information flow.

  9. [Cost estimation of an epidemiological surveillance network for animal diseases in Central Africa: a case study of the Chad network].

    Science.gov (United States)

    Ouagal, M; Berkvens, D; Hendrikx, P; Fecher-Bourgeois, F; Saegerman, C

    2012-12-01

    In sub-Saharan Africa, most epidemiological surveillance networks for animal diseases were temporarily funded by foreign aid. It should be possible for national public funds to ensure the sustainability of such decision support tools. Taking the epidemiological surveillance network for animal diseases in Chad (REPIMAT) as an example, this study aims to estimate the network's cost by identifying the various costs and expenditures for each level of intervention. The network cost was estimated on the basis of an analysis of the operational organisation of REPIMAT, additional data collected in surveys and interviews with network field workers and a market price listing for Chad. These costs were then compared with those of other epidemiological surveillance networks in West Africa. The study results indicate that REPIMAT costs account for 3% of the State budget allocated to the Ministry of Livestock. In Chad in general, as in other West African countries, fixed costs outweigh variable costs at every level of intervention. The cost of surveillance principally depends on what is needed for surveillance at the local level (monitoring stations) and at the intermediate level (official livestock sectors and regional livestock delegations) and on the cost of the necessary equipment. In African countries, the cost of surveillance per square kilometre depends on livestock density.

  10. Demographic changes in towns of Central Serbia: Comparative analysis

    Directory of Open Access Journals (Sweden)

    Filipović Marko

    2007-01-01

    Full Text Available Due to intensive urbanization, Central Serbia's urban population reached almost 60% in the total population. Despite the fact that the urban residents share in Serbia is still bellow the level of urbanization in developed countries, in which the percentage of urban residents exudes 70% (in majority of cases even more than 80%, it is an impression that demographic "resources" of rural areas have bean rather exhausted and that all demographic revitalization potential of Central Serbia is concentrated in towns. This paper treats the demographic changes which encompassed the towns of Central Serbia since 1981 census onwards, with special emphasis on the population migrations as well as on the natural growth, i.e. age - gender structure formation featuring the towns of Central Serbia. The changes will be analyzed trough a comparative analysis at the level of small, medium sized towns and big cities, while Belgrade will be represent as a special category.

  11. An evaluation of centrality measures used in cluster analysis

    Science.gov (United States)

    Engström, Christopher; Silvestrov, Sergei

    2014-12-01

    Clustering of data into groups of similar objects plays an important part when analysing many types of data, especially when the datasets are large as they often are in for example bioinformatics, social networks and computational linguistics. Many clustering algorithms such as K-means and some types of hierarchical clustering need a number of centroids representing the 'center' of the clusters. The choice of centroids for the initial clusters often plays an important role in the quality of the clusters. Since a data point with a high centrality supposedly lies close to the 'center' of some cluster, this can be used to assign centroids rather than through some other method such as picking them at random. Some work have been done to evaluate the use of centrality measures such as degree, betweenness and eigenvector centrality in clustering algorithms. The aim of this article is to compare and evaluate the usefulness of a number of common centrality measures such as the above mentioned and others such as PageRank and related measures.

  12. Social network analysis and network connectedness analysis for industrial symbiotic systems: model development and case study

    Science.gov (United States)

    Zhang, Yan; Zheng, Hongmei; Chen, Bin; Yang, Naijin

    2013-06-01

    An important and practical pattern of industrial symbiosis is rapidly developing: eco-industrial parks. In this study, we used social network analysis to study the network connectedness (i.e., the proportion of the theoretical number of connections that had been achieved) and related attributes of these hybrid ecological and industrial symbiotic systems. This approach provided insights into details of the network's interior and analyzed the overall degree of connectedness and the relationships among the nodes within the network. We then characterized the structural attributes of the network and subnetwork nodes at two levels (core and periphery), thereby providing insights into the operational problems within each eco-industrial park. We chose ten typical ecoindustrial parks in China and around the world and compared the degree of network connectedness of these systems that resulted from exchanges of products, byproducts, and wastes. By analyzing the density and nodal degree, we determined the relative power and status of the nodes in these networks, as well as other structural attributes such as the core-periphery structure and the degree of sub-network connectedness. The results reveal the operational problems created by the structure of the industrial networks and provide a basis for improving the degree of completeness, thereby increasing their potential for sustainable development and enriching the methods available for the study of industrial symbiosis.

  13. Syphilis Networks in Louisiana: An Analysis of Network Configuration and Disease Transmission

    Science.gov (United States)

    Desmarais, Catherine Theresa

    Background: In 2009, Louisiana had the highest rate of primary and secondary syphilis in the country. Recent partner notification approaches have been insufficient in addressing Louisiana's deeply entrenched areas of syphilis infection. Prior researchers have suggested that surveillance systems may benefit from utilizing social and spatial network analysis in syphilis control efforts. Objective: To expand the understanding of the spread of syphilis in Louisiana, and to add new tools to the state's case finding resources through the description of the characteristics of cases of early syphilis and their partners in Louisiana, the socio-sexual networks of these cases, and the geospatial clustering of cases and partners. Methods: Utilizing state surveillance data, all cases of primary, secondary, and early latent syphilis that were diagnosed in 2009 and data on their sexual or needle sharing partners were analyzed using a combination of descriptive, network, and geospatial measures. Results: In 2009, Louisiana experienced a high rate of heterosexual syphilis transmission. Within syphilis transmission networks, 50.8% of all cases were female and 84.2% of all cases were black. The average and median ages of males with reactive syphilis tests were higher than that of females in Louisiana, and in 88.9% of regions, older individuals were more likely to have a syphilis test than no test. A greater proportion of males (11.4%) refused to discuss partners than females (7.4%) and a greater proportion of males (5.5%) refused testing and prophylactic treatment than females (2.8%). No distinct patterns were seen in disease prevalence between regions based upon demographic data. Classic summary network measures such as density, degree, centrality, and betweenness provided little information on similarities and differences between the different regions in Louisiana. All measures indicated low density and extreme fragmentation of networks in Louisiana. The majority of network

  14. Introduction to stream network habitat analysis

    Science.gov (United States)

    Bartholow, John M.; Waddle, Terry J.

    1986-01-01

    Increasing demands on stream resources by a variety of users have resulted in an increased emphasis on studies that evaluate the cumulative effects of basinwide water management programs. Network habitat analysis refers to the evaluation of an entire river basin (or network) by predicting its habitat response to alternative management regimes. The analysis principally focuses on the biological and hydrological components of the riv er basin, which include both micro- and macrohabitat. (The terms micro- and macrohabitat are further defined and discussed later in this document.) Both conceptual and analytic models are frequently used for simplifying and integrating the various components of the basin. The model predictions can be used in developing management recommendations to preserve, restore, or enhance instream fish habitat. A network habitat analysis should begin with a clear and concise statement of the study objectives and a thorough understanding of the institutional setting in which the study results will be applied. This includes the legal, social, and political considerations inherent in any water management setting. The institutional environment may dictate the focus and level of detail required of the study to a far greater extent than the technical considerations. After the study objectives, including species on interest, and institutional setting are collectively defined, the technical aspects should be scoped to determine the spatial and temporal requirements of the analysis. A macro level approach should be taken first to identify critical biological elements and requirements. Next, habitat availability is quantified much as in a "standard" river segment analysis, with the likely incorporation of some macrohabitat components, such as stream temperature. Individual river segments may be aggregated to represent the networkwide habitat response of alternative water management schemes. Things learned about problems caused or opportunities generated may

  15. Principal component analysis networks and algorithms

    CERN Document Server

    Kong, Xiangyu; Duan, Zhansheng

    2017-01-01

    This book not only provides a comprehensive introduction to neural-based PCA methods in control science, but also presents many novel PCA algorithms and their extensions and generalizations, e.g., dual purpose, coupled PCA, GED, neural based SVD algorithms, etc. It also discusses in detail various analysis methods for the convergence, stabilizing, self-stabilizing property of algorithms, and introduces the deterministic discrete-time systems method to analyze the convergence of PCA/MCA algorithms. Readers should be familiar with numerical analysis and the fundamentals of statistics, such as the basics of least squares and stochastic algorithms. Although it focuses on neural networks, the book only presents their learning law, which is simply an iterative algorithm. Therefore, no a priori knowledge of neural networks is required. This book will be of interest and serve as a reference source to researchers and students in applied mathematics, statistics, engineering, and other related fields.

  16. Service network analysis for agricultural mental health

    Directory of Open Access Journals (Sweden)

    Fuller Jeffrey D

    2009-05-01

    Full Text Available Abstract Background Farmers represent a subgroup of rural and remote communities at higher risk of suicide attributed to insecure economic futures, self-reliant cultures and poor access to health services. Early intervention models are required that tap into existing farming networks. This study describes service networks in rural shires that relate to the mental health needs of farming families. This serves as a baseline to inform service network improvements. Methods A network survey of mental health related links between agricultural support, health and other human services in four drought declared shires in comparable districts in rural New South Wales, Australia. Mental health links covered information exchange, referral recommendations and program development. Results 87 agencies from 111 (78% completed a survey. 79% indicated that two thirds of their clients needed assistance for mental health related problems. The highest mean number of interagency links concerned information exchange and the frequency of these links between sectors was monthly to three monthly. The effectiveness of agricultural support and health sector links were rated as less effective by the agricultural support sector than by the health sector (p Conclusion Aligning with agricultural agencies is important to build effective mental health service pathways to address the needs of farming populations. Work is required to ensure that these agricultural support agencies have operational and effective links to primary mental health care services. Network analysis provides a baseline to inform this work. With interventions such as local mental health training and joint service planning to promote network development we would expect to see over time an increase in the mean number of links, the frequency in which these links are used and the rated effectiveness of these links.

  17. BioCichlid: central dogma-based 3D visualization system of time-course microarray data on a hierarchical biological network.

    Science.gov (United States)

    Ishiwata, Ryosuke R; Morioka, Masaki S; Ogishima, Soichi; Tanaka, Hiroshi

    2009-02-15

    BioCichlid is a 3D visualization system of time-course microarray data on molecular networks, aiming at interpretation of gene expression data by transcriptional relationships based on the central dogma with physical and genetic interactions. BioCichlid visualizes both physical (protein) and genetic (regulatory) network layers, and provides animation of time-course gene expression data on the genetic network layer. Transcriptional regulations are represented to bridge the physical network (transcription factors) and genetic network (regulated genes) layers, thus integrating promoter analysis into the pathway mapping. BioCichlid enhances the interpretation of microarray data and allows for revealing the underlying mechanisms causing differential gene expressions. BioCichlid is freely available and can be accessed at http://newton.tmd.ac.jp/. Source codes for both biocichlid server and client are also available.

  18. Network Analysis and Modeling in Systems Biology

    OpenAIRE

    Bosque Chacón, Gabriel

    2017-01-01

    This thesis is dedicated to the study and comprehension of biological networks at the molecular level. The objectives were to analyse their topology, integrate it in a genotype-phenotype analysis, develop richer mathematical descriptions for them, study their community structure and compare different methodologies for estimating their internal fluxes. The work presented in this document moves around three main axes. The first one is the biological. Which organisms were studied in this ...

  19. A user’s guide to network analysis in R

    CERN Document Server

    Luke, Douglas

    2015-01-01

    Presenting a comprehensive resource for the mastery of network analysis in R, the goal of Network Analysis with R is to introduce modern network analysis techniques in R to social, physical, and health scientists. The mathematical foundations of network analysis are emphasized in an accessible way and readers are guided through the basic steps of network studies: network conceptualization, data collection and management, network description, visualization, and building and testing statistical models of networks. As with all of the books in the Use R! series, each chapter contains extensive R code and detailed visualizations of datasets. Appendices will describe the R network packages and the datasets used in the book. An R package developed specifically for the book, available to readers on GitHub, contains relevant code and real-world network datasets as well.

  20. A super base station based centralized network architecture for 5G mobile communication systems

    Directory of Open Access Journals (Sweden)

    Manli Qian

    2015-04-01

    Full Text Available To meet the ever increasing mobile data traffic demand, the mobile operators are deploying a heterogeneous network with multiple access technologies and more and more base stations to increase the network coverage and capacity. However, the base stations are isolated from each other, so different types of radio resources and hardware resources cannot be shared and allocated within the overall network in a cooperative way. The mobile operators are thus facing increasing network operational expenses and a high system power consumption. In this paper, a centralized radio access network architecture, referred to as the super base station (super BS, is proposed, as a possible solution for an energy-efficient fifth-generation (5G mobile system. The super base station decouples the logical functions and physical entities of traditional base stations, so different types of system resources can be horizontally shared and statistically multiplexed among all the virtual base stations throughout the entire system. The system framework and main functionalities of the super BS are described. Some key technologies for system implementation, i.e., the resource pooling, real-time virtualization, adaptive hardware resource allocation are also highlighted.

  1. Computation and analysis of temporal betweenness in a knowledge mobilization network.

    Science.gov (United States)

    Afrasiabi Rad, Amir; Flocchini, Paola; Gaudet, Joanne

    2017-01-01

    Highly dynamic social networks, where connectivity continuously changes in time, are becoming more and more pervasive. Knowledge mobilization, which refers to the use of knowledge toward the achievement of goals, is one of the many examples of dynamic social networks. Despite the wide use and extensive study of dynamic networks, their temporal component is often neglected in social network analysis, and statistical measures are usually performed on static network representations. As a result, measures of importance (like betweenness centrality) typically do not reveal the temporal role of the entities involved. Our goal is to contribute to fill this limitation by proposing a form of temporal betweenness measure (foremost betweenness). Our method is analytical as well as experimental: we design an algorithm to compute foremost betweenness, and we apply it to a case study to analyze a knowledge mobilization network. We propose a form of temporal betweenness measure (foremost betweenness) to analyze a knowledge mobilization network and we introduce, for the first time, an algorithm to compute exact foremost betweenness. We then show that this measure, which explicitly takes time into account, allows us to detect centrality roles that were completely hidden in the classical statistical analysis. In particular, we uncover nodes whose static centrality was negligible, but whose temporal role might instead be important to accelerate mobilization flow in the network. We also observe the reverse behavior by detecting nodes with high static centrality, whose role as temporal bridges is instead very low. In this paper, we focus on a form of temporal betweenness designed to detect accelerators in dynamic networks. By revealing potentially important temporal roles, this study is a first step toward a better understanding of the impact of time in social networks and opens the road to further investigation.

  2. Structural Investigation of Aluminum in the U.S. Economy using Network Analysis.

    Science.gov (United States)

    Nuss, Philip; Chen, Wei-Qiang; Ohno, Hajime; Graedel, T E

    2016-04-05

    Metals are used in numerous products and are sourced via increasingly global and complex supply chains. Monetary input-output tables (MIOT) and network analysis can be applied to intersectoral supply chains and used to analyze structural aspects. We first provide a concise review of the literature related to network analysis applied to MIOTs. On the basis of a physical input-output table (PIOT) table of aluminum in the United States economy in 2007, we identify key sectors and discuss the overall topology of the aluminum network using tools of network analysis. Sectors highly dependent on metal product inputs or sales are identified using weighted degree centrality and their hierarchical organization is explored via clustering. Betweenness centrality and random walk centrality (page rank) are explored as means to identify network bottlenecks and relative sector importance. Aluminum, even though dominated by uses in the automobile, beverage and containers, and construction industries, finds application in a wide range of sectors. Motor vehicle parts manufacturing relies on a large number of upstream and downstream suppliers to function. We conclude by analyzing structural aspects of a subnetwork for automobile manufacturing and discuss how the use of network analysis relates to current criticality analyses of metal and mineral resources.

  3. Network value and optimum analysis on the mode of networked marketing in TV media

    Directory of Open Access Journals (Sweden)

    Xiao Dongpo

    2012-12-01

    Full Text Available Purpose: With the development of the networked marketing in TV media, it is important to do the research on network value and optimum analysis in this field.Design/methodology/approach: According to the research on the mode of networked marketing in TV media and Correlation theory, the essence of media marketing is creating, spreading and transferring values. The Participants of marketing value activities are in network, and value activities proceed in networked form. Network capability is important to TV media marketing activities.Findings: This article raises the direction of research of analysis and optimization about network based on the mode of networked marketing in TV media by studying TV media marketing Development Mechanism , network analysis and network value structure.

  4. Central venous pressure monitoring via peripherally or centrally inserted central catheters: a systematic review and meta-analysis.

    Science.gov (United States)

    Sanfilippo, Filippo; Noto, Alberto; Martucci, Gennaro; Farbo, Marco; Burgio, Gaetano; Biasucci, Daniele G

    2017-07-14

    The central venous pressure (CVP) is the most commonly used static marker of preload for guiding fluid therapy in critically ill patients, though its usefulness remains controversial. Centrally inserted central catheters (CICCs) are the gold-standard devices for CVP monitoring but peripherally inserted central catheters (PICCs) may represent a valid alternative. We undertook a systematic review and meta-analysis with the aim to investigate whether the difference between PICC- and CICC-measured CVP is not significant. We searched for clinical studies published in PubMed and EMBASE databases from inception until December 21st 2016. We included studies providing data on paired and simultaneous CVP measurement from PICCs and CICCs. We conducted two analyses on the values of CVP, the first one according to the total number of CVP assessments, the second one considering the number of patients recruited. Four studies matched the inclusion criteria, but only three of them provided data for the meta-analyses. Both analyses showed non-significant differences between PICC-measured and CICC-measured CVP: 1489 paired simultaneous CVP assessments (MD 0.16, 95%CI -0.14, 0.45, p = 0.30) on a total of 57 patients (MD 0.22, 95%CI -1.46, 1.91, p = 0.80). Both analyses showed no heterogeneity (I2 = 0%). Available evidence supports that CVP monitoring with PICCs is accurate and reproduces similar values to those obtained from CICCs. The possibility to monitor CVP should not be used among clinical criteria for preferring a CICC over a PICC line.

  5. The Impacts of Network Centrality and Self-Regulation on an E-Learning Environment with the Support of Social Network Awareness

    Science.gov (United States)

    Lin, Jian-Wei; Huang, Hsieh-Hong; Chuang, Yuh-Shy

    2015-01-01

    An e-learning environment that supports social network awareness (SNA) is a highly effective means of increasing peer interaction and assisting student learning by raising awareness of social and learning contexts of peers. Network centrality profoundly impacts student learning in an SNA-related e-learning environment. Additionally,…

  6. Inflation and Central Bank Independence: A Meta-regression Analysis

    NARCIS (Netherlands)

    Klomp, J.G.; Haan, de J.

    2010-01-01

    Using 59 studies, we perform a meta-regression analysis of studies examining the relationship between inflation and central bank independence (CBI). The studies considered are very different with respect to the CBI indicator used, the sample of countries and time periods covered, model

  7. INFLATION AND CENTRAL BANK INDEPENDENCE : A META-REGRESSION ANALYSIS

    NARCIS (Netherlands)

    Klomp, Jeroen; de Haan, Jakob

    Using 59 studies, we perform a meta-regression analysis of studies examining the relationship between inflation and central bank independence (CBI). The studies considered are very different with respect to the CBI indicator used, the sample of countries and time periods covered, model

  8. The Central and Eastern U.S. Seismic Network: Legacy of USArray

    Science.gov (United States)

    Eakins, J. A.; Astiz, L.; Benz, H.; Busby, R. W.; Hafner, K.; Reyes, J. C.; Sharer, G.; Vernon, F.; Woodward, R.

    2014-12-01

    As the USArray Transportable Array entered the central and eastern United States, several Federal agencies (National Science Foundation, U.S. Geological Survey, U.S. Nuclear Regulatory Commission, and Department of Energy) recognized the unique opportunity to retain TA stations beyond the original timeline. The mission of the CEUSN is to produce data that enables researchers and Federal agencies alike to better understand the basic geologic questions, background earthquake rates and distribution, seismic hazard potential, and associated societal risks of this region. The selected long-term sub-array from Transportable Array (TA) stations includes nearly 200 sites, complemented by 100 broadband stations from the existing regional seismic networks to form the Central and Eastern United States Network (CEUSN). Multiple criteria for site selection were weighed by an inter-agency TA Station Selection (TASS) Working Group: seismic noise characteristics, data availability in real time, proximity to nuclear power plants, and homogeneous distribution throughout the region. The Array Network Facility (ANF) started collecting data for CEUSN network stations since late 2013, with all stations collected since May 2014. Regional seismic data streams are collected in real-time from the IRIS Data Management Center (DMC). TA stations selected to be part of CEUSN, retain the broadband sensor to which a 100 sps channel is added, the infrasound and environmental channels, and, at some stations, accelerometers are deployed. The upgraded sites become part of the N4 network for which ANF provides metadata and can issue remote commands to the station equipment. Stations still operated by TA, but planned for CEUSN, are included in the virtual network so all stations are currently available now. By the end of 2015, the remaining TA stations will be upgraded. Data quality control procedures developed for TA stations at ANF and at the DMC are currently performed on N4 data. However

  9. The Application of Social Network Analysis to Team Sports

    Science.gov (United States)

    Lusher, Dean; Robins, Garry; Kremer, Peter

    2010-01-01

    This article reviews how current social network analysis might be used to investigate individual and group behavior in sporting teams. Social network analysis methods permit researchers to explore social relations between team members and their individual-level qualities simultaneously. As such, social network analysis can be seen as augmenting…

  10. Analysis and visualization of citation networks

    CERN Document Server

    Zhao, Dangzhi

    2015-01-01

    Citation analysis-the exploration of reference patterns in the scholarly and scientific literature-has long been applied in a number of social sciences to study research impact, knowledge flows, and knowledge networks. It has important information science applications as well, particularly in knowledge representation and in information retrieval.Recent years have seen a burgeoning interest in citation analysis to help address research, management, or information service issues such as university rankings, research evaluation, or knowledge domain visualization. This renewed and growing interest

  11. The use of nodes attributes in social network analysis with an application to an international trade network

    Science.gov (United States)

    de Andrade, Ricardo Lopes; Rêgo, Leandro Chaves

    2018-02-01

    The social network analysis (SNA) studies the interactions among actors in a network formed through some relationship (friendship, cooperation, trade, among others). The SNA is constantly approached from a binary point of view, i.e., it is only observed if a link between two actors is present or not regardless of the strength of this link. It is known that different information can be obtained in weighted and unweighted networks and that the information extracted from weighted networks is more accurate and detailed. Another rarely discussed approach in the SNA is related to the individual attributes of the actors (nodes), because such analysis is usually focused on the topological structure of networks. Features of the nodes are not incorporated in the SNA what implies that there is some loss or misperception of information in those analyze. This paper aims at exploring more precisely the complexities of a social network, initially developing a method that inserts the individual attributes in the topological structure of the network and then analyzing the network in four different ways: unweighted, edge-weighted and two methods for using both edge-weights and nodes' attributes. The international trade network was chosen in the application of this approach, where the nodes represent the countries, the links represent the cash flow in the trade transactions and countries' GDP were chosen as nodes' attributes. As a result, it is possible to observe which countries are most connected in the world economy and with higher cash flows, to point out the countries that are central to the intermediation of the wealth flow and those that are most benefited from being included in this network. We also made a principal component analysis to study which metrics are more influential in describing the data variability, which turn out to be mostly the weighted metrics which include the nodes' attributes.

