Evolution of students’ friendship networks: Examining the influence of group size

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Valentina Sokolovska

2017-01-01

Full Text Available  The main aim of this study was to examine the effect of the network size on formation and evolution of students’ friendship relations. Data was collected from two groups of sociology freshmen: a group from the University of Belgrade, which represents a larger group, and a group from the University of Novi Sad, which represents a smaller group. The data was collected in three periods of one academic year. We analyzed the structural features of students’ networks and constructed a stochastic model of network evolution in order to explore how friendships form and change during one year. The results showed that structural features of the larger and the smaller group differ in each stage of friendship formation. At the beginning of group forming, small world structure was noticeable in the larger group, although full small world structure was not confirmed in both groups. Furthermore, transitivity of triads had effect on the evolution of the larger network, while balance or structural equivalence had effect on the evolution of the smaller network. Results of the structural analysis are in line with findings of the network evolution model and together they provide an insight into how friendship evolves in groups of different sizes.

Transformational leadership and group interaction as climate antecedents: a social network analysis.

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Zohar, Dov; Tenne-Gazit, Orly

2008-07-01

In order to test the social mechanisms through which organizational climate emerges, this article introduces a model that combines transformational leadership and social interaction as antecedents of climate strength (i.e., the degree of within-unit agreement about climate perceptions). Despite their longstanding status as primary variables, both antecedents have received limited empirical research. The sample consisted of 45 platoons of infantry soldiers from 5 different brigades, using safety climate as the exemplar. Results indicate a partially mediated model between transformational leadership and climate strength, with density of group communication network as the mediating variable. In addition, the results showed independent effects for group centralization of the communication and friendship networks, which exerted incremental effects on climate strength over transformational leadership. Whereas centralization of the communication network was found to be negatively related to climate strength, centralization of the friendship network was positively related to it. Theoretical and practical implications are discussed.

Extracting intrinsic functional networks with feature-based group independent component analysis.

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Calhoun, Vince D; Allen, Elena

2013-04-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in functional MRI data. While networks are typically estimated based on the temporal similarity between regions (based on temporal correlation, clustering methods, or independent component analysis [ICA]), some recent work has suggested that these intrinsic networks can be extracted from the inter-subject covariation among highly distilled features, such as amplitude maps reflecting regions modulated by a task or even coordinates extracted from large meta analytic studies. In this paper our goal was to explicitly compare the networks obtained from a first-level ICA (ICA on the spatio-temporal functional magnetic resonance imaging (fMRI) data) to those from a second-level ICA (i.e., ICA on computed features rather than on the first-level fMRI data). Convergent results from simulations, task-fMRI data, and rest-fMRI data show that the second-level analysis is slightly noisier than the first-level analysis but yields strikingly similar patterns of intrinsic networks (spatial correlations as high as 0.85 for task data and 0.65 for rest data, well above the empirical null) and also preserves the relationship of these networks with other variables such as age (for example, default mode network regions tended to show decreased low frequency power for first-level analyses and decreased loading parameters for second-level analyses). In addition, the best-estimated second-level results are those which are the most strongly reflected in the input feature. In summary, the use of feature-based ICA appears to be a valid tool for extracting intrinsic networks. We believe it will become a useful and important approach in the study of the macro

Stochastic Geometric Network Models for Groups of Functional and Structural Connectomes

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Friedman, Eric J.; Landsberg, Adam S.; Owen, Julia P.; Li, Yi-Ou; Mukherjee, Pratik

2014-01-01

Structural and functional connectomes are emerging as important instruments in the study of normal brain function and in the development of new biomarkers for a variety of brain disorders. In contrast to single-network studies that presently dominate the (non-connectome) network literature, connectome analyses typically examine groups of empirical networks and then compare these against standard (stochastic) network models. Current practice in connectome studies is to employ stochastic network models derived from social science and engineering contexts as the basis for the comparison. However, these are not necessarily best suited for the analysis of connectomes, which often contain groups of very closely related networks, such as occurs with a set of controls or a set of patients with a specific disorder. This paper studies important extensions of standard stochastic models that make them better adapted for analysis of connectomes, and develops new statistical fitting methodologies that account for inter-subject variations. The extensions explicitly incorporate geometric information about a network based on distances and inter/intra hemispherical asymmetries (to supplement ordinary degree-distribution information), and utilize a stochastic choice of networks' density levels (for fixed threshold networks) to better capture the variance in average connectivity among subjects. The new statistical tools introduced here allow one to compare groups of networks by matching both their average characteristics and the variations among them. A notable finding is that connectomes have high “smallworldness” beyond that arising from geometric and degree considerations alone. PMID:25067815

Comparing multilayer brain networks between groups: Introducing graph metrics and recommendations.

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Mandke, Kanad; Meier, Jil; Brookes, Matthew J; O'Dea, Reuben D; Van Mieghem, Piet; Stam, Cornelis J; Hillebrand, Arjan; Tewarie, Prejaas

2018-02-01

There is an increasing awareness of the advantages of multi-modal neuroimaging. Networks obtained from different modalities are usually treated in isolation, which is however contradictory to accumulating evidence that these networks show non-trivial interdependencies. Even networks obtained from a single modality, such as frequency-band specific functional networks measured from magnetoencephalography (MEG) are often treated independently. Here, we discuss how a multilayer network framework allows for integration of multiple networks into a single network description and how graph metrics can be applied to quantify multilayer network organisation for group comparison. We analyse how well-known biases for single layer networks, such as effects of group differences in link density and/or average connectivity, influence multilayer networks, and we compare four schemes that aim to correct for such biases: the minimum spanning tree (MST), effective graph resistance cost minimisation, efficiency cost optimisation (ECO) and a normalisation scheme based on singular value decomposition (SVD). These schemes can be applied to the layers independently or to the multilayer network as a whole. For correction applied to whole multilayer networks, only the SVD showed sufficient bias correction. For correction applied to individual layers, three schemes (ECO, MST, SVD) could correct for biases. By using generative models as well as empirical MEG and functional magnetic resonance imaging (fMRI) data, we further demonstrated that all schemes were sensitive to identify network topology when the original networks were perturbed. In conclusion, uncorrected multilayer network analysis leads to biases. These biases may differ between centres and studies and could consequently lead to unreproducible results in a similar manner as for single layer networks. We therefore recommend using correction schemes prior to multilayer network analysis for group comparisons. Copyright © 2017 Elsevier

Grooming network cohesion and the role of individuals in a captive chimpanzee group.

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Kanngiesser, Patricia; Sueur, Cédric; Riedl, Katrin; Grossmann, Johannes; Call, Josep

2011-08-01

Social network analysis offers new tools to study the social structure of primate groups. We used social network analysis to investigate the cohesiveness of a grooming network in a captive chimpanzee group (N = 17) and the role that individuals may play in it. Using data from a year-long observation, we constructed an unweighted social network of preferred grooming interactions by retaining only those dyads that groomed above the group mean. This choice of criterion was validated by the finding that the properties of the unweighted network correlated with the properties of a weighted network (i.e. a network representing the frequency of grooming interactions) constructed from the same data. To investigate group cohesion, we tested the resilience of the unweighted grooming network to the removal of central individuals (i.e. individuals with high betweenness centrality). The network fragmented more after the removal of individuals with high betweenness centrality than after the removal of random individuals. Central individuals played a pivotal role in maintaining the network's cohesiveness, and we suggest that this may be a typical property of affiliative networks like grooming networks. We found that the grooming network correlated with kinship and age, and that individuals with higher social status occupied more central positions in the network. Overall, the grooming network showed a heterogeneous structure, yet did not exhibit scale-free properties similar to many other primate networks. We discuss our results in light of recent findings on animal social networks and chimpanzee grooming. © 2010 Wiley-Liss, Inc.

Understanding Online Health Groups for Depression: Social Network and Linguistic Perspectives.