  12. Attitudes towards reforming primary care in Belgium: social network analysis in a pluralist context.

    Science.gov (United States)

    Lorant, Vincent; Rihoux, Benoît; Nicaise, Pablo

    2016-10-01

    Health care policies are influenced by many groups which in turn influence each other. Our aim was to describe a network of nominated influential stakeholders and analyze how it affects attitudes to reforming primary care. Face-to-face interviews were carried out in Belgium with 102 influential people. Each respondent was asked to score solutions for improving the role of general practice in the health care system and to nominate up to six other influential stakeholders. Social network and multivariate analyses were used to describe the nomination network and its effect on attitudes to reform. The network was highly centralized and homophilous (tendency to bond with people who are similar) for language groups. Despite Belgium having a strong pluralist tradition of decision making, policy makers were central to the network (average indegree = 10.8) compared to professional representatives (6.9). Respondents supported an enhanced role for general practitioners but did not support radically new policies. Social network analysis contributes to understanding why health care reforms may languish in pluralistic, decentralized health care systems. The central position of a stakeholder in a network is related to perceived influence but does not favour a radical policy orientation. In addition, language-group homophily in the 'perceived influence network' leads to a weak coalition that only favours small-step reform. © The Author(s) 2016.

  13. Will HIV vaccination reshape HIV risk behavior networks? A social network analysis of drug users' anticipated risk compensation.

    Science.gov (United States)

    Young, April M; Halgin, Daniel S; DiClemente, Ralph J; Sterk, Claire E; Havens, Jennifer R

    2014-01-01

    An HIV vaccine could substantially impact the epidemic. However, risk compensation (RC), or post-vaccination increase in risk behavior, could present a major challenge. The methodology used in previous studies of risk compensation has been almost exclusively individual-level in focus, and has not explored how increased risk behavior could affect the connectivity of risk networks. This study examined the impact of anticipated HIV vaccine-related RC on the structure of high-risk drug users' sexual and injection risk network. A sample of 433 rural drug users in the US provided data on their risk relationships (i.e., those involving recent unprotected sex and/or injection equipment sharing). Dyad-specific data were collected on likelihood of increasing/initiating risk behavior if they, their partner, or they and their partner received an HIV vaccine. Using these data and social network analysis, a "post-vaccination network" was constructed and compared to the current network on measures relevant to HIV transmission, including network size, cohesiveness (e.g., diameter, component structure, density), and centrality. Participants reported 488 risk relationships. Few reported an intention to decrease condom use or increase equipment sharing (4% and 1%, respectively). RC intent was reported in 30 existing risk relationships and vaccination was anticipated to elicit the formation of five new relationships. RC resulted in a 5% increase in risk network size (n = 142 to n = 149) and a significant increase in network density. The initiation of risk relationships resulted in the connection of otherwise disconnected network components, with the largest doubling in size from five to ten. This study demonstrates a new methodological approach to studying RC and reveals that behavior change following HIV vaccination could potentially impact risk network connectivity. These data will be valuable in parameterizing future network models that can determine if network-level change

  14. Social networks, market transactions, and reputation as a central resource. The Mercado del Mar, a fish market in central Mexico.

    Science.gov (United States)

    Pedroza-Gutiérrez, Carmen; Hernández, Juan M

    2017-01-01

    Fish consumption in Mexico is considered low (around 12 kg per person per year) and non-homogeneously distributed across the country. One of the reasons for this situation is the scarcity of wholesale selling sites. In this context, the Mercado del Mar (MM), located in Guadalajara city, Jalisco, is the second biggest wholesale fish market in Mexico, with a distribution of about 500 tons per day and a variety of about 350 different species of fish. In this paper, we argue that MM has accumulated social capital, which is formed from two main resources: buyer and seller relationships, and reputation. Specifically, the MM manages a broad and intensive interaction among business actors and the already achieved reputation allows the MM to adapt to market changes. To validate our hypotheses, an empirical study was conducted in 2015 by means of interviews to fish wholesalers in the MM and a sample of their suppliers and buyers. For simplicity we have only considered fresh water fish. We have followed snow-ball sampling as the survey strategy. Results show that the MM has responded to fish market dynamics organizing a complex network of buyers and suppliers whose relationships can be explained in the form of strong and weak ties. At the same time, reputation has been the central resource to build this social capital and also gives place to market transactions. Additionally, the strategic position of Guadalajara city and the well-connected routes have facilitated fish bulking and distribution in the region.

  15. Social networks, market transactions, and reputation as a central resource. The Mercado del Mar, a fish market in central Mexico.

    Directory of Open Access Journals (Sweden)

    Carmen Pedroza-Gutiérrez

    Full Text Available Fish consumption in Mexico is considered low (around 12 kg per person per year and non-homogeneously distributed across the country. One of the reasons for this situation is the scarcity of wholesale selling sites. In this context, the Mercado del Mar (MM, located in Guadalajara city, Jalisco, is the second biggest wholesale fish market in Mexico, with a distribution of about 500 tons per day and a variety of about 350 different species of fish. In this paper, we argue that MM has accumulated social capital, which is formed from two main resources: buyer and seller relationships, and reputation. Specifically, the MM manages a broad and intensive interaction among business actors and the already achieved reputation allows the MM to adapt to market changes. To validate our hypotheses, an empirical study was conducted in 2015 by means of interviews to fish wholesalers in the MM and a sample of their suppliers and buyers. For simplicity we have only considered fresh water fish. We have followed snow-ball sampling as the survey strategy. Results show that the MM has responded to fish market dynamics organizing a complex network of buyers and suppliers whose relationships can be explained in the form of strong and weak ties. At the same time, reputation has been the central resource to build this social capital and also gives place to market transactions. Additionally, the strategic position of Guadalajara city and the well-connected routes have facilitated fish bulking and distribution in the region.

  16. Ensemble approach to the analysis of weighted networks

    Science.gov (United States)

    Ahnert, S. E.; Garlaschelli, D.; Fink, T. M. A.; Caldarelli, G.

    2007-07-01

    We present an approach to the analysis of weighted networks, by providing a straightforward generalization of any network measure defined on unweighted networks, such as the average degree of the nearest neighbors, the clustering coefficient, the “betweenness,” the distance between two nodes, and the diameter of a network. All these measures are well established for unweighted networks but have hitherto proven difficult to define for weighted networks. Our approach is based on the translation of a weighted network into an ensemble of edges. Further introducing this approach we demonstrate its advantages by applying the clustering coefficient constructed in this way to two real-world weighted networks.

  17. Spectral analysis of crater central peak material (ccp)

    Science.gov (United States)

    Galiano, A.; Palomba, E.; Longobardo, A.; De Sanctis, M. C.; Carrozzo, F. G.; Tosi, F.

    2017-09-01

    The dwarf planet Ceres, the largest and most massive object in the main asteroid belt, is dark and heavily cratered by impacts. The detection of bright spots, especially in the Occator crater, suggested a vertical gradient in Ceres mineralogical composition. Geologic mapping of Ceres enabled the identification of various surface features of interest. Here we focus our attention on the geologic units known as crater central peak material (ccp). Ccp composes the central peak of several complex craters, probably representative of fresher material coming from the subsurface as a consequence of the impact. We carried out a spectral analysis of ccps found on Ceres to investigate the mineralogical properties of the subsurface material.

  18. Hearing health network: a spatial analysis

    Directory of Open Access Journals (Sweden)

    Camila Ferreira de Rezende

    2015-06-01

    Full Text Available INTRODUCTION: In order to meet the demands of the patient population with hearing impairment, the Hearing Health Care Network was created, consisting of primary care actions of medium and high complexity. Spatial analysis through geoprocessing is a way to understand the organization of such services. OBJECTIVE: To analyze the organization of the Hearing Health Care Network of the State of Minas Gerais. METHODS: Cross-sectional analytical study using geoprocessing techniques. The absolute frequency and the frequency per 1000 inhabitants of the following variables were analyzed: assessment and diagnosis, selection and adaptation of hearing aids, follow-up, and speech therapy. The spatial analysis unit was the health micro-region. RESULTS: The assessment and diagnosis, selection, and adaptation of hearing aids and follow-up had a higher absolute number in the micro-regions with hearing health services. The follow-up procedure showed the lowest occurrence. Speech therapy showed higher occurrence in the state, both in absolute numbers, as well as per population. CONCLUSION: The use of geoprocessing techniques allowed the identification of the care flow as a function of the procedure performance frequency, population concentration, and territory distribution. All procedures offered by the Hearing Health Care Network are performed for users of all micro-regions of the state.

  19. Design Criteria For Networked Image Analysis System

    Science.gov (United States)

    Reader, Cliff; Nitteberg, Alan

    1982-01-01

    Image systems design is currently undergoing a metamorphosis from the conventional computing systems of the past into a new generation of special purpose designs. This change is motivated by several factors, notably among which is the increased opportunity for high performance with low cost offered by advances in semiconductor technology. Another key issue is a maturing in understanding of problems and the applicability of digital processing techniques. These factors allow the design of cost-effective systems that are functionally dedicated to specific applications and used in a utilitarian fashion. Following an overview of the above stated issues, the paper presents a top-down approach to the design of networked image analysis systems. The requirements for such a system are presented, with orientation toward the hospital environment. The three main areas are image data base management, viewing of image data and image data processing. This is followed by a survey of the current state of the art, covering image display systems, data base techniques, communications networks and software systems control. The paper concludes with a description of the functional subystems and architectural framework for networked image analysis in a production environment.

  20. Capacity analysis of wireless mesh networks | Gumel | Nigerian ...

    African Journals Online (AJOL)

    The next generation wireless· netWorks experienced agreat development with emergence of wireless mesh networks (WMNs), which can be regarded as a realistic solution that provides wireless broadband access. The limited available bandwidth makes capacity analysis of the network very essential. While the network ...

  1. Functional network centrality in obesity: A resting-state and task fMRI study.

    Science.gov (United States)

    García-García, Isabel; Jurado, María Ángeles; Garolera, Maite; Marqués-Iturria, Idoia; Horstmann, Annette; Segura, Bàrbara; Pueyo, Roser; Sender-Palacios, María José; Vernet-Vernet, Maria; Villringer, Arno; Junqué, Carme; Margulies, Daniel S; Neumann, Jane

    2015-09-30

    Obesity is associated with structural and functional alterations in brain areas that are often functionally distinct and anatomically distant. This suggests that obesity is associated with differences in functional connectivity of regions distributed across the brain. However, studies addressing whole brain functional connectivity in obesity remain scarce. Here, we compared voxel-wise degree centrality and eigenvector centrality between participants with obesity (n=20) and normal-weight controls (n=21). We analyzed resting state and task-related fMRI data acquired from the same individuals. Relative to normal-weight controls, participants with obesity exhibited reduced degree centrality in the right middle frontal gyrus in the resting-state condition. During the task fMRI condition, obese participants exhibited less degree centrality in the left middle frontal gyrus and the lateral occipital cortex along with reduced eigenvector centrality in the lateral occipital cortex and occipital pole. Our results highlight the central role of the middle frontal gyrus in the pathophysiology of obesity, a structure involved in several brain circuits signaling attention, executive functions and motor functions. Additionally, our analysis suggests the existence of task-dependent reduced centrality in occipital areas; regions with a role in perceptual processes and that are profoundly modulated by attention. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

  2. Activity Recognition Using Complex Network Analysis.

    Science.gov (United States)

    Jalloul, Nahed; Poree, Fabienne; Viardot, Geoffrey; L'Hostis, Phillipe; Carrault, Guy

    2017-10-12

    In this paper, we perform complex network analysis on a connectivity dataset retrieved from a monitoring system in order to classify simple daily activities. The monitoring system is composed of a set of wearable sensing modules positioned on the subject's body and the connectivity data consists of the correlation between each pair of modules. A number of network measures are then computed followed by the application of statistical significance and feature selection methods. These methods were implemented for the purpose of reducing the total number of modules in the monitoring system required to provide accurate activity classification. The obtained results show that an overall accuracy of 84.6% for activity classification is achieved, using a Random Forest (RF) classifier, and when considering a monitoring system composed of only two modules positioned at the Neck and Thigh of the subject's body.

  3. Integrated Adaptive Analysis and Visualization of Satellite Network Data Project

    Data.gov (United States)

    National Aeronautics and Space Administration — We propose to develop a system that enables integrated and adaptive analysis and visualization of satellite network management data. Integrated analysis and...

  4. Analysis of Ego Network Structure in Online Social Networks

    OpenAIRE

    Arnaboldi, Valerio; Conti, Marco; Passarella, Andrea; Pezzoni, Fabio

    2012-01-01

    Results about offline social networks demonstrated that the social relationships that an individual (ego) maintains with other people (alters) can be organised into different groups according to the ego network model. In this model the ego can be seen as the centre of a series of layers of increasing size. Social relationships between ego and alters in layers close to ego are stronger than those belonging to more external layers. Online Social Networks are becoming a fundamental medium for hu...

  5. Analysis of networking characteristics of different personality types

    OpenAIRE

    Charilaos, Mylonas

    2014-01-01

    The MBTI personality test and a personal facebook network were used in order to gain some insights on the relationship of social network centrality and path length measures and different personality types. Although the personality classification data were scarce, there were some intuitive quantitative results supporting anecdotal statements, based on empirical observations, about the expected social behavior of personality types.

  6. Sediment Analysis Network for Decision Support (SANDS)

    Science.gov (United States)

    Hardin, D. M.; Keiser, K.; Graves, S. J.; Conover, H.; Ebersole, S.

    2009-12-01

    Since the year 2000, Eastern Louisiana, coastal Mississippi, Alabama, and the western Florida panhandle have been affected by 28 tropical storms, seven of which were hurricanes. These tropical cyclones have significantly altered normal coastal processes and characteristics in the Gulf region through sediment disturbance. Although tides, seasonality, and agricultural development influence suspended sediment and sediment deposition over periods of time, tropical storm activity has the capability of moving the largest sediment loads in the shortest periods of time for coastal areas. The importance of sediments upon water quality, coastal erosion, habitats and nutrients has made their study and monitoring vital to decision makers in the region. Currently agencies such as United States Army Corps of Engineers (USACE), NASA, and Geological Survey of Alabama (GSA) are employing a variety of in-situ and airborne based measurements to assess and monitor sediment loading and deposition. These methods provide highly accurate information but are limited in geographic range, are not continuous over a region and, in the case of airborne LIDAR are expensive and do not recur on a regular basis. Multi-temporal and multi-spectral satellite imagery that shows tropical-storm-induced suspended sediment and storm-surge sediment deposits can provide decision makers with immediate and long-term information about the impacts of tropical storms and hurricanes. It can also be valuable for those conducting research and for projects related to coastal issues such as recovery, planning, management, and mitigation. The recently awarded Sediment Analysis Network for Decision Support will generate decision support products using NASA satellite observations from MODIS, Landsat and SeaWiFS instruments to support resource management, planning, and decision making activities in the Gulf of Mexico. Specifically, SANDS will generate decision support products that address the impacts of tropical storms

  7. Functional centrality of amygdala, striatum and hypothalamus in a "small-world" network underlying joy: an fMRI study with music.

    Science.gov (United States)

    Koelsch, Stefan; Skouras, Stavros

    2014-07-01

    Current knowledge about small-world networks underlying emotions is sparse, and confined to functional magnetic resonance imaging (fMRI) studies using resting-state paradigms. This fMRI study applied Eigenvector Centrality Mapping (ECM) and functional connectivity analysis to reveal neural small-world networks underlying joy and fear. Joy and fear were evoked using music, presented in 4-min blocks. Results show that the superficial amygdala (SF), laterobasal amygdala (LB), striatum, and hypothalamus function as computational hubs during joy. Out of these computational hubs, the amygdala nuclei showed the highest centrality values. The SF showed functional connectivity during joy with the mediodorsal thalamus (MD) and nucleus accumbens (Nac), suggesting that SF, MD, and Nac modulate approach behavior in response to positive social signals such as joyful music. The striatum was functionally connected during joy with the LB, as well as with premotor cortex, areas 1 and 7a, hippocampus, insula and cingulate cortex, showing that sensorimotor, attentional, and emotional processes converge in the striatum during music perception. The hypothalamus showed functional connectivity during joy with hippocampus and MD, suggesting that hypothalamic endocrine activity is modulated by hippocampal and thalamic activity during sustained periods of music-evoked emotion. Our study indicates high centrality of the amygdala nuclei groups within a functional network underlying joy, suggesting that these nuclei play a central role for the modulation of emotion-specific activity within this network.

  8. Understanding resilience in industrial symbiosis networks: insights from network analysis.

    Science.gov (United States)

    Chopra, Shauhrat S; Khanna, Vikas

    2014-08-01

    Industrial symbiotic networks are based on the principles of ecological systems where waste equals food, to develop synergistic networks. For example, industrial symbiosis (IS) at Kalundborg, Denmark, creates an exchange network of waste, water, and energy among companies based on contractual dependency. Since most of the industrial symbiotic networks are based on ad-hoc opportunities rather than strategic planning, gaining insight into disruptive scenarios is pivotal for understanding the balance of resilience and sustainability and developing heuristics for designing resilient IS networks. The present work focuses on understanding resilience as an emergent property of an IS network via a network-based approach with application to the Kalundborg Industrial Symbiosis (KIS). Results from network metrics and simulated disruptive scenarios reveal Asnaes power plant as the most critical node in the system. We also observe a decrease in the vulnerability of nodes and reduction in single points of failure in the system, suggesting an increase in the overall resilience of the KIS system from 1960 to 2010. Based on our findings, we recommend design strategies, such as increasing diversity, redundancy, and multi-functionality to ensure flexibility and plasticity, to develop resilient and sustainable industrial symbiotic networks. Copyright © 2014 Elsevier Ltd. All rights reserved.

  9. Diffusion on social networks: Survey data from rural villages in central China

    Directory of Open Access Journals (Sweden)

    Hang Xiong

    2016-06-01

    Full Text Available Empirical studies on social diffusions are often restricted by the access to data of diffusion and social relations on the same objects. We present a set of first-hand data that we collected in ten rural villages in central China through household surveys. The dataset contains detailed and comprehensive data of the diffusion of an innovation, the major social relationships and the household level demographic characteristics in these villages. The data have been used to study peer effects in social diffusion using simulation models, “Peer Effects and Social Network: The Case of Rural Diffusion in Central China” [1]. They can also be used to estimate spatial econometric models. Data are supplied with this article.

  10. A Novel Hierarchical Semi-centralized Telemedicine Network Architecture Proposition for Bangladesh

    DEFF Research Database (Denmark)

    Choudhury, Samiul; Peterson, Carrie Beth; Kyriazakos, Sofoklis

    2011-01-01

    One of the major functions of telemedicine is the prompt delivery of modern healthcare to the remotest areas with reduced cost and efficient use of communication resources. The establishment of a well organized telemedicine system is therefore exigent for the developing countries like Bangladesh...... where there are extreme paucities of efficient healthcare professionals and equipments, specifically in the rural areas. In this paper a novel, hierarchical and semi-centralized telemedicine network architecture has been proposed holisti-cally focusing on the rural underdeveloped areas of Bangladesh...

  11. Synchronization analysis of coloured delayed networks under ...

    Indian Academy of Sciences (India)

    Up to now, many network models on synchronization have been put forward, such as, the small-world network, directed network, neural network etc. Previous efforts were mainly to study the outer relationship between the nodes. But, the inner interaction is always overlooked. Afterwards, the coloured network model has ...

  12. Network meta-analysis: an introduction for clinicians.

    Science.gov (United States)

    Rouse, Benjamin; Chaimani, Anna; Li, Tianjing

    2017-02-01

    Network meta-analysis is a technique for comparing multiple treatments simultaneously in a single analysis by combining direct and indirect evidence within a network of randomized controlled trials. Network meta-analysis may assist assessing the comparative effectiveness of different treatments regularly used in clinical practice and, therefore, has become attractive among clinicians. However, if proper caution is not taken in conducting and interpreting network meta-analysis, inferences might be biased. The aim of this paper is to illustrate the process of network meta-analysis with the aid of a working example on first-line medical treatment for primary open-angle glaucoma. We discuss the key assumption of network meta-analysis, as well as the unique considerations for developing appropriate research questions, conducting the literature search, abstracting data, performing qualitative and quantitative synthesis, presenting results, drawing conclusions, and reporting the findings in a network meta-analysis.

  13. Social Network Analysis: A Simple but Powerful Tool for Identifying Teacher Leaders

    Science.gov (United States)

    Smith, P. Sean; Trygstad, Peggy J.; Hayes, Meredith L.

    2018-01-01

    Instructional teacher leadership is central to a vision of distributed leadership. However, identifying instructional teacher leaders can be a daunting task, particularly for administrators who find themselves either newly appointed or faced with high staff turnover. This article describes the use of social network analysis (SNA), a simple but…

  14. How Relations are Built within a SNS World -- Social Network Analysis on Mixi --

    Science.gov (United States)

    Matsuo, Yutaka; Yasud, Yuki

    Our purpose here is to (1) investigate the structure of the personal networks developed on mixi, a Japanese social networking service (SNS), and (2) to consider the governing mechanism which guides participants of a SNS to form an aggregate network. Our findings are as follows:the clustering coefficient of the network is as high as 0.33 while the characteristic path lenght is as low as 5.5. A network among central users (over 300 edges) consist of two cliques, which seems to be very fragile. Community-affiliation network suggests there are several easy-entry communities which later lead users to more high-entry, unique-theme communities. The analysis on connectedness within a community reveals the importance of real-world interaction. Lastly, we depict a probable image of the entire ecology on {\\\\em mixi} among users and communities, which contributes broadly to social systems on the Web.

  15. Applications of social media and social network analysis

    CERN Document Server

    Kazienko, Przemyslaw

    2015-01-01

    This collection of contributed chapters demonstrates a wide range of applications within two overlapping research domains: social media analysis and social network analysis. Various methodologies were utilized in the twelve individual chapters including static, dynamic and real-time approaches to graph, textual and multimedia data analysis. The topics apply to reputation computation, emotion detection, topic evolution, rumor propagation, evaluation of textual opinions, friend ranking, analysis of public transportation networks, diffusion in dynamic networks, analysis of contributors to commun

  16. Network-based analysis of proteomic profiles

    KAUST Repository

    Wong, Limsoon

    2016-01-26

    Mass spectrometry (MS)-based proteomics is a widely used and powerful tool for profiling systems-wide protein expression changes. It can be applied for various purposes, e.g. biomarker discovery in diseases and study of drug responses. Although RNA-based high-throughput methods have been useful in providing glimpses into the underlying molecular processes, the evidences they provide are indirect. Furthermore, RNA and corresponding protein levels have been known to have poor correlation. On the other hand, MS-based proteomics tend to have consistency issues (poor reproducibility and inter-sample agreement) and coverage issues (inability to detect the entire proteome) that need to be urgently addressed. In this talk, I will discuss how these issues can be addressed by proteomic profile analysis techniques that use biological networks (especially protein complexes) as the biological context. In particular, I will describe several techniques that we have been developing for network-based analysis of proteomics profile. And I will present evidence that these techniques are useful in identifying proteomics-profile analysis results that are more consistent, more reproducible, and more biologically coherent, and that these techniques allow expansion of the detected proteome to uncover and/or discover novel proteins.

  17. Social sciences via network analysis and computation

    CERN Document Server

    Kanduc, Tadej

    2015-01-01

    In recent years information and communication technologies have gained significant importance in the social sciences. Because there is such rapid growth of knowledge, methods and computer infrastructure, research can now seamlessly connect interdisciplinary fields such as business process management, data processing and mathematics. This study presents some of the latest results, practices and state-of-the-art approaches in network analysis, machine learning, data mining, data clustering and classifications in the contents of social sciences. It also covers various real-life examples such as t

  18. Network analysis of physics discussion forums and links to course success

    Science.gov (United States)

    Traxler, Adrienne; Gavrin, Andrew; Lindell, Rebecca

    2017-01-01

    Large introductory science courses tend to isolate students, with negative consequences for long-term retention in college. Many active learning courses build collaboration and community among students as an explicit goal, and social network analysis has been used to track the development and beneficial effects of these collaborations. Here we supplement such work by conducting network analysis of online course discussion forums in two semesters of an introductory physics class. Online forums provide a tool for engaging students with each other outside of class, and offer new opportunities to commuter or non-traditional students with limited on-campus time. We look for correlations between position in the forum network (centrality) and final course grades. Preliminary investigation has shown weak correlations in the very dense full-semester network, so we will consider reduced ''backbone'' networks that highlight the most consistent links between students. Future work and implications for instruction will also be discussed.

  19. Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks.

    Science.gov (United States)

    Meyer-Bäse, Anke; Roberts, Rodney G; Illan, Ignacio A; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

    2017-01-01

    Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary

  20. Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks

    Directory of Open Access Journals (Sweden)

    Anke Meyer-Bäse

    2017-10-01

    Full Text Available Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and

  1. Community partnerships in healthy eating and lifestyle promotion: A network analysis

    Directory of Open Access Journals (Sweden)

    Ruopeng An

    2017-06-01

    Full Text Available Promoting healthy eating and lifestyles among populations with limited resources is a complex undertaking that often requires strong partnerships between various agencies. In local communities, these agencies are typically located in different areas, serve diverse subgroups, and operate distinct programs, limiting their communication and interactions with each other. This study assessed the network of agencies in local communities that promote healthy eating and lifestyles among populations with limited resources. Network surveys were administered in 2016 among 89 agencies located in 4 rural counties in Michigan that served limited-resource audiences. The agencies were categorized into 8 types: K-12 schools, early childhood centers, emergency food providers, health-related agencies, social resource centers, low-income/subsidized housing complexes, continuing education organizations, and others. Network analysis was conducted to examine 4 network structures—communication, funding, cooperation, and collaboration networks between agencies within each county. Agencies had a moderate level of cooperation, but were only loosely connected in the other 3 networks, indicated by low network density. Agencies in a network were decentralized rather than centralized around a few influential agencies, indicated by low centralization. There was evidence regarding homophily in a network, indicated by some significant correlations within agencies of the same type. Agencies connected in any one network were considerably more likely to be connected in all the other networks as well. In conclusion, promoting healthy eating and lifestyles among populations with limited resources warrants strong partnership between agencies in communities. Network analysis serves as a useful tool to evaluate community partnerships and facilitate coalition building.

  2. Models as Tools of Analysis of a Network Organisation

    Directory of Open Access Journals (Sweden)

    Wojciech Pająk

    2013-06-01

    Full Text Available The paper presents models which may be applied as tools of analysis of a network organisation. The starting point of the discussion is defining the following terms: supply chain and network organisation. Further parts of the paper present basic assumptions analysis of a network organisation. Then the study characterises the best known models utilised in analysis of a network organisation. The purpose of the article is to define the notion and the essence of network organizations and to present the models used for their analysis.

  3. Uncertainty analysis for seismic hazard in Northern and Central Italy

    Science.gov (United States)

    Lombardi, A.M.; Akinci, A.; Malagnini, L.; Mueller, C.S.

    2005-01-01

    In this study we examine uncertainty and parametric sensitivity of Peak Ground Acceleration (PGA) and 1-Hz Spectral Acceleration (1-Hz SA) in probabilistic seismic hazard maps (10% probability of exceedance in 50 years) of Northern and Central Italy. The uncertainty in hazard is estimated using a Monte Carlo approach to randomly sample a logic tree that has three input-variables branch points representing alternative values for b-value, maximum magnitude (Mmax) and attenuation relationships. Uncertainty is expressed in terms of 95% confidence band and Coefficient Of Variation (COV). The overall variability of ground motions and their sensitivity to each parameter of the logic tree are investigated. The largest values of the overall 95% confidence band are around 0.15 g for PGA in the Friuli and Northern Apennines regions and around 0.35 g for 1-Hz SA in the Central Apennines. The sensitivity analysis shows that the largest contributor to seismic hazard variability is uncertainty in the choice of ground-motion attenuation relationships, especially in the Friuli Region (???0.10 g) for PGA and in the Friuli and Central Apennines regions (???0.15 g) for 1-Hz SA. This is followed by the variability of the b-value: its main contribution is evident in the Friuli and Central Apennines regions for both 1-Hz SA (???0.15 g) and PGA (???0.10 g). We observe that the contribution of Mmax to seismic hazard variability is negligible, at least for 10% exceedance in 50-years hazard. The overall COV map for PGA shows that the uncertainty in the hazard is larger in the Friuli and Northern Apennine regions, around 20-30%, than the Central Apennines and Northwestern Italy, around 10-20%. The overall uncertainty is larger for the 1-Hz SA map and reaches 50-60% in the Central Apennines and Western Alps.

  4. Branch-based centralized data collection for smart grids using wireless sensor networks.

    Science.gov (United States)

    Kim, Kwangsoo; Jin, Seong-il

    2015-05-21

    A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network constructs into several branches. A conflict-free query schedule is generated based on the branches. Each power usage is collected according to the schedule. The proposed method has important features: shortening query processing time and avoiding collisions between a query and query responses. We evaluate this method using the ns-2 simulator. The experimental results show that this method can achieve both collision avoidance and fast query processing at the same time. The success rate of data collection at a sink node executing this method is 100%. Its running time is about 35 percent faster than that of the round-robin method, and its memory size is reduced to about 10% of that of the depth-first search method.

  5. Branch-Based Centralized Data Collection for Smart Grids Using Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Kwangsoo Kim

    2015-05-01

    Full Text Available A smart grid is one of the most important applications in smart cities. In a smart grid, a smart meter acts as a sensor node in a sensor network, and a central device collects power usage from every smart meter. This paper focuses on a centralized data collection problem of how to collect every power usage from every meter without collisions in an environment in which the time synchronization among smart meters is not guaranteed. To solve the problem, we divide a tree that a sensor network constructs into several branches. A conflict-free query schedule is generated based on the branches. Each power usage is collected according to the schedule. The proposed method has important features: shortening query processing time and avoiding collisions between a query and query responses. We evaluate this method using the ns-2 simulator. The experimental results show that this method can achieve both collision avoidance and fast query processing at the same time. The success rate of data collection at a sink node executing this method is 100%. Its running time is about 35 percent faster than that of the round-robin method, and its memory size is reduced to about 10% of that of the depth-first search method.

  6. Analysis of regulatory networks constructed based on gene ...

    Indian Academy of Sciences (India)

    Gene coexpression patterns can reveal gene collections with functional consistency. This study systematically constructs regulatory networks for pituitary tumours by integrating gene coexpression, transcriptional and posttranscriptional regulation. Through network analysis, we elaborate the incidence mechanism of pituitary ...

  7. Analysis of regulatory networks constructed based on gene ...

    Indian Academy of Sciences (India)

    2013-12-09

    Dec 9, 2013 ... Abstract. Gene coexpression patterns can reveal gene collections with functional consistency. This study systematically constructs regulatory networks for pituitary tumours by integrating gene coexpression, transcriptional and posttranscriptional regulation. Through network analysis, we elaborate the ...

  8. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

    Directory of Open Access Journals (Sweden)

    Kovaleva Galina

    2011-06-01

    Full Text Available Abstract Background Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. Results To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. Multiple variations in regulatory strategies between the Shewanella spp. and E. coli include regulon contraction and expansion (as in the case of PdhR, HexR, FadR, numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. PsrA for fatty acid degradation and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp. Conclusions We tentatively defined the first reference collection of ~100 transcriptional regulons in 16 Shewanella genomes. The resulting regulatory network contains ~600 regulated genes per genome that are mostly involved in metabolism of carbohydrates, amino acids, fatty acids, vitamins, metals, and stress responses. Several reconstructed regulons including NagR for N-acetylglucosamine catabolism were experimentally validated in S

  9. Robustness Analysis of Real Network Topologies Under Multiple Failure Scenarios

    DEFF Research Database (Denmark)

    Manzano, M.; Marzo, J. L.; Calle, E.

    2012-01-01

    on topological characteristics. Recently approaches also consider the services supported by such networks. In this paper we carry out a robustness analysis of five real backbone telecommunication networks under defined multiple failure scenarios, taking into account the consequences of the loss of established......Nowadays the ubiquity of telecommunication networks, which underpin and fulfill key aspects of modern day living, is taken for granted. Significant large-scale failures have occurred in the last years affecting telecommunication networks. Traditionally, network robustness analysis has been focused...... connections. Results show which networks are more robust in response to a specific type of failure....

  10. Identifying changes in the support networks of end-of-life carers using social network analysis.

    Science.gov (United States)

    Leonard, Rosemary; Horsfall, Debbie; Noonan, Kerrie

    2015-06-01

    End-of-life caring is often associated with reduced social networks for both the dying person and for the carer. However, those adopting a community participation and development approach, see the potential for the expansion and strengthening of networks. This paper uses Knox, Savage and Harvey's definitions of three generations social network analysis to analyse the caring networks of people with a terminal illness who are being cared for at home and identifies changes in these caring networks that occurred over the period of caring. Participatory network mapping of initial and current networks was used in nine focus groups. The analysis used key concepts from social network analysis (size, density, transitivity, betweenness and local clustering) together with qualitative analyses of the group's reflections on the maps. The results showed an increase in the size of the networks and that ties between the original members of the network strengthened. The qualitative data revealed the importance between core and peripheral network members and the diverse contributions of the network members. The research supports the value of third generation social network analysis and the potential for end-of-life caring to build social capital. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

  11. Parameters of Regional Cooperative Behavior in the German Biotech Industry – A Quantitative Social Network Analysis

    DEFF Research Database (Denmark)

    Mitze, Timo; Strotebeck, Falk

    We analyse the determinants of network formation in Germany’s biotechnology industry using social network analysis combined with a regression approach for count data. Outcome variable of interest is the degree centrality of German regions, which is specified as a function of the region’s innovative...... and economic performance as well as biotech-related policy variables. The inclusion of the latter allows us to shed new light on the question to what extent R&D-based cluster policies are able to impact on the formation of the German biotech network. Our results show that policy indicators such as the volume...

  12. Advantages of Social Network Analysis in Educational Research

    Science.gov (United States)

    Ushakov, K. M.; Kukso, K. N.

    2015-01-01

    Currently one of the main tools for the large scale studies of schools is statistical analysis. Although it is the most common method and it offers greatest opportunities for analysis, there are other quantitative methods for studying schools, such as network analysis. We discuss the potential advantages that network analysis has for educational…

  13. Exercise-induced neuronal plasticity in central autonomic networks: role in cardiovascular control.

    Science.gov (United States)

    Michelini, Lisete C; Stern, Javier E

    2009-09-01

    It is now well established that brain plasticity is an inherent property not only of the developing but also of the adult brain. Numerous beneficial effects of exercise, including improved memory, cognitive function and neuroprotection, have been shown to involve an important neuroplastic component. However, whether major adaptive cardiovascular adjustments during exercise, needed to ensure proper blood perfusion of peripheral tissues, also require brain neuroplasticity, is presently unknown. This review will critically evaluate current knowledge on proposed mechanisms that are likely to underlie the continuous resetting of baroreflex control of heart rate during/after exercise and following exercise training. Accumulating evidence indicates that not only somatosensory afferents (conveyed by skeletal muscle receptors, baroreceptors and/or cardiopulmonary receptors) but also projections arising from central command neurons (in particular, peptidergic hypothalamic pre-autonomic neurons) converge into the nucleus tractus solitarii (NTS) in the dorsal brainstem, to co-ordinate complex cardiovascular adaptations during dynamic exercise. This review focuses in particular on a reciprocally interconnected network between the NTS and the hypothalamic paraventricular nucleus (PVN), which is proposed to act as a pivotal anatomical and functional substrate underlying integrative feedforward and feedback cardiovascular adjustments during exercise. Recent findings supporting neuroplastic adaptive changes within the NTS-PVN reciprocal network (e.g. remodelling of afferent inputs, structural and functional neuronal plasticity and changes in neurotransmitter content) will be discussed within the context of their role as important underlying cellular mechanisms supporting the tonic activation and improved efficacy of these central pathways in response to circulatory demand at rest and during exercise, both in sedentary and in trained individuals. We hope this review will stimulate

  14. Handling transmission limitations in the central power network; Haandtering av overfoeringsbegrensninger

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1998-12-01

    From 1996, the Norwegian and Swedish power markets were joined and a common power exchange was established. The two countries deal differently with bottlenecks (transmission obstruction) in their central networks. This report compares methods for dealing with such bottlenecks and looks at the alternatives. It emphasises the efficiency of pricing and incentives and the possibility of exercising market power under the different methods. Norway uses a method of price regions, or bottleneck tax. Prices are determined for the various price regions so as to keep the power flow below specified bounds. A surplus region is assigned a lower price than a deficit region and the bottleneck tax is the difference in price between two such price regions. The Swedish system is based on a counter purchase concept. In his offer to the spotmarket, the supplier has bound himself to provide a certain amount to the current system price regardless of network limitations. Up-regulation means that he produces more than this amount. Down-regulation means that he is paid for supplying less than he had offered to the current system price. In up- or down-regulation, compensation is given as the difference between the system price and the price on the counter purchase market. The main conclusions are: (1) Counter purchase is unsuitable as the main strategy for Norway. (2) Counter purchase may be suitable with short-lived and unpredicted bottlenecks; price regions may be suitable for long-lasting and predicted bottlenecks. Time is a central factor. (3) Present-day models for bottleneck management in Norway and Sweden do not give the optimum short-term load distribution on the network. In general, the current Norwegian system works fairly well, although it might be worthwhile to consider a system that approaches node pricing. 3 refs., 34 figs., 3 tabs.

  15. Inverse problem and variation method to optimize cascade heat exchange network in central heating system

    Science.gov (United States)

    Zhang, Yin; Wei, Zhiyuan; Zhang, Yinping; Wang, Xin

    2017-12-01

    Urban heating in northern China accounts for 40% of total building energy usage. In central heating systems, heat is often transferred from heat source to users by the heat network where several heat exchangers are installed at heat source, substations and terminals respectively. For given overall heating capacity and heat source temperature, increasing the terminal fluid temperature is an effective way to improve the thermal performance of such cascade heat exchange network for energy saving. In this paper, the mathematical optimization model of the cascade heat exchange network with three-stage heat exchangers in series is established. Aim at maximizing the cold fluid temperature for given hot fluid temperature and overall heating capacity, the optimal heat exchange area distribution and the medium fluids' flow rates are determined through inverse problem and variation method. The preliminary results show that the heat exchange areas should be distributed equally for each heat exchanger. It also indicates that in order to improve the thermal performance of the whole system, more heat exchange areas should be allocated to the heat exchanger where flow rate difference between two fluids is relatively small. This work is important for guiding the optimization design of practical cascade heating systems.

  16. A Centralized Energy Efficient Distance Based Routing Protocol for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Rohit D. Gawade

    2016-01-01

    Full Text Available Wireless sensor network (WSN typically consists of a large number of low cost wireless sensor nodes which collect and send various messages to a base station (BS. WSN nodes are small battery powered devices having limited energy resources. Replacement of such energy resources is not easy for thousands of nodes as they are inaccessible to users after their deployment. This generates a requirement of energy efficient routing protocol for increasing network lifetime while minimizing energy consumption. Low Energy Adaptive Clustering Hierarchy (LEACH is a widely used classic clustering algorithm in WSNs. In this paper, we propose a Centralized Energy Efficient Distance (CEED based routing protocol to evenly distribute energy dissipation among all sensor nodes. We calculate optimum number of cluster heads based on LEACH’s energy dissipation model. We propose a distributed cluster head selection algorithm based on dissipated energy of a node and its distance to BS. Moreover, we extend our protocol by multihop routing scheme to reduce energy dissipated by nodes located far away from base station. The performance of CEED is compared with other protocols such as LEACH and LEACH with Distance Based Thresholds (LEACH-DT. Simulation results show that CEED is more energy efficient as compared to other protocols. Also it improves the network lifetime and stability period over the other protocols.

  17. NetworkAnalyst--integrative approaches for protein-protein interaction network analysis and visual exploration.

    Science.gov (United States)

    Xia, Jianguo; Benner, Maia J; Hancock, Robert E W

    2014-07-01

    Biological network analysis is a powerful approach to gain systems-level understanding of patterns of gene expression in different cell types, disease states and other biological/experimental conditions. Three consecutive steps are required--identification of genes or proteins of interest, network construction and network analysis and visualization. To date, researchers have to learn to use a combination of several tools to accomplish this task. In addition, interactive visualization of large networks has been primarily restricted to locally installed programs. To address these challenges, we have developed NetworkAnalyst, taking advantage of state-of-the-art web technologies, to enable high performance network analysis with rich user experience. NetworkAnalyst integrates all three steps and presents the results via a powerful online network visualization framework. Users can upload gene or protein lists, single or multiple gene expression datasets to perform comprehensive gene annotation and differential expression analysis. Significant genes are mapped to our manually curated protein-protein interaction database to construct relevant networks. The results are presented through standard web browsers for network analysis and interactive exploration. NetworkAnalyst supports common functions for network topology and module analyses. Users can easily search, zoom and highlight nodes or modules, as well as perform functional enrichment analysis on these selections. The networks can be customized with different layouts, colors or node sizes, and exported as PNG, PDF or GraphML files. Comprehensive FAQs, tutorials and context-based tips and instructions are provided. NetworkAnalyst currently supports protein-protein interaction network analysis for human and mouse and is freely available at http://www.networkanalyst.ca. © The Author(s) 2014. Published by Oxford University Press on behalf of Nucleic Acids Research.

  18. Examining the central and peripheral processes of written word production through meta-analysis

    Directory of Open Access Journals (Sweden)

    Jeremy ePurcell

    2011-10-01

    Full Text Available Producing written words requires central cognitive processes (such as orthographic long-term and working memory as well as more peripheral processes responsible for generating the motor actions needed for producing written words in a variety of formats (handwriting, typing, etc.. In recent years, various functional neuroimaging studies have examined the neural substrates underlying the central and peripheral processes of written word production. This study provides the first quantitative meta-analysis of these studies by applying Activation Likelihood Estimation methods (Turkeltaub et al., 2002. For alphabet languages, we identified 11 studies (with a total of 17 experimental contrasts that had been designed to isolate central and/or peripheral processes of word spelling (total number of participants = 146. Three ALE meta-analyses were carried out. One involved the complete set of 17 contrasts; two others were applied to subsets of contrasts to distinguish the neural substrates of central from peripheral processes. These analyses identified a network of brain regions reliably associated with the central and peripheral processes of word spelling. Among the many significant results, is the finding that the regions with the greatest correspondence across studies were in the left inferior temporal/fusiform gyri and left inferior frontal gyrus. Furthermore, although the angular gyrus has traditionally been identified as a key site within the written word production network, none of the meta-analyses found it to be a consistent site of activation, identifying instead a region just superior/medial to the left angular gyrus in the left posterior intraparietal sulcus. In general these meta-analyses and the discussion of results provide a valuable foundation upon which future studies that examine the neural basis of written word production can build.

  19. Co-occurrence network analysis of Chinese and English poems

    Science.gov (United States)

    Liang, Wei; Wang, Yanli; Shi, Yuming; Chen, Guanrong

    2015-02-01

    A total of 572 co-occurrence networks of Chinese characters and words as well as English words are constructed from both Chinese and English poems. It is found that most of the networks have small-world features; more Chinese networks have scale-free properties and hierarchical structures as compared with the English networks; all the networks are disassortative, and the disassortativeness of the Chinese word networks is more prominent than those of the English networks; the spectral densities of the Chinese word networks and English networks are similar, but they are different from those of the ER, BA, and WS networks. For the above observed phenomena, analysis is provided with interpretation from a linguistic perspective.

  20. Comparative analysis of quantitative efficiency evaluation methods for transportation networks.

    Science.gov (United States)

    He, Yuxin; Qin, Jin; Hong, Jian

    2017-01-01

    An effective evaluation of transportation network efficiency could offer guidance for the optimal control of urban traffic. Based on the introduction and related mathematical analysis of three quantitative evaluation methods for transportation network efficiency, this paper compares the information measured by them, including network structure, traffic demand, travel choice behavior and other factors which affect network efficiency. Accordingly, the applicability of various evaluation methods is discussed. Through analyzing different transportation network examples it is obtained that Q-H method could reflect the influence of network structure, traffic demand and user route choice behavior on transportation network efficiency well. In addition, the transportation network efficiency measured by this method and Braess's Paradox can be explained with each other, which indicates a better evaluation of the real operation condition of transportation network. Through the analysis of the network efficiency calculated by Q-H method, it can also be drawn that a specific appropriate demand is existed to a given transportation network. Meanwhile, under the fixed demand, both the critical network structure that guarantees the stability and the basic operation of the network and a specific network structure contributing to the largest value of the transportation network efficiency can be identified.

  1. Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada

    Directory of Open Access Journals (Sweden)

    Christina C. Loitz

    2017-08-01

    Full Text Available Abstract Background Coordinated partnerships and collaborations can optimize the efficiency and effectiveness of service and program delivery in organizational networks. However, the extent to which organizations are working together to promote physical activity, and use physical activity policies in Canada, is unknown. This project sought to provide a snapshot of the funding, coordination and partnership relationships among provincial active living organizations (ALOs in Alberta, Canada. Additionally, the awareness, and use of the provincial policy and national strategy by the organizations was examined. Methods Provincial ALOs (N = 27 answered questions regarding their funding, coordination and partnership connections with other ALOs in the network. Social network analysis was employed to examine network structure and position of each ALO. Discriminant function analysis determined the extent to which degree centrality was associated with the use of the Active Alberta (AA policy and Active Canada 20/20 (AC 20/20 strategy. Results The funding network had a low density level (density = .20 and was centralized around Alberta Tourism Parks and Recreation (ATPR; degree centralization = 48.77%, betweenness centralization = 32.43%. The coordination network had a moderate density level (density = .31, and was low-to-moderately centralized around a few organizations (degree centralization = 45.37%, betweenness centrality = 19.92%. The partnership network had a low density level (density = .15, and was moderate-to-highly centralized around ATPR. Most organizations were aware of AA (89% and AC 20/20 (78%, however more were using AA (67% compared to AC 20/20 (33%. Central ALOs in the funding network were more likely to use AA and AC 20/20. Central ALOs in the coordination network were more likely to use AC 20/20, but not AA. Conclusions Increasing formal and informal relationships between organizations and integrating disconnected or

  2. Network analysis of inter-organizational relationships and policy use among active living organizations in Alberta, Canada.

    Science.gov (United States)

    Loitz, Christina C; Stearns, Jodie A; Fraser, Shawn N; Storey, Kate; Spence, John C

    2017-08-09

    Coordinated partnerships and collaborations can optimize the efficiency and effectiveness of service and program delivery in organizational networks. However, the extent to which organizations are working together to promote physical activity, and use physical activity policies in Canada, is unknown. This project sought to provide a snapshot of the funding, coordination and partnership relationships among provincial active living organizations (ALOs) in Alberta, Canada. Additionally, the awareness, and use of the provincial policy and national strategy by the organizations was examined. Provincial ALOs (N = 27) answered questions regarding their funding, coordination and partnership connections with other ALOs in the network. Social network analysis was employed to examine network structure and position of each ALO. Discriminant function analysis determined the extent to which degree centrality was associated with the use of the Active Alberta (AA) policy and Active Canada 20/20 (AC 20/20) strategy. The funding network had a low density level (density = .20) and was centralized around Alberta Tourism Parks and Recreation (ATPR; degree centralization = 48.77%, betweenness centralization = 32.43%). The coordination network had a moderate density level (density = .31), and was low-to-moderately centralized around a few organizations (degree centralization = 45.37%, betweenness centrality = 19.92%). The partnership network had a low density level (density = .15), and was moderate-to-highly centralized around ATPR. Most organizations were aware of AA (89%) and AC 20/20 (78%), however more were using AA (67%) compared to AC 20/20 (33%). Central ALOs in the funding network were more likely to use AA and AC 20/20. Central ALOs in the coordination network were more likely to use AC 20/20, but not AA. Increasing formal and informal relationships between organizations and integrating disconnected or peripheral organizations could increase the capacity of the

  3. Par@Graph - a parallel toolbox for the construction and analysis of large complex climate networks

    Science.gov (United States)

    Ihshaish, H.; Tantet, A.; Dijkzeul, J. C. M.; Dijkstra, H. A.

    2015-10-01

    In this paper, we present Par@Graph, a software toolbox to reconstruct and analyze complex climate networks having a large number of nodes (up to at least 106) and edges (up to at least 1012). The key innovation is an efficient set of parallel software tools designed to leverage the inherited hybrid parallelism in distributed-memory clusters of multi-core machines. The performance of the toolbox is illustrated through networks derived from sea surface height (SSH) data of a global high-resolution ocean model. Less than 8 min are needed on 90 Intel Xeon E5-4650 processors to reconstruct a climate network including the preprocessing and the correlation of 3 × 105 SSH time series, resulting in a weighted graph with the same number of vertices and about 3.2 × 108 edges. In less than 14 min on 30 processors, the resulted graph's degree centrality, strength, connected components, eigenvector centrality, entropy and clustering coefficient metrics were obtained. These results indicate that a complete cycle to construct and analyze a large-scale climate network is available under 22 min Par@Graph therefore facilitates the application of climate network analysis on high-resolution observations and model results, by enabling fast network reconstruct from the calculation of statistical similarities between climate time series. It also enables network analysis at unprecedented scales on a variety of different sizes of input data sets.

  4. Analysis and monitoring design for networks

    Energy Technology Data Exchange (ETDEWEB)

    Fedorov, V.; Flanagan, D.; Rowan, T.; Batsell, S.

    1998-06-01

    The idea of applying experimental design methodologies to develop monitoring systems for computer networks is relatively novel even though it was applied in other areas such as meteorology, seismology, and transportation. One objective of a monitoring system should always be to collect as little data as necessary to be able to monitor specific parameters of the system with respect to assigned targets and objectives. This implies a purposeful monitoring where each piece of data has a reason to be collected and stored for future use. When a computer network system as large and complex as the Internet is the monitoring subject, providing an optimal and parsimonious observing system becomes even more important. Many data collection decisions must be made by the developers of a monitoring system. These decisions include but are not limited to the following: (1) The type data collection hardware and software instruments to be used; (2) How to minimize interruption of regular network activities during data collection; (3) Quantification of the objectives and the formulation of optimality criteria; (4) The placement of data collection hardware and software devices; (5) The amount of data to be collected in a given time period, how large a subset of the available data to collect during the period, the length of the period, and the frequency of data collection; (6) The determination of the data to be collected (for instance, selection of response and explanatory variables); (7) Which data will be retained and how long (i.e., data storage and retention issues); and (8) The cost analysis of experiments. Mathematical statistics, and, in particular, optimal experimental design methods, may be used to address the majority of problems generated by 3--7. In this study, the authors focus their efforts on topics 3--5.

  5. Social Network Analysis and Qualitative Interviews for Assessing Geographic Characteristics of Tourism Business Networks

    National Research Council Canada - National Science Library

    Kelman, Ilan; Luthe, Tobias; Wyss, Romano; Tørnblad, Silje H; Evers, Yvette; Curran, Marina Martin; Williams, Richard J; Berlow, Eric L

    2016-01-01

    This study integrates quantitative social network analysis (SNA) and qualitative interviews for understanding tourism business links in isolated communities through analysing spatial characteristics...

  6. 6th International Conference on Network Analysis

    CERN Document Server

    Nikolaev, Alexey; Pardalos, Panos; Prokopyev, Oleg

    2017-01-01

    This valuable source for graduate students and researchers provides a comprehensive introduction to current theories and applications in optimization methods and network models. Contributions to this book are focused on new efficient algorithms and rigorous mathematical theories, which can be used to optimize and analyze mathematical graph structures with massive size and high density induced by natural or artificial complex networks. Applications to social networks, power transmission grids, telecommunication networks, stock market networks, and human brain networks are presented. Chapters in this book cover the following topics: Linear max min fairness Heuristic approaches for high-quality solutions Efficient approaches for complex multi-criteria optimization problems Comparison of heuristic algorithms New heuristic iterative local search Power in network structures Clustering nodes in random graphs Power transmission grid structure Network decomposition problems Homogeneity hypothesis testing Network analy...

  7. Integrated in silico Analyses of Regulatory and Metabolic Networks of Synechococcus sp. PCC 7002 Reveal Relationships between Gene Centrality and Essentiality

    Directory of Open Access Journals (Sweden)

    Hyun-Seob Song

    2015-03-01

    Full Text Available Cyanobacteria dynamically relay environmental inputs to intracellular adaptations through a coordinated adjustment of photosynthetic efficiency and carbon processing rates. The output of such adaptations is reflected through changes in transcriptional patterns and metabolic flux distributions that ultimately define growth strategy. To address interrelationships between metabolism and regulation, we performed integrative analyses of metabolic and gene co-expression networks in a model cyanobacterium, Synechococcus sp. PCC 7002. Centrality analyses using the gene co-expression network identified a set of key genes, which were defined here as “topologically important.” Parallel in silico gene knock-out simulations, using the genome-scale metabolic network, classified what we termed as “functionally important” genes, deletion of which affected growth or metabolism. A strong positive correlation was observed between topologically and functionally important genes. Functionally important genes exhibited variable levels of topological centrality; however, the majority of topologically central genes were found to be functionally essential for growth. Subsequent functional enrichment analysis revealed that both functionally and topologically important genes in Synechococcus sp. PCC 7002 are predominantly associated with translation and energy metabolism, two cellular processes critical for growth. This research demonstrates how synergistic network-level analyses can be used for reconciliation of metabolic and gene expression data to uncover fundamental biological principles.

  8. Quantitative methods for ecological network analysis.

    Science.gov (United States)

    Ulanowicz, Robert E

    2004-12-01

    The analysis of networks of ecological trophic transfers is a useful complement to simulation modeling in the quest for understanding whole-ecosystem dynamics. Trophic networks can be studied in quantitative and systematic fashion at several levels. Indirect relationships between any two individual taxa in an ecosystem, which often differ in either nature or magnitude from their direct influences, can be assayed using techniques from linear algebra. The same mathematics can also be employed to ascertain where along the trophic continuum any individual taxon is operating, or to map the web of connections into a virtual linear chain that summarizes trophodynamic performance by the system. Backtracking algorithms with pruning have been written which identify pathways for the recycle of materials and energy within the system. The pattern of such cycling often reveals modes of control or types of functions exhibited by various groups of taxa. The performance of the system as a whole at processing material and energy can be quantified using information theory. In particular, the complexity of process interactions can be parsed into separate terms that distinguish organized, efficient performance from the capacity for further development and recovery from disturbance. Finally, the sensitivities of the information-theoretic system indices appear to identify the dynamical bottlenecks in ecosystem functioning.

  9. Analysis of gene regulatory networks in the mammalian circadian rhythm.

    Directory of Open Access Journals (Sweden)

    Jun Yan

    2008-10-01

    Full Text Available Circadian rhythm is fundamental in regulating a wide range of cellular, metabolic, physiological, and behavioral activities in mammals. Although a small number of key circadian genes have been identified through extensive molecular and genetic studies in the past, the existence of other key circadian genes and how they drive the genomewide circadian oscillation of gene expression in different tissues still remains unknown. Here we try to address these questions by integrating all available circadian microarray data in mammals. We identified 41 common circadian genes that showed circadian oscillation in a wide range of mouse tissues with a remarkable consistency of circadian phases across tissues. Comparisons across mouse, rat, rhesus macaque, and human showed that the circadian phases of known key circadian genes were delayed for 4-5 hours in rat compared to mouse and 8-12 hours in macaque and human compared to mouse. A systematic gene regulatory network for the mouse circadian rhythm was constructed after incorporating promoter analysis and transcription factor knockout or mutant microarray data. We observed the significant association of cis-regulatory elements: EBOX, DBOX, RRE, and HSE with the different phases of circadian oscillating genes. The analysis of the network structure revealed the paths through which light, food, and heat can entrain the circadian clock and identified that NR3C1 and FKBP/HSP90 complexes are central to the control of circadian genes through diverse environmental signals. Our study improves our understanding of the structure, design principle, and evolution of gene regulatory networks involved in the mammalian circadian rhythm.

  10. Network analysis of translocated Takahe populations to identify disease surveillance targets.

    Science.gov (United States)

    Grange, Zoë L; VAN Andel, Mary; French, Nigel P; Gartrell, Brett D

    2014-04-01

    Social network analysis is being increasingly used in epidemiology and disease modeling in humans, domestic animals, and wildlife. We investigated this tool in describing a translocation network (area that allows movement of animals between geographically isolated locations) used for the conservation of an endangered flightless rail, the Takahe (Porphyrio hochstetteri). We collated records of Takahe translocations within New Zealand and used social network principles to describe the connectivity of the translocation network. That is, networks were constructed and analyzed using adjacency matrices with values based on the tie weights between nodes. Five annual network matrices were created using the Takahe data set, each incremental year included records of previous years. Weights of movements between connected locations were assigned by the number of Takahe moved. We calculated the number of nodes (i(total)) and the number of ties (t(total)) between the nodes. To quantify the small-world character of the networks, we compared the real networks to random graphs of the equivalent size, weighting, and node strength. Descriptive analysis of cumulative annual Takahe movement networks involved determination of node-level characteristics, including centrality descriptors of relevance to disease modeling such as weighted measures of in degree (k(i)(in)), out degree (k(i)(out)), and betweenness (B(i)). Key players were assigned according to the highest node measure of k(i)(in), k(i)(out), and B(i) per network. Networks increased in size throughout the time frame considered. The network had some degree small-world characteristics. Nodes with the highest cumulative tie weights connecting them were the captive breeding center, the Murchison Mountains and 2 offshore islands. The key player fluctuated between the captive breeding center and the Murchison Mountains. The cumulative networks identified the captive breeding center every year as the hub of the network until the final

  11. Dynamic analysis of biochemical network using complex network method

    Directory of Open Access Journals (Sweden)

    Wang Shuqiang

    2015-01-01

    Full Text Available In this study, the stochastic biochemical reaction model is proposed based on the law of mass action and complex network theory. The dynamics of biochemical reaction system is presented as a set of non-linear differential equations and analyzed at the molecular-scale. Given the initial state and the evolution rules of the biochemical reaction system, the system can achieve homeostasis. Compared with random graph, the biochemical reaction network has larger information capacity and is more efficient in information transmission. This is consistent with theory of evolution.

  12. A GRASS GIS Semi-Stochastic Model for Evaluating the Probability of Landslides Impacting Road Networks in Collazzone, Central Italy

    Science.gov (United States)

    Taylor, Faith E.; Santangelo, Michele; Marchesini, Ivan; Malamud, Bruce D.

    2013-04-01

    During a landslide triggering event, the tens to thousands of landslides resulting from the trigger (e.g., earthquake, heavy rainfall) may block a number of sections of the road network, posing a risk to rescue efforts, logistics and accessibility to a region. Here, we present initial results from a semi-stochastic model we are developing to evaluate the probability of landslides intersecting a road network and the network-accessibility implications of this across a region. This was performed in the open source GRASS GIS software, where we took 'model' landslides and dropped them on a 79 km2 test area region in Collazzone, Umbria, Central Italy, with a given road network (major and minor roads, 404 km in length) and already determined landslide susceptibilities. Landslide areas (AL) were randomly selected from a three-parameter inverse gamma probability density function, consisting of a power-law decay of about -2.4 for medium and large values of AL and an exponential rollover for small values of AL; the rollover (maximum probability) occurs at about AL = 400 m.2 The number of landslide areas selected for each triggered event iteration was chosen to have an average density of 1 landslide km-2, i.e. 79 landslide areas chosen randomly for each iteration. Landslides were then 'dropped' over the region semi-stochastically: (i) random points were generated across the study region; (ii) based on the landslide susceptibility map, points were accepted/rejected based on the probability of a landslide occurring at that location. After a point was accepted, it was assigned a landslide area (AL) and length to width ratio. Landslide intersections with roads were then assessed and indices such as the location, number and size of road blockage recorded. The GRASS-GIS model was performed 1000 times in a Monte-Carlo type simulation. Initial results show that for a landslide triggering event of 1 landslide km-2 over a 79 km2 region with 404 km of road, the number of road blockages

  13. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Science.gov (United States)

    Castet, Jean-Francois; Saleh, Joseph H

    2013-01-01

    This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs) allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats) of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also examined, and the

  14. Interdependent multi-layer networks: modeling and survivability analysis with applications to space-based networks.

    Directory of Open Access Journals (Sweden)

    Jean-Francois Castet

    Full Text Available This article develops a novel approach and algorithmic tools for the modeling and survivability analysis of networks with heterogeneous nodes, and examines their application to space-based networks. Space-based networks (SBNs allow the sharing of spacecraft on-orbit resources, such as data storage, processing, and downlink. Each spacecraft in the network can have different subsystem composition and functionality, thus resulting in node heterogeneity. Most traditional survivability analyses of networks assume node homogeneity and as a result, are not suited for the analysis of SBNs. This work proposes that heterogeneous networks can be modeled as interdependent multi-layer networks, which enables their survivability analysis. The multi-layer aspect captures the breakdown of the network according to common functionalities across the different nodes, and it allows the emergence of homogeneous sub-networks, while the interdependency aspect constrains the network to capture the physical characteristics of each node. Definitions of primitives of failure propagation are devised. Formal characterization of interdependent multi-layer networks, as well as algorithmic tools for the analysis of failure propagation across the network are developed and illustrated with space applications. The SBN applications considered consist of several networked spacecraft that can tap into each other's Command and Data Handling subsystem, in case of failure of its own, including the Telemetry, Tracking and Command, the Control Processor, and the Data Handling sub-subsystems. Various design insights are derived and discussed, and the capability to perform trade-space analysis with the proposed approach for various network characteristics is indicated. The select results here shown quantify the incremental survivability gains (with respect to a particular class of threats of the SBN over the traditional monolith spacecraft. Failure of the connectivity between nodes is also

  15. Evolutionary analysis and interaction prediction for protein-protein interaction network in geometric space.

    Science.gov (United States)

    Huang, Lei; Liao, Li; Wu, Cathy H

    2017-01-01

    Prediction of protein-protein interaction (PPI) remains a central task in systems biology. With more PPIs identified, forming PPI networks, it has become feasible and also imperative to study PPIs at the network level, such as evolutionary analysis of the networks, for better understanding of PPI networks and for more accurate prediction of pairwise PPIs by leveraging the information gained at the network level. In this work we developed a novel method that enables us to incorporate evolutionary information into geometric space to improve PPI prediction, which in turn can be used to select and evaluate various evolutionary models. The method is tested with cross-validation using human PPI network and yeast PPI network data. The results show that the accuracy of PPI prediction measured by ROC score is increased by up to 14.6%, as compared to a baseline without using evolutionary information. The results also indicate that our modified evolutionary model DANEOsf-combining a gene duplication/neofunctionalization model and scale-free model-has a better fitness and prediction efficacy for these two PPI networks. The improved PPI prediction performance may suggest that our DANEOsf evolutionary model can uncover the underlying evolutionary mechanism for these two PPI networks better than other tested models. Consequently, of particular importance is that our method offers an effective way to select evolutionary models that best capture the underlying evolutionary mechanisms, evaluating the fitness of evolutionary models from the perspective of PPI prediction on real PPI networks.

  16. [Semantic Network Analysis of Online News and Social Media Text Related to Comprehensive Nursing Care Service].

    Science.gov (United States)

    Kim, Minji; Choi, Mona; Youm, Yoosik

    2017-12-01

    As comprehensive nursing care service has gradually expanded, it has become necessary to explore the various opinions about it. The purpose of this study is to explore the large amount of text data regarding comprehensive nursing care service extracted from online news and social media by applying a semantic network analysis. The web pages of the Korean Nurses Association (KNA) News, major daily newspapers, and Twitter were crawled by searching the keyword 'comprehensive nursing care service' using Python. A morphological analysis was performed using KoNLPy. Nodes on a 'comprehensive nursing care service' cluster were selected, and frequency, edge weight, and degree centrality were calculated and visualized with Gephi for the semantic network. A total of 536 news pages and 464 tweets were analyzed. In the KNA News and major daily newspapers, 'nursing workforce' and 'nursing service' were highly rated in frequency, edge weight, and degree centrality. On Twitter, the most frequent nodes were 'National Health Insurance Service' and 'comprehensive nursing care service hospital.' The nodes with the highest edge weight were 'national health insurance,' 'wards without caregiver presence,' and 'caregiving costs.' 'National Health Insurance Service' was highest in degree centrality. This study provides an example of how to use atypical big data for a nursing issue through semantic network analysis to explore diverse perspectives surrounding the nursing community through various media sources. Applying semantic network analysis to online big data to gather information regarding various nursing issues would help to explore opinions for formulating and implementing nursing policies.

  17. Analysis of Computer Network Information Based on "Big Data"

    Science.gov (United States)

    Li, Tianli

    2017-11-01

    With the development of the current era, computer network and large data gradually become part of the people's life, people use the computer to provide convenience for their own life, but at the same time there are many network information problems has to pay attention. This paper analyzes the information security of computer network based on "big data" analysis, and puts forward some solutions.

  18. Road Transport Network Analysis In Port-Harcourt Metropolics ...

    African Journals Online (AJOL)

    Road transport network contributes to the economy of an area as it connects points of origin to destinations. The thrust of this article therefore, is on the analysis of the road networks in Port – Harcourt metropolis with the aim of determining the connectivity of the road networks and the most accessible node. Consequently ...

  19. Neural network analysis of varying trends in real exchange rates

    NARCIS (Netherlands)

    J.F. Kaashoek (Johan); H.K. van Dijk (Herman)

    1999-01-01

    textabstractIn this paper neural networks are fitted to the real exchange rates of seven industrialized countries. The size and topology of the used networks is found by reducing the size of the network through the use of multiple correlation coefficients, principal component analysis of residuals

  20. Method and tool for network vulnerability analysis

    Science.gov (United States)

    Swiler, Laura Painton [Albuquerque, NM; Phillips, Cynthia A [Albuquerque, NM

    2006-03-14

    A computer system analysis tool and method that will allow for qualitative and quantitative assessment of security attributes and vulnerabilities in systems including computer networks. The invention is based on generation of attack graphs wherein each node represents a possible attack state and each edge represents a change in state caused by a single action taken by an attacker or unwitting assistant. Edges are weighted using metrics such as attacker effort, likelihood of attack success, or time to succeed. Generation of an attack graph is accomplished by matching information about attack requirements (specified in "attack templates") to information about computer system configuration (contained in a configuration file that can be updated to reflect system changes occurring during the course of an attack) and assumed attacker capabilities (reflected in "attacker profiles"). High risk attack paths, which correspond to those considered suited to application of attack countermeasures given limited resources for applying countermeasures, are identified by finding "epsilon optimal paths."

  1. Co-occurrence network analysis of modern Chinese poems

    Science.gov (United States)

    Liang, Wei; Wang, Yanli; Shi, Yuming; Chen, Guanrong

    2015-02-01

    A total of 606 co-occurrence networks of Chinese characters and words are constructed from rhymes, free verses, and prose poems. It is found that 98.5 % of networks have scale-free properties, while 19.8 % of networks do not have small-world features, especially the clustering coefficients in 5.6 % of networks are zero. In addition, 61.4 % of networks have significant hierarchical structures, and 98 % of networks are disassortative. For the above observed phenomena, analysis is provided with interpretation from a linguistic perspective.

  2. Dynamic network connectivity analysis to identify the epileptogenic zones based on stereo-electroencephalography

    Directory of Open Access Journals (Sweden)

    Junwei Mao

    2016-10-01

    Full Text Available ObjectivesAccurate localization of the epileptogenic zones (EZs is essential for the successful surgical treatment of the refractory focal epilepsy. The aim of the present study is to investigate whether a dynamic network connectivity analysis based on stereo-electroencephalography (SEEG signals is effective in localizing the EZs.MethodsSEEG data were recorded from seven patients underwent presurgical evaluation for the treatment of refractory focal epilepsy, and the subsequent resective surgery gave the patients good outcome. The time-variant multivariate autoregressive model was constructed by Kalman filter and the time-variant partial directed coherence was computed, which was then used to construct the dynamic directed network of the epileptic brain. Three graph measures, in-degree, out-degree and betweenness centrality, were used to analyze the characteristics of the dynamic network and to find the important nodes in it. ResultsIn all seven patients, the indicative EZs localized by in-degree and betweenness centrality were highly consistent to the clinical diagnosed EZs. However, the out-degree did not indicate significant difference between nodes in the network.ConclusionsIn this work, the method based on ictal SEEG signals and effective connectivity analysis localized the EZs accurately. It suggested that in-degree and betweenness centrality may be better network characteristics to localize the EZs than out-degree.

  3. Determination of keystone species in CSM food web: A topological analysis of network structure

    Directory of Open Access Journals (Sweden)

    LiQin Jiang

    2015-03-01

    Full Text Available The importance of a species is correlated with its topological properties in a food web. Studies of keystone species provide the valuable theory and evidence for conservation ecology, biodiversity, habitat management, as well as the dynamics and stability of the ecosystem. Comparing with biological experiments, network methods based on topological structure possess particular advantage in the identification of keystone species. In present study, we quantified the relative importance of species in Carpinteria Salt Marsh food web by analyzing five centrality indices. The results showed that there were large differences in rankings species in terms of different centrality indices. Moreover, the correlation analysis of those centralities was studied in order to enhance the identifying ability of keystone species. The results showed that the combination of degree centrality and closeness centrality could better identify keystone species, and the keystone species in the CSM food web were identified as, Stictodora hancocki, small cyathocotylid, Pygidiopsoides spindalis, Phocitremoides ovale and Parorchis acanthus.

  4. Complex Network Analysis of Brazilian Power Grid

    CERN Document Server

    Martins, Gabriela C; Ribeiro, Fabiano L; Forgerini, Fabricio L

    2016-01-01

    Power Grids and other delivery networks has been attracted some attention by the network literature last decades. Despite the Power Grids dynamics has been controlled by computer systems and human operators, the static features of this type of network can be studied and analyzed. The topology of the Brazilian Power Grid (BPG) was studied in this work. We obtained the spatial structure of the BPG from the ONS (electric systems national operator), consisting of high-voltage transmission lines, generating stations and substations. The local low-voltage substations and local power delivery as well the dynamic features of the network were neglected. We analyze the complex network of the BPG and identify the main topological information, such as the mean degree, the degree distribution, the network size and the clustering coefficient to caracterize the complex network. We also detected the critical locations on the network and, therefore, the more susceptible points to lead to a cascading failure and even to a blac...

  5. Comparative genomic reconstruction of transcriptional networks controlling central metabolism in the Shewanella genus

    Energy Technology Data Exchange (ETDEWEB)

    Rodionov, Dmitry A.; Novichkov, Pavel; Stavrovskaya, Elena D.; Rodionova, Irina A.; Li, Xiaoqing; Kazanov, Marat D.; Ravcheev, Dmitry A.; Gerasimova, Anna V.; Kazakov, Alexey E.; Kovaleva, Galina Y.; Permina, Elizabeth A.; Laikova, Olga N.; Overbeek, Ross; Romine, Margaret F.; Fredrickson, Jim K.; Arkin, Adam P.; Dubchak, Inna; Osterman, Andrei L.; Gelfand, Mikhail S.

    2011-06-15

    Genome-scale prediction of gene regulation and reconstruction of transcriptional regulatory networks in bacteria is one of the critical tasks of modern genomics. Despite the growing number of genome-scale gene expression studies, our abilities to convert the results of these studies into accurate regulatory annotations and to project them from model to other organisms are extremely limited. The comparative genomics approaches and computational identification of regulatory sites are useful for the in silico reconstruction of transcriptional regulatory networks in bacteria. The Shewanella genus is comprised of metabolically versatile gamma-proteobacteria, whose lifestyles and natural environments are substantially different from Escherichia coli and other model bacterial species. To explore conservation and variations in the Shewanella transcriptional networks we analyzed the repertoire of transcription factors and performed genomics-based reconstruction and comparative analysis of regulons in 16 Shewanella genomes. The inferred regulatory network includes 82 transcription factors and their DNA binding sites, 8 riboswitches and 6 translational attenuators. Forty five regulons were newly inferred from the genome context analysis, whereas others were propagated from previously characterized regulons in the Enterobacteria and Pseudomonas spp.. However, even orthologous regulators with conserved DNA-binding motifs may control substantially different gene sets, revealing striking differences in regulatory strategies between the Shewanella spp. and E. coli. Multiple examples of regulatory network rewiring include regulon contraction and expansion (as in the case of PdhR, HexR, FadR), and numerous cases of recruiting non-orthologous regulators to control equivalent pathways (e.g. NagR for N-acetylglucosamine catabolism and PsrA for fatty acid degradation) and, conversely, orthologous regulators to control distinct pathways (e.g. TyrR, ArgR, Crp).

  6. Advanced functional network analysis in the geosciences: The pyunicorn package

    Science.gov (United States)

    Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen

    2013-04-01

    Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.

  7. The network researchers' network: A social network analysis of the IMP Group 1985-2006

    DEFF Research Database (Denmark)

    Henneberg, Stephan C. M.; Ziang, Zhizhong; Naudé, Peter

    ). In this paper, based upon the papers presented at the 22 conferences held to date, we undertake a Social Network Analysis in order to examine the degree of co-publishing that has taken place between this group of researchers. We identify the different components in this database, and examine the large main...... components in some detail. The egonets of three of the original 'founding fathers' are examined in detail, and we draw comparisons as to how their publishing strategies vary. Finally, the paper draws some more general conclusions as to the insights that SNA can bring to those working within business...

  8. Analysis of gravity data in Central Valleys, Oaxaca, southern, Mexico

    Science.gov (United States)

    Gonzalez, T.; Ferrusquia, I.

    2015-12-01

    The region known as Central Valleys is located in the state of Oaxaca, southern, Mexico (16.3o- 17.7 o N Lat. and 96 o - 97 o W Long.) In its central portion is settled the capital of the state. There are very few published detailed geological studies.. Geomorphological and geological features, indicates that Central Valleys and surrounding mountains conform a graben structure. Its shape is an inverted Y, centred on Oaxaca City. The study area was covered by a detailed gravity survey with a homogenous distribution of stations. The Bouguer gravity map is dominated by a large gravity low, oriented NW-SE. In order to know the characteristics of anomalies observed gravity, data transformations were used. The use of spectral methods has increased in recent years, especially for the estimation of the depth of the source. Analysis of the gravity data sheds light on the regional depth of the Graben basement and the spatial distribution of the volcanic rocks

  9. Analysis of neural networks through base functions

    NARCIS (Netherlands)

    van der Zwaag, B.J.; Slump, Cornelis H.; Spaanenburg, L.

    Problem statement. Despite their success-story, neural networks have one major disadvantage compared to other techniques: the inability to explain comprehensively how a trained neural network reaches its output; neural networks are not only (incorrectly) seen as a "magic tool" but possibly even more

  10. Synchronization analysis of coloured delayed networks under ...

    Indian Academy of Sciences (India)

    This paper investigates synchronization of coloured delayed networks under decentralized pinning intermittent control. To begin with, the time delays are taken into account in the coloured networks. In addition, we propose a decentralized pinning intermittent control for coloured delayed networks, which is different from that ...

  11. Spectral Modelling for Spatial Network Analysis

    NARCIS (Netherlands)

    Nourian, P.; Rezvani, S.; Sariyildiz, I.S.; van der Hoeven, F.D.; Attar, Ramtin; Chronis, Angelos; Hanna, Sean; Turrin, Michela

    2016-01-01

    Spatial Networks represent the connectivity structure between units of space as a weighted graph whose links are weighted as to the strength of connections. In case of urban spatial networks, the units of space correspond closely to streets and in architectural spatial networks the units correspond

  12. A Network Analysis Model for Selecting Sustainable Technology

    Directory of Open Access Journals (Sweden)

    Sangsung Park

    2015-09-01

    Full Text Available Most companies develop technologies to improve their competitiveness in the marketplace. Typically, they then patent these technologies around the world in order to protect their intellectual property. Other companies may use patented technologies to develop new products, but must pay royalties to the patent holders or owners. Should they fail to do so, this can result in legal disputes in the form of patent infringement actions between companies. To avoid such situations, companies attempt to research and develop necessary technologies before their competitors do so. An important part of this process is analyzing existing patent documents in order to identify emerging technologies. In such analyses, extracting sustainable technology from patent data is important, because sustainable technology drives technological competition among companies and, thus, the development of new technologies. In addition, selecting sustainable technologies makes it possible to plan their R&D (research and development efficiently. In this study, we propose a network model that can be used to select the sustainable technology from patent documents, based on the centrality and degree of a social network analysis. To verify the performance of the proposed model, we carry out a case study using actual patent data from patent databases.

  13. Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast.

    Directory of Open Access Journals (Sweden)

    Chun Ye

    2009-03-01

    Full Text Available Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.

  14. Using network component analysis to dissect regulatory networks mediated by transcription factors in yeast.

    Science.gov (United States)

    Ye, Chun; Galbraith, Simon J; Liao, James C; Eskin, Eleazar

    2009-03-01

    Understanding the relationship between genetic variation and gene expression is a central question in genetics. With the availability of data from high-throughput technologies such as ChIP-Chip, expression, and genotyping arrays, we can begin to not only identify associations but to understand how genetic variations perturb the underlying transcription regulatory networks to induce differential gene expression. In this study, we describe a simple model of transcription regulation where the expression of a gene is completely characterized by two properties: the concentrations and promoter affinities of active transcription factors. We devise a method that extends Network Component Analysis (NCA) to determine how genetic variations in the form of single nucleotide polymorphisms (SNPs) perturb these two properties. Applying our method to a segregating population of Saccharomyces cerevisiae, we found statistically significant examples of trans-acting SNPs located in regulatory hotspots that perturb transcription factor concentrations and affinities for target promoters to cause global differential expression and cis-acting genetic variations that perturb the promoter affinities of transcription factors on a single gene to cause local differential expression. Although many genetic variations linked to gene expressions have been identified, it is not clear how they perturb the underlying regulatory networks that govern gene expression. Our work begins to fill this void by showing that many genetic variations affect the concentrations of active transcription factors in a cell and their affinities for target promoters. Understanding the effects of these perturbations can help us to paint a more complete picture of the complex landscape of transcription regulation. The software package implementing the algorithms discussed in this work is available as a MATLAB package upon request.

  15. Data Farming Process and Initial Network Analysis Capabilities

    Directory of Open Access Journals (Sweden)

    Gary Horne

    2016-01-01

    Full Text Available Data Farming, network applications and approaches to integrate network analysis and processes to the data farming paradigm are presented as approaches to address complex system questions. Data Farming is a quantified approach that examines questions in large possibility spaces using modeling and simulation. It evaluates whole landscapes of outcomes to draw insights from outcome distributions and outliers. Social network analysis and graph theory are widely used techniques for the evaluation of social systems. Incorporation of these techniques into the data farming process provides analysts examining complex systems with a powerful new suite of tools for more fully exploring and understanding the effect of interactions in complex systems. The integration of network analysis with data farming techniques provides modelers with the capability to gain insight into the effect of network attributes, whether the network is explicitly defined or emergent, on the breadth of the model outcome space and the effect of model inputs on the resultant network statistics.

  16. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Science.gov (United States)

    Chernoded, Andrey; Dudko, Lev; Myagkov, Igor; Volkov, Petr

    2017-10-01

    Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  17. Deep Learning Neural Networks and Bayesian Neural Networks in Data Analysis

    Directory of Open Access Journals (Sweden)

    Chernoded Andrey

    2017-01-01

    Full Text Available Most of the modern analyses in high energy physics use signal-versus-background classification techniques of machine learning methods and neural networks in particular. Deep learning neural network is the most promising modern technique to separate signal and background and now days can be widely and successfully implemented as a part of physical analysis. In this article we compare Deep learning and Bayesian neural networks application as a classifiers in an instance of top quark analysis.

  18. Privacy Breach Analysis in Social Networks

    Science.gov (United States)

    Nagle, Frank

    This chapter addresses various aspects of analyzing privacy breaches in social networks. We first review literature that defines three types of privacy breaches in social networks: interactive, active, and passive. We then survey the various network anonymization schemes that have been constructed to address these privacy breaches. After exploring these breaches and anonymization schemes, we evaluate a measure for determining the level of anonymity inherent in a network graph based on its topological structure. Finally, we close by emphasizing the difficulty of anonymizing social network data while maintaining usability for research purposes and offering areas for future work.

  19. Aberrant emotion networks in early major depressive disorder patients: an eigenvector centrality mapping study.

    Science.gov (United States)

    Song, Z; Zhang, M; Huang, P

    2016-05-24

    Major depressive disorder (MDD) is a serious mental disorder that negatively affects the quality of life of many individuals, and is a heavy economic burden to society. In recent years it was thought that depression is a 'disconnection syndrome'. Disorganized brain activity and un-modulated emotion responses were considered the key neuropathologies underlying depression. In the present study, we investigated the alteration of whole brain network connectivity in 28 first-episode, drug-naive patients, using resting-state functional magnetic resonance imaging and a new analytical method called voxel-based eigenvector centrality mapping. We found that compared with normal controls, MDD patients had lower functional connectivity in the bilateral middle frontal gyrus, insula, hippocampus, amygdala and cerebellum, and higher functional connectivity in the medial prefrontal cortex. The functional connectivity strength at the right hippocampus (r=-0.413, P=0.032) and the right insula (r=-0.372, P=0.041) negatively correlated with the severity of the disease. We further examined coordination among these regions, and found that frontal-subcortical connection was reduced and insula-medial prefrontal cortex (mPFC) connection was increased. These results are consistent with previous hypotheses on the neural mechanism of MDD, and provide further evidence that emotion networks are already interrupted in early stages of depression.

  20. Glial kon/NG2 gene network for central nervous system repair

    Directory of Open Access Journals (Sweden)

    Maria Losada-Perez

    2017-01-01

    Full Text Available The glial regenerative response to central nervous system (CNS injury, although limited, can be harnessed to promote regeneration and repair. Injury provokes the proliferation of ensheathing glial cells, which can differentiate to remyelinate axons, and partially restore function. This response is evolutionarily conserved, strongly implying an underlying genetic mechanism. In mammals, it is elicited by NG2 glia, but most often newly generated cells fail to differentiate. Thus an important goal had been to find out how to promote glial differentiation following the proliferative response. A gene network involving Notch and prospero (pros controls the balance between glial proliferation and differentiation in flies and mice, and promotes CNS repair at least in fruit-flies. A key missing link had been how to relate the function of NG2 to this gene network. Recent findings by Losada-Perez et al., published in JCB, demonstrated that the Drosophila NG2 homologue kon-tiki (kon is functionally linked to Notch and pros in glia. By engaging in two feedback loops with Notch and Pros, in response to injury, Kon can regulate both glial cell number and glial shape homeostasis, essential for repair. Drosophila offers powerful genetics to unravel the control of stem and progenitor cells for regeneration and repair.

  1. CENTERA: A Centralized Trust-Based Efficient Routing Protocol with Authentication for Wireless Sensor Networks

    Directory of Open Access Journals (Sweden)

    Ayman Tajeddine

    2015-02-01

    Full Text Available In this paper, we present CENTERA, a CENtralized Trust-based Efficient Routing protocol with an appropriate authentication scheme for wireless sensor networks (WSN. CENTERA utilizes the more powerful base station (BS to gather minimal neighbor trust information from nodes and calculate the best routes after isolating different types of “bad” nodes. By periodically accumulating these simple local observations and approximating the nodes’ battery lives, the BS draws a global view of the network, calculates three quality metrics—maliciousness, cooperation, and compatibility—and evaluates the Data Trust and Forwarding Trust values of each node. Based on these metrics, the BS isolates “bad”, “misbehaving” or malicious nodes for a certain period, and put some nodes on probation. CENTERA increases the node’s bad/probation level with repeated “bad” behavior, and decreases it otherwise. Then it uses a very efficient method to distribute the routing information to “good” nodes. Based on its target environment, and if required, CENTERA uses an authentication scheme suitable for severely constrained nodes, ranging from the symmetric RC5 for safe environments under close administration, to pairing-based cryptography (PBC for hostile environments with a strong attacker model. We simulate CENTERA using TOSSIM and verify its correctness and show some energy calculations.

  2. CENTERA: a centralized trust-based efficient routing protocol with authentication for wireless sensor networks.

    Science.gov (United States)

    Tajeddine, Ayman; Kayssi, Ayman; Chehab, Ali; Elhajj, Imad; Itani, Wassim

    2015-02-02

    In this paper, we present CENTERA, a CENtralized Trust-based Efficient Routing protocol with an appropriate authentication scheme for wireless sensor networks (WSN). CENTERA utilizes the more powerful base station (BS) to gather minimal neighbor trust information from nodes and calculate the best routes after isolating different types of "bad" nodes. By periodically accumulating these simple local observations and approximating the nodes' battery lives, the BS draws a global view of the network, calculates three quality metrics-maliciousness, cooperation, and compatibility-and evaluates the Data Trust and Forwarding Trust values of each node. Based on these metrics, the BS isolates "bad", "misbehaving" or malicious nodes for a certain period, and put some nodes on probation. CENTERA increases the node's bad/probation level with repeated "bad" behavior, and decreases it otherwise. Then it uses a very efficient method to distribute the routing information to "good" nodes. Based on its target environment, and if required, CENTERA uses an authentication scheme suitable for severely constrained nodes, ranging from the symmetric RC5 for safe environments under close administration, to pairing-based cryptography (PBC) for hostile environments with a strong attacker model. We simulate CENTERA using TOSSIM and verify its correctness and show some energy calculations.

  3. CENTERA: A Centralized Trust-Based Efficient Routing Protocol with Authentication for Wireless Sensor Networks

    Science.gov (United States)

    Tajeddine, Ayman; Kayssi, Ayman; Chehab, Ali; Elhajj, Imad; Itani, Wassim

    2015-01-01

    In this paper, we present CENTERA, a CENtralized Trust-based Efficient Routing protocol with an appropriate authentication scheme for wireless sensor networks (WSN). CENTERA utilizes the more powerful base station (BS) to gather minimal neighbor trust information from nodes and calculate the best routes after isolating different types of “bad” nodes. By periodically accumulating these simple local observations and approximating the nodes' battery lives, the BS draws a global view of the network, calculates three quality metrics—maliciousness, cooperation, and compatibility—and evaluates the Data Trust and Forwarding Trust values of each node. Based on these metrics, the BS isolates “bad”, “misbehaving” or malicious nodes for a certain period, and put some nodes on probation. CENTERA increases the node's bad/probation level with repeated “bad” behavior, and decreases it otherwise. Then it uses a very efficient method to distribute the routing information to “good” nodes. Based on its target environment, and if required, CENTERA uses an authentication scheme suitable for severely constrained nodes, ranging from the symmetric RC5 for safe environments under close administration, to pairing-based cryptography (PBC) for hostile environments with a strong attacker model. We simulate CENTERA using TOSSIM and verify its correctness and show some energy calculations. PMID:25648712

  4. From Spinal Central Pattern Generators to Cortical Network: Integrated BCI for Walking Rehabilitation

    Directory of Open Access Journals (Sweden)

    G. Cheron

    2012-01-01

    Full Text Available Success in locomotor rehabilitation programs can be improved with the use of brain-computer interfaces (BCIs. Although a wealth of research has demonstrated that locomotion is largely controlled by spinal mechanisms, the brain is of utmost importance in monitoring locomotor patterns and therefore contains information regarding central pattern generation functioning. In addition, there is also a tight coordination between the upper and lower limbs, which can also be useful in controlling locomotion. The current paper critically investigates different approaches that are applicable to this field: the use of electroencephalogram (EEG, upper limb electromyogram (EMG, or a hybrid of the two neurophysiological signals to control assistive exoskeletons used in locomotion based on programmable central pattern generators (PCPGs or dynamic recurrent neural networks (DRNNs. Plantar surface tactile stimulation devices combined with virtual reality may provide the sensation of walking while in a supine position for use of training brain signals generated during locomotion. These methods may exploit mechanisms of brain plasticity and assist in the neurorehabilitation of gait in a variety of clinical conditions, including stroke, spinal trauma, multiple sclerosis, and cerebral palsy.

  5. RESTful M2M Gateway for Remote Wireless Monitoring for District Central Heating Networks

    Directory of Open Access Journals (Sweden)

    Bo Cheng

    2014-11-01

    Full Text Available In recent years, the increased interest in energy conservation and environmental protection, combined with the development of modern communication and computer technology, has resulted in the replacement of distributed heating by central heating in urban areas. This paper proposes a Representational State Transfer (REST Machine-to-Machine (M2M gateway for wireless remote monitoring for a district central heating network. In particular, we focus on the resource-oriented RESTful M2M gateway architecture, and present an uniform devices abstraction approach based on Open Service Gateway Initiative (OSGi technology, and implement the resource mapping mechanism between resource address mapping mechanism between RESTful resources and the physical sensor devices, and present the buffer queue combined with polling method to implement the data scheduling and Quality of Service (QoS guarantee, and also give the RESTful M2M gateway open service Application Programming Interface (API set. The performance has been measured and analyzed. Finally, the conclusions and future work are presented.

  6. RESTful M2M gateway for remote wireless monitoring for district central heating networks.

    Science.gov (United States)

    Cheng, Bo; Wei, Zesan

    2014-11-27

    In recent years, the increased interest in energy conservation and environmental protection, combined with the development of modern communication and computer technology, has resulted in the replacement of distributed heating by central heating in urban areas. This paper proposes a Representational State Transfer (REST) Machine-to-Machine (M2M) gateway for wireless remote monitoring for a district central heating network. In particular, we focus on the resource-oriented RESTful M2M gateway architecture, and present an uniform devices abstraction approach based on Open Service Gateway Initiative (OSGi) technology, and implement the resource mapping mechanism between resource address mapping mechanism between RESTful resources and the physical sensor devices, and present the buffer queue combined with polling method to implement the data scheduling and Quality of Service (QoS) guarantee, and also give the RESTful M2M gateway open service Application Programming Interface (API) set. The performance has been measured and analyzed. Finally, the conclusions and future work are presented.

  7. Brain network alterations in Alzheimer's disease measured by Eigenvector centrality in fMRI are related to cognition and CSF biomarkers

    NARCIS (Netherlands)

    Binnewijzend, M.A.A.; Adriaanse, S.M.; van der Flier, W.M.; Teunissen, C.E.; de Munck, J.C.; Stam, C.J.; Scheltens, P.; van Berckel, B.N.M.; Barkhof, F.; Wink, A.M.

    2014-01-01

    Recent imaging studies have demonstrated functional brain network changes in patients with Alzheimer's disease (AD). Eigenvector centrality (EC) is a graph analytical measure that identifies prominent regions in the brain network hierarchy and detects localized differences between patient

  8. Amino acid positions subject to multiple coevolutionary constraints can be robustly identified by their eigenvector network centrality scores.

    Science.gov (United States)

    Parente, Daniel J; Ray, J Christian J; Swint-Kruse, Liskin

    2015-12-01

    As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for coevolution between pairs of positions. Coevolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of coevolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded coevolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; "central" positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise coevolution scores: instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints-detectable by divergent algorithms--that occur at key protein locations. Finally, we discuss the fact that multiple patterns coexist in evolutionary data that, together, give rise to emergent protein functions. © 2015 Wiley Periodicals, Inc.

  9. Central and peripheral chemoreceptors evoke distinct responses in simultaneously recorded neurons of the raphé-pontomedullary respiratory network

    OpenAIRE

    Nuding, Sarah C.; Segers, Lauren S.; Shannon, Roger; O'Connor, Russell; Morris, Kendall F.; Lindsey, Bruce G.

    2009-01-01

    The brainstem network for generating and modulating the respiratory motor pattern includes neurons of the medullary ventrolateral respiratory column (VRC), dorsolateral pons (PRG) and raphé nuclei. Midline raphé neurons are proposed to be elements of a distributed brainstem system of central chemoreceptors, as well as modulators of central chemoreceptors at other sites, including the retrotrapezoid nucleus. Stimulation of the raphé system or peripheral chemoreceptors can induce a long-term fa...

  10. Detecting central fixation by means of artificial neural networks in a pediatric vision screener using retinal birefringence scanning.

    Science.gov (United States)

    Gramatikov, Boris I

    2017-04-27

    Reliable detection of central fixation and eye alignment is essential in the diagnosis of amblyopia ("lazy eye"), which can lead to blindness. Our lab has developed and reported earlier a pediatric vision screener that performs scanning of the retina around the fovea and analyzes changes in the polarization state of light as the scan progresses. Depending on the direction of gaze and the instrument design, the screener produces several signal frequencies that can be utilized in the detection of central fixation. The objective of this study was to compare artificial neural networks with classical statistical methods, with respect to their ability to detect central fixation reliably. A classical feedforward, pattern recognition, two-layer neural network architecture was used, consisting of one hidden layer and one output layer. The network has four inputs, representing normalized spectral powers at four signal frequencies generated during retinal birefringence scanning. The hidden layer contains four neurons. The output suggests presence or absence of central fixation. Backpropagation was used to train the network, using the gradient descent algorithm and the cross-entropy error as the performance function. The network was trained, validated and tested on a set of controlled calibration data obtained from 600 measurements from ten eyes in a previous study, and was additionally tested on a clinical set of 78 eyes, independently diagnosed by an ophthalmologist. In the first part of this study, a neural network was designed around the calibration set. With a proper architecture and training, the network provided performance that was comparable to classical statistical methods, allowing perfect separation between the central and paracentral fixation data, with both the sensitivity and the specificity of the instrument being 100%. In the second part of the study, the neural network was applied to the clinical data. It allowed reliable separation between normal subjects

  11. Former Military Networks a Threat to Peace? The Demobilisation and Remobilization of Renamo in Central Mozambique

    Directory of Open Access Journals (Sweden)

    Nikkie Wiegink

    2015-11-01

    Full Text Available Renamo’s recent upsurge against the Mozambican Frelimo-led government after 22 years of relative stability has challenged the country’s often celebrated disarmament, demobilization and reintegration process (1992 to 1994. Drawing on ethnographic fieldwork conducted in Maringue (Sofala province, the location of the rebels’ wartime headquarters and a post-war Renamo stronghold, this paper shows that while the DDR program supposedly ended Renamo’s command and control structure, the former rebel network continued to be a central feature of ex-combatants’ social worlds. Former Renamo combatants spend most of their time in the company of their ‘colleagues of the trenches’ and engaged in relationships of dependency with political Renamo leaders and former commanders. These relationships were not only shaped by the former military structure, but also by friendship, marriage, and patronage dynamics, providing ex-Renamo combatants with physical and economic safety, a sense of belonging and economic possibilities. Recent events in Mozambique suggest that the post-conflict continuation of informal wartime networks is a threat to peace and a failure of demobilization. Nevertheless, the fieldwork conducted in Maringue reveals that the dismantling of the command and control structure is often in vain, as it may be worthwhile for ex-combatants to maintain ties with their former military group for various reasons. Therefore, I argue that it may be useful to consider these networks based on the former armed group in processes of violence reduction, also in the development of DDR programs, as these may offer possibilities for the re-positioning and transformation of (former armed actors.

  12. United Complex Centrality for Identification of Essential Proteins from PPI Networks.

    Science.gov (United States)

    Li, Min; Lu, Yu; Niu, Zhibei; Wu, Fang-Xiang

    2017-01-01

    Essential proteins are indispensable for the survival or reproduction of an organism. Identification of essential proteins is not only necessary for the understanding of the minimal requirements for cellular life, but also important for the disease study and drug design. With the development of high-throughput techniques, a large number of protein-protein interaction data are available, which promotes the studies of essential proteins from the network level. Up to now, though a series of computational methods have been proposed, the prediction precision still needs to be improved. In this paper, we propose a new method, United complex Centrality (UC), to identify essential proteins by integrating the protein complexes with the topological features of protein-protein interaction (PPI) networks. By analyzing the relationship between the essential proteins and the known protein complexes of S. cerevisiae and human, we find that the proteins in complexes are more likely to be essential compared with the proteins not included in any complexes and the proteins appeared in multiple complexes are more inclined to be essential compared to those only appeared in a single complex. Considering that some protein complexes generated by computational methods are inaccurate, we also provide a modified version of UC with parameter alpha, named UC-P. The experimental results show that protein complex information can help identify the essential proteins more accurate both for the PPI network of S. cerevisiae and that of human. The proposed method UC performs obviously better than the eight previously proposed methods (DC, IC, EC, SC, BC, CC, NC, and LAC) for identifying essential proteins.

  13. An Analysis of Social Seed Network and Its Contribution to On-Farm Conservation of Crop Genetic Diversity in Nepal

    Directory of Open Access Journals (Sweden)

    Diwakar Poudel

    2015-01-01

    Full Text Available Social seed systems are important for the maintenance of crop genetic diversity on farm. This is governed by local and informal system in the community through a farmers’ network. This paper analyses these local seed systems through application of social network analysis tools and mappings and examines the network member and its stability over space and time in a small rice farming community in Nepal. NetDraw software is used for data analysis and network mapping. We found that the dynamic network structure had key role in provisioning of traditional varieties and maintaining of crop genetic diversity on farm. We identify and ascertain the key network members, constituted either as nodal or bridging (connector farmers, occupying central position in the network who promote seed flow of local crop diversity, thus strengthening crop genetic resource diversity on farm.

  14. A network perspective on the calamity, induced inaccessibility of communities and the robustness of centralized, landbound relief efforts

    Science.gov (United States)

    Valenzuela, Jesus Felix; Legara, Erika Fille; Fu, Xiuju; Goh, Rick Siow Mong; de Souza, Robert; Monterola, Christopher

    2014-04-01

    We examine the robustness of centralized, landbound relief operations' capability to promptly reach areas affected by a disaster event from a network perspective. We initially look at two idealized road networks: a two-dimensional grid and a scale-free network, and compare them to an actual road network obtained from OpenStreetMap. We show that, from a node designated as the center for relief operations (a "relief center"), damage to a road network causes a substantial fraction of the other nodes (about 20% in the three networks we examined) to become initially inaccessible from any relief effort, although the remaining majority can still be reached readily. Furthermore, we show the presence of a threshold in the two idealized road networks but not in the real one. Below this threshold, all nodes can robustly be reached in a short span of time, and above it, not only the partitioning mentioned above sets in, but also the time needed to reach the nodes becomes susceptible to the amount of damage sustained by the road network. Under damage sustained by random segments of the network, this threshold is higher in the scale-free network compared to the grid, due to the robustness of the former against random attacks. Our results may be of importance in formulating contingency plans for the logistics of disaster relief operations.

  15. Network analysis of wildfire transmission and implications for risk governance.

    Science.gov (United States)

    Ager, Alan A; Evers, Cody R; Day, Michelle A; Preisler, Haiganoush K; Barros, Ana M G; Nielsen-Pincus, Max

    2017-01-01

    We characterized wildfire transmission and exposure within a matrix of large land tenures (federal, state, and private) surrounding 56 communities within a 3.3 million ha fire prone region of central Oregon US. Wildfire simulation and network analysis were used to quantify the exchange of fire among land tenures and communities and analyze the relative contributions of human versus natural ignitions to wildfire exposure. Among the land tenures examined, the area burned by incoming fires averaged 57% of the total burned area. Community exposure from incoming fires ignited on surrounding land tenures accounted for 67% of the total area burned. The number of land tenures contributing wildfire to individual communities and surrounding wildland urban interface (WUI) varied from 3 to 20. Community firesheds, i.e. the area where ignitions can spawn fires that can burn into the WUI, covered 40% of the landscape, and were 5.5 times larger than the combined area of the community core and WUI. For the major land tenures within the study area, the amount of incoming versus outgoing fire was relatively constant, with some exceptions. The study provides a multi-scale characterization of wildfire networks within a large, mixed tenure and fire prone landscape, and illustrates the connectivity of risk between communities and the surrounding wildlands. We use the findings to discuss how scale mismatches in local wildfire governance result from disconnected planning systems and disparate fire management objectives among the large landowners (federal, state, private) and local communities. Local and regional risk planning processes can adopt our concepts and methods to better define and map the scale of wildfire risk from large fire events and incorporate wildfire network and connectivity concepts into risk assessments.

  16. Network analysis of wildfire transmission and implications for risk governance.

    Directory of Open Access Journals (Sweden)

    Alan A Ager

    Full Text Available We characterized wildfire transmission and exposure within a matrix of large land tenures (federal, state, and private surrounding 56 communities within a 3.3 million ha fire prone region of central Oregon US. Wildfire simulation and network analysis were used to quantify the exchange of fire among land tenures and communities and analyze the relative contributions of human versus natural ignitions to wildfire exposure. Among the land tenures examined, the area burned by incoming fires averaged 57% of the total burned area. Community exposure from incoming fires ignited on surrounding land tenures accounted for 67% of the total area burned. The number of land tenures contributing wildfire to individual communities and surrounding wildland urban interface (WUI varied from 3 to 20. Community firesheds, i.e. the area where ignitions can spawn fires that can burn into the WUI, covered 40% of the landscape, and were 5.5 times larger than the combined area of the community core and WUI. For the major land tenures within the study area, the amount of incoming versus outgoing fire was relatively constant, with some exceptions. The study provides a multi-scale characterization of wildfire networks within a large, mixed tenure and fire prone landscape, and illustrates the connectivity of risk between communities and the surrounding wildlands. We use the findings to discuss how scale mismatches in local wildfire governance result from disconnected planning systems and disparate fire management objectives among the large landowners (federal, state, private and local communities. Local and regional risk planning processes can adopt our concepts and methods to better define and map the scale of wildfire risk from large fire events and incorporate wildfire network and connectivity concepts into risk assessments.

  17. Ground Motion in Central Mexico: A Comprehensive Analysis

    Science.gov (United States)

    Ramirez-Guzman, L.; Juarez, A.; Rábade, S.; Aguirre, J.; Bielak, J.

    2015-12-01

    This study presents a detailed analysis of the ground motion in Central Mexico based on numerical simulations, as well as broadband and strong ground motion records. We describe and evaluate a velocity model for Central Mexico derived from noise and regional earthquake cross-correlations, which is used throughout this research to estimate the ground motion in the region. The 3D crustal model includes a geotechnical structure of the Valley of Mexico (VM), subduction zone geometry, and 3D velocity distributions. The latter are based on more than 200 low magnitude (Mw earthquakes and two years of noise recordings. We emphasize the analysis on the ground motion in the Valley of Mexico originating from intra-slab deep events and temblors located along the Pacific coast. Also, we quantify the effects Trans-Mexican Volcanic Belt (TMVB) and the low-velocity deposits on the ground motion. The 3D octree-based finite element wave propagation computations, valid up to 1 Hz, reveal that the inclusion of a basin with a structure as complex as the Valley of Mexico dramatically enhances the regional effects induced by the TMVB. Moreover, the basin not only produces ground motion amplification and anomalous duration, but it also favors the energy focusing into zones of Mexico City where structures typically undergo high levels of damage.

  18. Spatial Analysis of Macro Economic in Central Java (PDRB Analysis in Year 1993-2003

    Directory of Open Access Journals (Sweden)

    Eddy Kiswanto

    2016-12-01

    Full Text Available This paper aims to study the spatial analysis macroeconomics condition in central Java from 1993-2001 base on PDRB analysis. Central Java stands in the last position in the economic in Central Java based on PDRB variable and economic growth is in the lowest category in the comparation with another provinces in Java. This is reason why Central Java is categorized as "LL" (Low low. One of the prime sectors in Central Java is small medium scale enterprises which is dominated 30% of national market, but since the economic crisis stroke in 1997 the manufacture sector, especially industry and processing had collapse. In 1996-1997, the level of manufacture growth increased to 14.4% but then decreased until minus 19.3%. This condition caused by bankruptcy to many of the industries. The poverty profile in Central Java from 1999-2003 is average 23.3% from the total population every years. Central Java stepping to number 2 in level of poverty absolute number 1. In poverty relativity level, Central Java became number 1 in Java from 2002-2003 with the level of poverty reached above the national average. This fact shows the unsuccessfully effort in reducing the poverty level.

  19. Central Somatosensory Networks Respond to a De Novo Innervated Penis: A Proof of Concept in Three Spina Bifida Patients.

    Science.gov (United States)

    Kortekaas, Rudie; Nanetti, Luca; Overgoor, Max L E; de Jong, Bauke M; Georgiadis, Janniko R

    2015-09-01

    Spina bifida (SB) causes low spinal lesions, and patients often have absent genital sensation and a highly impaired sex life. TOMAX (TO MAX-imize sensation, sexuality and quality of life) is a surgical procedure whereby the penis is newly innervated using a sensory nerve originally targeting the inguinal area. Most TOMAX-treated SB patients initially experience penile stimulation as inguinal sensation, but eventually, the perception shifts to penis sensation with erotic feelings. The brain mechanisms mediating this perceptual shift, which are completely unknown, could hold relevance for understanding the brain's role in sexual development. The aim of this study was to study how a newly perceived penis would be mapped onto the brain after a lifelong disconnection. Three TOMAX-treated SB patients participated in a functional magnetic resonance imagery experiment while glans penis, inguinal area, and index finger were stimulated with a paint brush. Brush stimulation-induced activation of the primary somatosensory cortex (SI) and functional connectivity between SI and remote cerebral regions. Stimulation of the re-innervated side of the glans penis and the intact contralateral inguinal area activated a very similar location on SI. Yet, connectivity analysis identified distinct SI functional networks. In all three subjects, the middle cingulate cortex (MCC) and the parietal operculum-insular cortex (OIC) were functionally connected to SI activity during glans penis stimulation, but not to SI activity induced by inguinal stimulation. Investigating central somatosensory network activity to a de novo innervated penis in SB patients is feasible and informative. The consistent involvement of MCC and OIC above and beyond the brain network expected on the basis of inguinal stimulation suggests that these areas mediate the novel penis sensation in these patients. The potential role of MCC and OIC in this process is discussed, along with recommendations for further research.

  20. Network analysis and synthesis a modern systems theory approach

    CERN Document Server

    Anderson, Brian D O

    2006-01-01

    Geared toward upper-level undergraduates and graduate students, this book offers a comprehensive look at linear network analysis and synthesis. It explores state-space synthesis as well as analysis, employing modern systems theory to unite the classical concepts of network theory. The authors stress passive networks but include material on active networks. They avoid topology in dealing with analysis problems and discuss computational techniques. The concepts of controllability, observability, and degree are emphasized in reviewing the state-variable description of linear systems. Explorations

  1. Sensor Network Information Analytical Methods: Analysis of Similarities and Differences

    Directory of Open Access Journals (Sweden)

    Chen Jian

    2014-04-01

    Full Text Available In the Sensor Network information engineering literature, few references focus on the definition and design of Sensor Network information analytical methods. Among those that do are Munson, et al. and the ISO standards on functional size analysis. To avoid inconsistent vocabulary and potentially incorrect interpretation of data, Sensor Network information analytical methods must be better designed, including definitions, analysis principles, analysis rules, and base units. This paper analyzes the similarities and differences across three different views of analytical methods, and uses a process proposed for the design of Sensor Network information analytical methods to analyze two examples of such methods selected from the literature.

  2. State of the art applications of social network analysis

    CERN Document Server

    Can, Fazli; Polat, Faruk

    2014-01-01

    Social network analysis increasingly bridges the discovery of patterns in diverse areas of study as more data becomes available and complex. Yet the construction of huge networks from large data often requires entirely different approaches for analysis including; graph theory, statistics, machine learning and data mining. This work covers frontier studies on social network analysis and mining from different perspectives such as social network sites, financial data, e-mails, forums, academic research funds, XML technology, blog content, community detection and clique finding, prediction of user

  3. Graph Analysis of Functional Brain Networks in Patients with Mild Traumatic Brain Injury

    Science.gov (United States)

    van der Horn, Harm J.; Liemburg, Edith J.; Scheenen, Myrthe E.; de Koning, Myrthe E.; Spikman, Jacoba M.; van der Naalt, Joukje

    2017-01-01

    Mild traumatic brain injury (mTBI) is one of the most common neurological disorders worldwide. Posttraumatic complaints are frequently reported, interfering with outcome. However, a consistent neural substrate has not yet been found. We used graph analysis to further unravel the complex interactions between functional brain networks, complaints, anxiety and depression in the sub-acute stage after mTBI. This study included 54 patients with uncomplicated mTBI and 20 matched healthy controls. Posttraumatic complaints, anxiety and depression were measured at two weeks post-injury. Patients were selected based on presence (n = 34) or absence (n = 20) of complaints. Resting-state fMRI scans were made approximately four weeks post-injury. High order independent component analysis resulted in 89 neural components that were included in subsequent graph analyses. No differences in graph measures were found between patients with mTBI and healthy controls. Regarding the two patient subgroups, degree, strength, local efficiency and eigenvector centrality of the bilateral posterior cingulate/precuneus and bilateral parahippocampal gyrus were higher, and eigenvector centrality of the frontal pole/ bilateral middle & superior frontal gyrus was lower in patients with complaints compared to patients without complaints. In patients with mTBI, higher degree, strength and eigenvector centrality of default mode network components were related to higher depression scores, and higher degree and eigenvector centrality of executive network components were related to lower depression scores. In patients without complaints, one extra module was found compared to patients with complaints and healthy controls, consisting of the cingulate areas. In conclusion, this research extends the knowledge of functional network connectivity after mTBI. Specifically, our results suggest that an imbalance in the function of the default mode- and executive network plays a central role in the interaction

  4. Graph Analysis of Functional Brain Networks in Patients with Mild Traumatic Brain Injury.

    Directory of Open Access Journals (Sweden)

    Harm J van der Horn

    Full Text Available Mild traumatic brain injury (mTBI is one of the most common neurological disorders worldwide. Posttraumatic complaints are frequently reported, interfering with outcome. However, a consistent neural substrate has not yet been found. We used graph analysis to further unravel the complex interactions between functional brain networks, complaints, anxiety and depression in the sub-acute stage after mTBI. This study included 54 patients with uncomplicated mTBI and 20 matched healthy controls. Posttraumatic complaints, anxiety and depression were measured at two weeks post-injury. Patients were selected based on presence (n = 34 or absence (n = 20 of complaints. Resting-state fMRI scans were made approximately four weeks post-injury. High order independent component analysis resulted in 89 neural components that were included in subsequent graph analyses. No differences in graph measures were found between patients with mTBI and healthy controls. Regarding the two patient subgroups, degree, strength, local efficiency and eigenvector centrality of the bilateral posterior cingulate/precuneus and bilateral parahippocampal gyrus were higher, and eigenvector centrality of the frontal pole/ bilateral middle & superior frontal gyrus was lower in patients with complaints compared to patients without complaints. In patients with mTBI, higher degree, strength and eigenvector centrality of default mode network components were related to higher depression scores, and higher degree and eigenvector centrality of executive network components were related to lower depression scores. In patients without complaints, one extra module was found compared to patients with complaints and healthy controls, consisting of the cingulate areas. In conclusion, this research extends the knowledge of functional network connectivity after mTBI. Specifically, our results suggest that an imbalance in the function of the default mode- and executive network plays a central role in the

  5. Graph Analysis of Functional Brain Networks in Patients with Mild Traumatic Brain Injury.

    Science.gov (United States)

    van der Horn, Harm J; Liemburg, Edith J; Scheenen, Myrthe E; de Koning, Myrthe E; Spikman, Jacoba M; van der Naalt, Joukje

    2017-01-01

    Mild traumatic brain injury (mTBI) is one of the most common neurological disorders worldwide. Posttraumatic complaints are frequently reported, interfering with outcome. However, a consistent neural substrate has not yet been found. We used graph analysis to further unravel the complex interactions between functional brain networks, complaints, anxiety and depression in the sub-acute stage after mTBI. This study included 54 patients with uncomplicated mTBI and 20 matched healthy controls. Posttraumatic complaints, anxiety and depression were measured at two weeks post-injury. Patients were selected based on presence (n = 34) or absence (n = 20) of complaints. Resting-state fMRI scans were made approximately four weeks post-injury. High order independent component analysis resulted in 89 neural components that were included in subsequent graph analyses. No differences in graph measures were found between patients with mTBI and healthy controls. Regarding the two patient subgroups, degree, strength, local efficiency and eigenvector centrality of the bilateral posterior cingulate/precuneus and bilateral parahippocampal gyrus were higher, and eigenvector centrality of the frontal pole/ bilateral middle & superior frontal gyrus was lower in patients with complaints compared to patients without complaints. In patients with mTBI, higher degree, strength and eigenvector centrality of default mode network components were related to higher depression scores, and higher degree and eigenvector centrality of executive network components were related to lower depression scores. In patients without complaints, one extra module was found compared to patients with complaints and healthy controls, consisting of the cingulate areas. In conclusion, this research extends the knowledge of functional network connectivity after mTBI. Specifically, our results suggest that an imbalance in the function of the default mode- and executive network plays a central role in the interaction

  6. Investigating communication networks contextually: Qualitative network analysis as cross-media research

    Directory of Open Access Journals (Sweden)

    Andreas Hepp

    2016-06-01

    Full Text Available This article introduces the approach of contextualised communication network analysis as a qualitative procedure for researching communicative relationships realised through the media. It combines qualitative interviews on media appropriation, egocentric network maps, and media diaries. Through the triangulation of these methods of data collection, it is possible to gain a differentiated insight into the specific meanings, structures and processes of communication networks across a variety of media. The approach is illustrated using a recent study dealing with the mediatisation of community building among young people. In this context, the qualitative communication network analysis has been applied to distinguish “localists” from “centrists”, “multilocalists”, and “pluralists”. These different “horizons of mediatised communitisation” are connected to distinct communication networks. Since this involves today a variety of different media, the contextual analysis of communication networks necessarily has to imply a cross-media perspective.

  7. A window on emergent European social network analysis

    OpenAIRE

    Cronin, Bruce

    2011-01-01

    This paper introduces the collection of papers in this issue, providing context in the recent development of social network analysis in Europe and the catalytic contributions of the Essex University Summer School and latterly the UK Social Networks Association. While these organisations have provided important focuses for social network analysis in the UK their reach has been much broader, principally among graduate students across Europe and the emergent research agenda they are forging. Fiv...

  8. Methodologies and techniques for analysis of network flow data

    Energy Technology Data Exchange (ETDEWEB)

    Bobyshev, A.; Grigoriev, M.; /Fermilab

    2004-12-01

    Network flow data gathered at the border routers and core switches is used at Fermilab for statistical analysis of traffic patterns, passive network monitoring, and estimation of network performance characteristics. Flow data is also a critical tool in the investigation of computer security incidents. Development and enhancement of flow based tools is an on-going effort. This paper describes the most recent developments in flow analysis at Fermilab.

  9. Statistical Network Analysis for Functional MRI: Mean Networks and Group Comparisons.

    Directory of Open Access Journals (Sweden)

    Cedric E Ginestet

    2014-05-01

    Full Text Available Comparing networks in neuroscience is hard, because the topological properties of a given network are necessarily dependent on the number of edges of that network. This problem arises in the analysis of both weighted and unweighted networks. The term density is often used in this context, in order to refer to the mean edge weight of a weighted network, or to the number of edges in an unweighted one. Comparing families of networks is therefore statistically difficult because differences in topology are necessarily associated with differences in density. In this review paper, we consider this problem from two different perspectives, which include (i the construction of summary networks, such as how to compute and visualize the mean network from a sample of network-valued data points; and (ii how to test for topological differences, when two families of networks also exhibit significant differences in density. In the first instance, we show that the issue of summarizing a family of networks can be conducted by either adopting a mass-univariate approach, which produces a statistical parametric network (SPN, or by directly computing the mean network, provided that a metric has been specified on the space of all networks with a given number of nodes. In the second part of this review, we then highlight the inherent problems associated with the comparison of topological functions of families of networks that differ in density. In particular, we show that a wide range of topological summaries, such as global efficiency and network modularity are highly sensitive to differences in density. Moreover, these problems are not restricted to unweighted metrics, as we demonstrate that the same issues remain present when considering the weighted versions of these metrics. We conclude by encouraging caution, when reporting such statistical comparisons, and by emphasizing the importance of constructing summary networks.

  10. Reliability Analysis of Wireless Sensor Networks Using Markovian Model

    Directory of Open Access Journals (Sweden)

    Jin Zhu

    2012-01-01

    Full Text Available This paper investigates reliability analysis of wireless sensor networks whose topology is switching among possible connections which are governed by a Markovian chain. We give the quantized relations between network topology, data acquisition rate, nodes' calculation ability, and network reliability. By applying Lyapunov method, sufficient conditions of network reliability are proposed for such topology switching networks with constant or varying data acquisition rate. With the conditions satisfied, the quantity of data transported over wireless network node will not exceed node capacity such that reliability is ensured. Our theoretical work helps to provide a deeper understanding of real-world wireless sensor networks, which may find its application in the fields of network design and topology control.

  11. The reconstruction and analysis of tissue specific human metabolic networks.

    Science.gov (United States)

    Hao, Tong; Ma, Hong-Wu; Zhao, Xue-Ming; Goryanin, Igor

    2012-02-01

    Human tissues have distinct biological functions. Many proteins/enzymes are known to be expressed only in specific tissues and therefore the metabolic networks in various tissues are different. Though high quality global human metabolic networks and metabolic networks for certain tissues such as liver have already been studied, a systematic study of tissue specific metabolic networks for all main tissues is still missing. In this work, we reconstruct the tissue specific metabolic networks for 15 main tissues in human based on the previously reconstructed Edinburgh Human Metabolic Network (EHMN). The tissue information is firstly obtained for enzymes from Human Protein Reference Database (HPRD) and UniprotKB databases and transfers to reactions through the enzyme-reaction relationships in EHMN. As our knowledge of tissue distribution of proteins is still very limited, we replenish the tissue information of the metabolic network based on network connectivity analysis and thorough examination of the literature. Finally, about 80% of proteins and reactions in EHMN are determined to be in at least one of the 15 tissues. To validate the quality of the tissue specific network, the brain specific metabolic network is taken as an example for functional module analysis and the results reveal that the function of the brain metabolic network is closely related with its function as the centre of the human nervous system. The tissue specific human metabolic networks are available at .

  12. Exploratory social network analysis with Pajek. - 2nd ed.

    NARCIS (Netherlands)

    de Nooy, W.; Mrvar, A.; Batagelj, V.

    2011-01-01

    This is an extensively revised and expanded second edition of the successful textbook on social network analysis integrating theory, applications, and network analysis using Pajek. The main structural concepts and their applications in social research are introduced with exercises. Pajek software

  13. Efficient health care service delivery using network analysis: a case ...

    African Journals Online (AJOL)

    Efficient health care service delivery using network analysis: a case study of Kwara State, Nigeria. ... Ethiopian Journal of Environmental Studies and Management ... This paper addresses challenges with prompt health care delivery using Network Analysis of Critical Path Model (CPM) to plan the hospital capacity with a ...

  14. A Social Network Analysis of Occupational Segregation

    OpenAIRE

    Buhai, Sebastian; van der Leij, Marco

    2006-01-01

    We develop a social network model of occupational segregation between different social groups, generated by the existence of positive inbreeding bias among individuals from the same group. If network referrals are important in getting a job, then expected inbreeding bias in the contact network structure induces different career choices for individuals from different social groups. This further translates into stable occupational segregation equilibria in the labour market. We derive the condi...

  15. Prediction of extreme floods in the Central Andes by means of Complex Networks

    Science.gov (United States)

    Boers, Niklas; Bookhagen, Bodo; Barbosa, Henrique; Marwan, Norbert; Kurths, Jürgen; Marengo, Jose

    2014-05-01

    Based on a non-linear synchronisation measure and complex network theory, we present a novel framework for the prediction of extreme events of spatially embedded, interrelated time series. This method is general in the sense that it can be applied to any type of spatially sampled time series with significant interrelations, ranging from climate observables to biological or stock market data. In this presentation, we apply our method to extreme rainfall in South America and show how this leads to the prediction of more than 60% (90% during El Niño conditions) of extreme rainfall events in the eastern Central Andes of Bolivia and northern Argentina, with only 1% false alarms. From paleoclimatic to decadal time scales, the Central Andes continue to be subject to pronounced changes in climatic conditions. In particular, our and past work shows that frequency as well as magnitudes of extreme rainfall events have increased significantly during past decades, calling for a better understanding of the involved climatic mechanisms. Due to their large spatial extend and occurrence at high elevations, these extreme events often lead to severe floods and landslides with disastrous socioeconomic impacts. They regularly affect tens of thousands of people and produce estimated costs of the order of several hundred million USD. Alongside with the societal value of predicting natural hazards, our study provides insights into the responsible climatic features and suggests interactions between Rossby waves in polar regions and large scale (sub-)tropical moisture transport as a driver of subseasonal variability of the South American monsoon system. Predictable extreme events result from the propagation of extreme rainfall from the region of Buenos Aires towards the Central Andes given characteristic atmospheric conditions. Our results indicate that the role of frontal systems originating from Rossby waves in polar latitudes is much more dominant for controlling extreme rainfall in

  16. Amino acid positions subject to multiple co-evolutionary constraints can be robustly identified by their eigenvector network centrality scores

    Science.gov (United States)

    Parente, Daniel J.; Ray, J. Christian J.; Swint-Kruse, Liskin

    2015-01-01

    As proteins evolve, amino acid positions key to protein structure or function are subject to mutational constraints. These positions can be detected by analyzing sequence families for amino acid conservation or for co-evolution between pairs of positions. Co-evolutionary scores are usually rank-ordered and thresholded to reveal the top pairwise scores, but they also can be treated as weighted networks. Here, we used network analyses to bypass a major complication of co-evolution studies: For a given sequence alignment, alternative algorithms usually identify different, top pairwise scores. We reconciled results from five commonly-used, mathematically divergent algorithms (ELSC, McBASC, OMES, SCA, and ZNMI), using the LacI/GalR and 1,6-bisphosphate aldolase protein families as models. Calculations used unthresholded co-evolution scores from which column-specific properties such as sequence entropy and random noise were subtracted; “central” positions were identified by calculating various network centrality scores. When compared among algorithms, network centrality methods, particularly eigenvector centrality, showed markedly better agreement than comparisons of the top pairwise scores. Positions with large centrality scores occurred at key structural locations and/or were functionally sensitive to mutations. Further, the top central positions often differed from those with top pairwise co-evolution scores: Instead of a few strong scores, central positions often had multiple, moderate scores. We conclude that eigenvector centrality calculations reveal a robust evolutionary pattern of constraints – detectable by divergent algorithms – that occur at key protein locations. Finally, we discuss the fact that multiple patterns co-exist in evolutionary data that, together, give rise to emergent protein functions. PMID:26503808

  17. On the Development of Multi-Hazard Early Warning Networks: Practical experiences from North and Central America.

    Science.gov (United States)

    Mencin, David; Hodgkinson, Kathleen; Braun, John; Meertens, Charles; Mattioli, Glen; Phillips, David; Blume, Fredrick; Berglund, Henry; Fox, Otina; Feaux, Karl

    2015-04-01

    The GAGE facility, managed by UNAVCO, maintains and operates about 1300 GNSS stations distributed across North and Central America as part of the EarthScope Plate Boundary Observatory (PBO) and the Continuously Operating Caribbean GPS Observational Network (COCONet). UNAVCO has upgraded about 450 stations in these networks to real-time and high-rate (RT-GNSS) and included surface meteorological instruments. The majority of these streaming stations are part of the PBO but also include approximately 50 RT-GNSS stations in the Caribbean and Central American region as part of the COCONet and TLALOCNet projects. Based on community input UNAVCO has been exploring ways to increase the capability and utility of these resources to improve our understanding in diverse areas of geophysics including seismic, volcanic, magmatic and tsunami deformation sources, extreme weather events such as hurricanes and storms, and space weather. The RT-GNSS networks also have the potential to profoundly transform our ability to rapidly characterize geophysical events, provide early warning, as well as improve hazard mitigation and response. Specific applications currently under development with university, commercial, non-profit and government collaboration on national and international scales include earthquake and tsunami early warning systems and near real-time tropospheric modeling of hurricanes and precipitable water vapor estimate assimilation. Using tsunami early warning as an example, an RT-GNSS network can provide multiple inputs in an operational system starting with rapid assessment of earthquake sources and associated deformation which informs the initial modeled tsunami. The networks can then can also provide direct measurements of the tsunami wave heights and propagation by tracking the associated ionospheric disturbance from several 100's of km away as the waves approaches the shoreline. These GNSS based constraints can refine the tsunami and inundation models and potentially

  18. EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model.

    Science.gov (United States)

    Hädicke, Oliver; Klamt, Steffen

    2017-01-03

    Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli's central metabolism.

  19. Analysis of friendship network from MMORPG based data

    OpenAIRE

    Črnigoj, Dean

    2016-01-01

    This work analyzes friendship network from a Massively Multiplayer Online Role-Playing Game (MMORPG). The network is based on data from a private server that was active from 2007 until 2011. The work conducts a standard analysis of the network and then divides players according to different groups based on their activity. Work checks how friendship network can be correlated to the clan (a self-organized group of players who often form a league and play on the same side in a match) network. Ma...

  20. Dynamical Networks for Smog Pattern Analysis

    CERN Document Server

    Zong, Linqi; Zhu, Jia

    2015-01-01

    Smog, as a form of air pollution, poses as a serious problem to the environment, health, and economy of the world[1-4] . Previous studies on smog mostly focused on the components and the effects of smog [5-10]. However, as the smog happens with increased frequency and duration, the smog pattern which is critical for smog forecast and control, is rarely investigated, mainly due to the complexity of the components, the causes, and the spreading processes of smog. Here we report the first analysis on smog pattern applying the model of dynamical networks with spontaneous recovery. We show that many phenomena such as the sudden outbreak and dissipation of smog and the long duration smog can be revealed with the mathematical mechanism under a random walk simulation. We present real-world air quality index data in accord with the predictions of the model. Also we found that compared to external causes such as pollution spreading from nearby, internal causes such as industrial pollution and vehicle emission generated...

  1. Blood meal analysis of culicoides (Diptera: ceratopogonidae) in central Tunisia.

    Science.gov (United States)

    Slama, Darine; Haouas, Najoua; Mezhoud, Habib; Babba, Hamouda; Chaker, Emna

    2015-01-01

    To evaluate the host preferences of Culicoides species (Diptera: Ceratopogonidae) in Central Tunisia, we identified the source of blood meals of field collected specimens by sequencing of the cytochrome b (cyt b) mitochondrial locus and Prepronociceptine single copy nuclear gene. The study includes the most common and abundant livestock associated species of biting midges in Tunisia: C. imicola, C. jumineri, C. newsteadi, C. paolae, C. cataneii, C. circumscriptus, C. kingi, C. pseudojumineri, C. submaritimus, C. langeroni, C. jumineri var and some unidentified C. species. Analysis of cyt b PCR products from 182 field collected blood-engorged females' midges revealed that 92% of them fed solely on mammalian species, 1.6% on birds, 2.4% on insects and 0.8% on reptiles. The blast results identified the blood origin of biting midges to the species level with exact or nearly exact matches (≥98%). The results confirm the presence of several Culicoides species, including proven vectors in Central Tunisia. Blood meal analyses show that these species will indeed feed on bigger mammals, thereby highlighting the risk that these viruses will be able to spread in Tunisia.

  2. Fractal and multifractal analysis of complex networks: Estonian network of payments

    Science.gov (United States)

    Rendón de la Torre, Stephanie; Kalda, Jaan; Kitt, Robert; Engelbrecht, Jüri

    2017-12-01

    Complex networks have gained much attention from different areas of knowledge in recent years. Particularly, the structures and dynamics of such systems have attracted considerable interest. Complex networks may have characteristics of multifractality. In this study, we analyze fractal and multifractal properties of a novel network: the large scale economic network of payments of Estonia, where companies are represented by nodes and the payments done between companies are represented by links. We present a fractal scaling analysis and examine the multifractal behavior of this network by using a sandbox algorithm. Our results indicate the existence of multifractality in this network and consequently, the existence of multifractality in the Estonian economy. To the best of our knowledge, this is the first study that analyzes multifractality of a complex network of payments.

  3. A Social Network Analysis of Occupational Segregation

    NARCIS (Netherlands)

    I.S. Buhai (Sebastian); M.J. van der Leij (Marco)

    2006-01-01

    textabstractThis paper proposes a simple social network model of occupational segregation, generated by the existence of inbreeding bias among individuals of the same social group. If network referrals are important in getting a job, then expected inbreeding bias in the social structure results in

  4. Buddhist social networks and health in old age: A study in central Thailand.

    Science.gov (United States)

    Sasiwongsaroj, Kwanchit; Wada, Taizo; Okumiya, Kiyohito; Imai, Hissei; Ishimoto, Yasuko; Sakamoto, Ryota; Fujisawa, Michiko; Kimura, Yumi; Chen, Wen-ling; Fukutomi, Eriko; Matsubayashi, Kozo

    2015-11-01

    Religious social networks are well known for their capacity to improve individual health, yet the effects of friendship networks within the Buddhist context remain largely unknown. The present study aimed to compare health status and social support in community-dwelling older adults according to their level of Buddhist social network (BSN) involvement, and to examine the association between BSN involvement and functional health among older adults. A cross-sectional survey was carried out among 427 Buddhist community-dwelling older adults aged ≥60 years in Nakhon Pathom, Thailand. Data were collected from home-based personal interviews using a structured questionnaire. Health status was defined according to the measures of basic and advanced activities of daily living (ADL), the 15-item Geriatric Depression Scale and subjective quality of life. Perceived social support was assessed across the four dimensions of tangible, belonging, emotional and information support. Multiple logistic regression was used for analysis. Older adults with BSN involvement reported better functional, mental and social health status, and perceived greater social support than those without BSN involvement. In addition, BSN involvement was positively associated with independence in basic and advanced ADL. After adjusting for age, sex, education, income, morbidity and depressive symptoms, BSN showed a strong association with advanced ADL and a weak association with basic ADL. The results show that involvement in BSN could contribute positively to functional health, particularly with regard to advanced ADL. Addressing the need for involvement in these networks by older adults might help delay functional decline and save on healthcare costs. © 2014 Japan Geriatrics Society.

  5. Abnormal intrinsic functional hubs in alcohol dependence: evidence from a voxelwise degree centrality analysis

    Directory of Open Access Journals (Sweden)

    Luo X

    2017-07-01

    Full Text Available Xiaoping Luo,1,2 Linghong Guo,1 Xi-Jian Dai,3 Qinglai Wang,2 Wenzhong Zhu,2 Xinjun Miao,2 Honghan Gong1 1Department of Radiology, The First Affiliated Hospital of Nanchang University, Nangchang, Jiangxi, People’s Republic of China; 2Department of Radiology, Wenzhou Chinese Medicine Hospital, Wenzhou, Zhejiang, People’s Republic of China; 3Department of Medical Imaging, Jinling Hospital, Medical School of Nanjing University, Nanjing, Jiangsu, People’s Republic of China Objective: To explore the abnormal intrinsic functional hubs in alcohol dependence using voxelwise degree centrality analysis approach, and their relationships with clinical features.Materials and methods: Twenty-four male alcohol dependence subjects free of medicine (mean age, 50.21±9.62 years and 24 age- and education-matched male healthy controls (mean age, 50.29±8.92 years were recruited. The alcohol use disorders identification test and the severity of alcohol dependence questionnaire (SADQ were administered to assess the severity of alcohol craving. Voxelwise degree centrality approach was used to assess the abnormal intrinsic functional hubs features in alcohol dependence. Simple linear regression analysis was performed to investigate the relationships between the clinical features and abnormal intrinsic functional hubs.Results: Compared with healthy controls, alcohol dependence subjects exhibited significantly different degree centrality values in widespread left lateralization brain areas, including higher degree centrality values in the left precentral gyrus (BA 6, right hippocampus (BA 35, 36, and left orbitofrontal cortex (BA 11 and lower degree centrality values in the left cerebellum posterior lobe, bilateral secondary visual network (BA 18, and left precuneus (BA 7, 19. SADQ revealed a negative linear correlation with the degree centrality value in the left precentral gyrus (R2=0.296, P=0.006.Conclusion: The specific abnormal intrinsic functional hubs appear

  6. Using Social Network Analysis to Identify Key Child Care Center Staff for Obesity Prevention Interventions: A Pilot Study

    Directory of Open Access Journals (Sweden)

    Jennifer Marks

    2013-01-01

    Full Text Available Introduction. Interest has grown in how systems thinking could be used in obesity prevention. Relationships between key actors, represented by social networks, are an important focus for considering intervention in systems. Method. Two long day care centers were selected in which previous obesity prevention programs had been implemented. Measures showed ways in which physical activity and dietary policy are conversations and actions transacted through social networks (interrelationships within centers, via an eight item closed-ended social network questionnaire. Questionnaire data were collected from (17/20; response rate 85% long day care center staff. Social network density and centrality statistics were calculated, using UCINET social network software, to examine the role of networks in obesity prevention. Results. “Degree” (influence and “betweeness” (gatekeeper centrality measures of staff inter-relationships about physical activity, dietary, and policy information identified key players in each center. Network density was similar and high on some relationship networks in both centers but markedly different in others, suggesting that the network tool identified unique center social dynamics. These differences could potentially be the focus of future team capacity building. Conclusion. Social network analysis is a feasible and useful method to identify existing obesity prevention networks and key personnel in long day care centers.

  7. NEXCADE: perturbation analysis for complex networks.

    Directory of Open Access Journals (Sweden)

    Gitanjali Yadav

    Full Text Available Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the 'robust, yet fragile' nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html.

  8. POINeT: protein interactome with sub-network analysis and hub prioritization

    Directory of Open Access Journals (Sweden)

    Lai Jin-Mei

    2009-04-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs are critical to every aspect of biological processes. Expansion of all PPIs from a set of given queries often results in a complex PPI network lacking spatiotemporal consideration. Moreover, the reliability of available PPI resources, which consist of low- and high-throughput data, for network construction remains a significant challenge. Even though a number of software tools are available to facilitate PPI network analysis, an integrated tool is crucial to alleviate the burden on querying across multiple web servers and software tools. Results We have constructed an integrated web service, POINeT, to simplify the process of PPI searching, analysis, and visualization. POINeT merges PPI and tissue-specific expression data from multiple resources. The tissue-specific PPIs and the numbers of research papers supporting the PPIs can be filtered with user-adjustable threshold values and are dynamically updated in the viewer. The network constructed in POINeT can be readily analyzed with, for example, the built-in centrality calculation module and an integrated network viewer. Nodes in global networks can also be ranked and filtered using various network analysis formulas, i.e., centralities. To prioritize the sub-network, we developed a ranking filtered method (S3 to uncover potential novel mediators in the midbody network. Several examples are provided to illustrate the functionality of POINeT. The network constructed from four schizophrenia risk markers suggests that EXOC4 might be a novel marker for this disease. Finally, a liver-specific PPI network has been filtered with adult and fetal liver expression profiles. Conclusion The functionalities provided by POINeT are highly improved compared to previous version of POINT. POINeT enables the identification and ranking of potential novel genes involved in a sub-network. Combining with tissue-specific gene expression profiles, PPIs specific to

  9. egoSlider: Visual Analysis of Egocentric Network Evolution.

    Science.gov (United States)

    Wu, Yanhong; Pitipornvivat, Naveen; Zhao, Jian; Yang, Sixiao; Huang, Guowei; Qu, Huamin

    2016-01-01

    Ego-network, which represents relationships between a specific individual, i.e., the ego, and people connected to it, i.e., alters, is a critical target to study in social network analysis. Evolutionary patterns of ego-networks along time provide huge insights to many domains such as sociology, anthropology, and psychology. However, the analysis of dynamic ego-networks remains challenging due to its complicated time-varying graph structures, for example: alters come and leave, ties grow stronger and fade away, and alter communities merge and split. Most of the existing dynamic graph visualization techniques mainly focus on topological changes of the entire network, which is not adequate for egocentric analytical tasks. In this paper, we present egoSlider, a visual analysis system for exploring and comparing dynamic ego-networks. egoSlider provides a holistic picture of the data through multiple interactively coordinated views, revealing ego-network evolutionary patterns at three different layers: a macroscopic level for summarizing the entire ego-network data, a mesoscopic level for overviewing specific individuals' ego-network evolutions, and a microscopic level for displaying detailed temporal information of egos and their alters. We demonstrate the effectiveness of egoSlider with a usage scenario with the DBLP publication records. Also, a controlled user study indicates that in general egoSlider outperforms a baseline visualization of dynamic networks for completing egocentric analytical tasks.

  10. Quasiclassical analysis of spectra in two groups of central potentials

    CERN Document Server

    Shpatakovskaya, G V

    2001-01-01

    The method for the spectra analysis in the gravitational central potentials with the Coulomb feature in the zero (interatomic potentials) and the finite ones in the zero (potentials in the spheric clusters nuclei) is proposed. It is shown that by the degeneration removal by the orbital quantum number for the n-shell by small l the difference epsilon sub n sub l - epsilon sub n sub 0 approx = a subepsilon sub sub n sub sub 0 (l + 1/2) sup 2. The correctness of the presented formula for the internal electrons is demonstrated by the mercury atoms spectrum calculations. The reverse dependence takes place, as a rule, in the cluster potentials. The dependence of the area position with the degenerated level on the N cluster size is analyzed by the example of the Al sub N aluminium clusters. It is known that the increase in the N leads to the pressing-out of this area upwards

  11. Free vibration analysis of rectangular plates with central cutout

    Directory of Open Access Journals (Sweden)

    Kanak Kalita

    2016-12-01

    Full Text Available A nine-node isoparametric plate element in conjunction with first-order shear deformation theory is used for free vibration analysis of rectangular plates with central cutouts. Both thick and thin plate problems are solved for various aspect ratios and boundary conditions. In this article, primary focus is given to the effect of rotary inertia on natural frequencies of perforated rectangular plates. It is found that rotary inertia has significant effect on thick plates, while for thin plates the rotary inertia term can be ignored. It is seen that the numerical convergence is very rapid and based on comparison with experimental and analytical data from literature, it is proposed that the present formulation is capable of yielding highly accurate results. Finally, some new numerical solutions are provided here, which may serve as benchmark for future research on similar problems.

  12. Analysis of short squeeze film dampers with a central groove

    Science.gov (United States)

    San Andres, Luis A.

    1992-10-01

    A novel analysis for the dynamic force response of a squeeze film damper with a central feeding groove considers the dynamic flow interaction between the squeeze film lands and the feeding groove. For small amplitude centered motions and based on the short bearing model, corrected values for the damping and inertia force coefficients are determined. Correlations with existing experimental evidence is excellent. Analytical results show that the grooved-damper behaves at low frequencies as a single land damper. Dynamic force coefficients are determined to be frequency dependent. Analytical predictions show that the combined action of fluid inertia and groove volume-liquid compressibility affects the force coefficients for dynamic excitation at large frequencies.

  13. Centralized Data-Sampling Approach for Global Ot-α Synchronization of Fractional-Order Neural Networks with Time Delays

    Directory of Open Access Journals (Sweden)

    Jin-E Zhang

    2017-01-01

    Full Text Available In this paper, the global O(t-α synchronization problem is investigated for a class of fractional-order neural networks with time delays. Taking into account both better control performance and energy saving, we make the first attempt to introduce centralized data-sampling approach to characterize the O(t-α synchronization design strategy. A sufficient criterion is given under which the drive-response-based coupled neural networks can achieve global O(t-α synchronization. It is worth noting that, by using centralized data-sampling principle, fractional-order Lyapunov-like technique, and fractional-order Leibniz rule, the designed controller performs very well. Two numerical examples are presented to illustrate the efficiency of the proposed centralized data-sampling scheme.

  14. Assessing a Sport/Cultural Events Network: An Application of Social Network Analysis

    OpenAIRE

    Ziakas, V; Costa, CA

    2009-01-01

    The purpose of this study was to assess the complexity of a sport/cultural events network. To that intent, a social network analysis was conducted in a small community in the US. The study had three main objectives: (1) Examine relationships among organisations involved in planning and implementing sport and cultural events based on their communication, exchange of resources, and assistance; (2) Identify the most important actors within the events network and their relationships; (3) Investig...

  15. Weighted Complex Network Analysis of Shanghai Rail Transit System

    Directory of Open Access Journals (Sweden)

    Yingying Xing

    2016-01-01

    Full Text Available With increasing passenger flows and construction scale, Shanghai rail transit system (RTS has entered a new era of networking operation. In addition, the structure and properties of the RTS network have great implications for urban traffic planning, design, and management. Thus, it is necessary to acquire their network properties and impacts. In this paper, the Shanghai RTS, as well as passenger flows, will be investigated by using complex network theory. Both the topological and dynamic properties of the RTS network are analyzed and the largest connected cluster is introduced to assess the reliability and robustness of the RTS network. Simulation results show that the distribution of nodes strength exhibits a power-law behavior and Shanghai RTS network shows a strong weighted rich-club effect. This study also indicates that the intentional attacks are more detrimental to the RTS network than to the random weighted network, but the random attacks can cause slightly more damage to the random weighted network than to the RTS network. Our results provide a richer view of complex weighted networks in real world and possibilities of risk analysis and policy decisions for the RTS operation department.

  16. xMWAS: a data-driven integration and differential network analysis tool.

    Science.gov (United States)

    Uppal, Karan; Ma, Chunyu; Go, Young-Mi; Jones, Dean P; Wren, Jonathan

    2018-02-15

    Integrative omics is a central component of most systems biology studies. Computational methods are required for extracting meaningful relationships across different omics layers. Various tools have been developed to facilitate integration of paired heterogenous omics data; however most existing tools allow integration of only two omics datasets. Furthermore, existing data integration tools do not incorporate additional steps of identifying sub-networks or communities of highly connected entities and evaluating the topology of the integrative network under different conditions. Here we present xMWAS, a software for data integration, network visualization, clustering, and differential network analysis of data from biochemical and phenotypic assays, and two or more omics platforms. https://kuppal.shinyapps.io/xmwas (Online) and https://github.com/kuppal2/xMWAS/ (R). kuppal2@emory.edu. Supplementary data are available at Bioinformatics online.

  17. System Analysis of LWDH Related Genes Based on Text Mining in Biological Networks

    Directory of Open Access Journals (Sweden)

    Mingzhi Liao

    2014-01-01

    Full Text Available Liuwei-dihuang (LWDH is widely used in traditional Chinese medicine (TCM, but its molecular mechanism about gene interactions is unclear. LWDH genes were extracted from the existing literatures based on text mining technology. To simulate the complex molecular interactions that occur in the whole body, protein-protein interaction networks (PPINs were constructed and the topological properties of LWDH genes were analyzed. LWDH genes have higher centrality properties and may play important roles in the complex biological network environment. It was also found that the distances within LWDH genes are smaller than expected, which means that the communication of LWDH genes during the biological process is rapid and effectual. At last, a comprehensive network of LWDH genes, including the related drugs and regulatory pathways at both the transcriptional and posttranscriptional levels, was constructed and analyzed. The biological network analysis strategy used in this study may be helpful for the understanding of molecular mechanism of TCM.

  18. Implementation of evidence-informed practice through central network actors; a case study of three public health units in Canada.

    Science.gov (United States)

    Yousefi Nooraie, Reza; Marin, Alexandra; Hanneman, Robert; Lohfeld, Lynne; Dobbins, Maureen

    2017-03-15

    Workforce development is an important aspect of evidence-informed decision making (EIDM) interventions. The social position of individuals in formal and informal social networks, and the relevance of formal roles in relation to EIDM are important factors identifying key EIDM players in public health organizations. We assessed the role of central actors in information sharing networks in promoting the adoption of EIDM by the staff of three public health units in Canada, over a two-year period during which an organization-wide intervention was implemented. A multi-faceted and tailored intervention to train select staff applying research evidence in practice was implemented in three public health units in Canada from 2011 to 2013. Staff (n = 572) were asked to identify those in the health unit whom they turned to get help using research in practice, whom they considered as experts in EIDM, and friends. We developed multi-level linear regression models to predict the change in EIDM behavior scores predicted by being connected to peers who were central in networks and were engaged in the intervention. Only the group of highly engaged central actors who were connected to each other, and the staff who were not engaged in the intervention but were connected to highly engaged central actors significantly improved their EIDM behavior scores. Among the latter group, the staff who were also friends with their information sources showed a larger improvement in EIDM behavior. If engaged, central network actors use their formal and informal connections to promote EIDM. Central actors themselves are more likely to adopt EIDM if they communicate with each other. These social communications should be reinforced and supported through the implementation of training interventions as a means to promoting EIDM.

  19. Residue Geometry Networks: A Rigidity-Based Approach to the Amino Acid Network and Evolutionary Rate Analysis

    Science.gov (United States)

    Fokas, Alexander S.; Cole, Daniel J.; Ahnert, Sebastian E.; Chin, Alex W.

    2016-09-01

    Amino acid networks (AANs) abstract the protein structure by recording the amino acid contacts and can provide insight into protein function. Herein, we describe a novel AAN construction technique that employs the rigidity analysis tool, FIRST, to build the AAN, which we refer to as the residue geometry network (RGN). We show that this new construction can be combined with network theory methods to include the effects of allowed conformal motions and local chemical environments. Importantly, this is done without costly molecular dynamics simulations required by other AAN-related methods, which allows us to analyse large proteins and/or data sets. We have calculated the centrality of the residues belonging to 795 proteins. The results display a strong, negative correlation between residue centrality and the evolutionary rate. Furthermore, among residues with high closeness, those with low degree were particularly strongly conserved. Random walk simulations using the RGN were also successful in identifying allosteric residues in proteins involved in GPCR signalling. The dynamic function of these residues largely remain hidden in the traditional distance-cutoff construction technique. Despite being constructed from only the crystal structure, the results in this paper suggests that the RGN can identify residues that fulfil a dynamical function.

  20. Trauma-Exposed Latina Immigrants' Networks: A Social Network Analysis Approach.

    Science.gov (United States)

    Hurtado-de-Mendoza, Alejandra; Serrano, Adriana; Gonzales, Felisa A; Fernandez, Nicole C; Cabling, Mark; Kaltman, Stacey

    2016-11-01

    Trauma exposure among Latina immigrants is common. Social support networks can buffer the impact of trauma on mental health. This study characterizes the social networks of trauma-exposed Latina immigrants using a social network analysis perspective. In 2011-2012 a convenience sample (n=28) of Latina immigrants with trauma exposure and presumptive depression or posttraumatic stress disorder was recruited from a community clinic in Washington DC. Participants completed a social network assessment and listed up to ten persons in their network (alters). E-Net was used to describe the aggregate structural, interactional, and functional characteristics of networks and Node-XL was used in a case study to diagram one network. Most participants listed children (93%), siblings (82%), and friends (71%) as alters, and most alters lived in the US (69%). Perceived emotional support and positive social interaction were higher compared to tangible, language, information, and financial support. A case study illustrates the use of network visualizations to assess the strengths and weaknesses of social networks. Targeted social network interventions to enhance supportive networks among trauma-exposed Latina immigrants are warranted.