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Xu, Ronghua; Zhang, Qingpeng

2016-03-10

Mental health problems have become increasingly prevalent in the past decade. With the advance of Web 2.0 technologies, social media present a novel platform for Web users to form online health groups. Members of online health groups discuss health-related issues and mutually help one another by anonymously revealing their mental conditions, sharing personal experiences, exchanging health information, and providing suggestions and support. The conversations in online health groups contain valuable information to facilitate the understanding of their mutual help behaviors and their mental health problems. We aimed to characterize the conversations in a major online health group for major depressive disorder (MDD) patients in a popular Chinese social media platform. In particular, we intended to explain how Web users discuss depression-related issues from the perspective of the social networks and linguistic patterns revealed by the members' conversations. Social network analysis and linguistic analysis were employed to characterize the social structure and linguistic patterns, respectively. Furthermore, we integrated both perspectives to exploit the hidden relations between them. We found an intensive use of self-focus words and negative affect words. In general, group members used a higher proportion of negative affect words than positive affect words. The social network of the MDD group for depression possessed small-world and scale-free properties, with a much higher reciprocity ratio and clustering coefficient value as compared to the networks of other social media platforms and classic network models. We observed a number of interesting relationships, either strong correlations or convergent trends, between the topological properties and linguistic properties of the MDD group members. (1) The MDD group members have the characteristics of self-preoccupation and negative thought content, according to Beck's cognitive theory of depression; (2) the social structure

Social network analysis community detection and evolution

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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

Understanding Process in Group-Based Intervention Delivery: Social Network Analysis and Intra-entity Variability Methods as Windows into the "Black Box".

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Molloy Elreda, Lauren; Coatsworth, J Douglas; Gest, Scott D; Ram, Nilam; Bamberger, Katharine

2016-11-01

Although the majority of evidence-based programs are designed for group delivery, group process and its role in participant outcomes have received little empirical attention. Data were collected from 20 groups of participants (94 early adolescents, 120 parents) enrolled in an efficacy trial of a mindfulness-based adaptation of the Strengthening Families Program (MSFP). Following each weekly session, participants reported on their relations to group members. Social network analysis and methods sensitive to intraindividual variability were integrated to examine weekly covariation between group process and participant progress, and to predict post-intervention outcomes from levels and changes in group process. Results demonstrate hypothesized links between network indices of group process and intervention outcomes and highlight the value of this unique analytic approach to studying intervention group process.

Social Network Analysis as an Analytic Tool for Task Group Research: A Case Study of an Interdisciplinary Community of Practice

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Lockhart, Naorah C.

2017-01-01

Group counselors commonly collaborate in interdisciplinary settings in health care, substance abuse, and juvenile justice. Social network analysis is a methodology rarely used in counseling research yet has potential to examine task group dynamics in new ways. This case study explores the scholarly relationships among 36 members of an…

Combining social and genetic networks to study HIV transmission in mixing risk groups

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Zarrabi, N.; Prosperi, M.C.F.; Belleman, R.G.; Di Giambenedetto, S.; Fabbiani, M.; De Luca, A.; Sloot, P.M.A.

2013-01-01

Reconstruction of HIV transmission networks is important for understanding and preventing the spread of the virus and drug resistant variants. Mixing risk groups is important in network analysis of HIV in order to assess the role of transmission between risk groups in the HIV epidemic. Most of the

The Application of Social Network Analysis to Team Sports

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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…

Communication Network Integration and Group Uniformity in a Complex Organization.

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Danowski, James A.; Farace, Richard V.

This paper contains a discussion of the limitations of research on group processes in complex organizations and the manner in which a procedure for network analysis in on-going systems can reduce problems. The research literature on group uniformity processes and on theoretical models of these processes from an information processing perspective…

Finding Sales Promotion and Making Decision for New Product Based on Group Analysis of Edge-Enhanced Product Networks

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Huang, Yi; Tan, Jianbin; Wu, Bin

A novel method is proposed in this paper to find the promotive relationship of products from a network point of view. Firstly, a product network is built based on the dataset of handsets’ sale information collected from all outlets of a telecom operator of one province of China, with a period from Jan. 2006 to Jul. 2008. Then the edge enhanced model is applied on product network to divide all the products into several groups, according to which each outlet is assigned to class A or class B for a certain handset. Class A is defined as the outlet which sell the certain handset and contains all of handsets of its group, while other situation for class B which sell the certain handset too. It’s shown from the result of analysis on these two kinds of outlets that many handsets are sold better in outlets of class A than that of class B, even though the sales revenue of all these outlets in the time period is close. That is to say the handsets within a group would promote the sale for each other. Furthermore, a method proposed in this paper gives a way to find out the important attributes of the handsets which lead them to br divided into the same group, and it also explains how to add a new handset to an existing group and where would the new handset be sold best.

Custom Ontologies for Expanded Network Analysis

Science.gov (United States)

2006-12-01

for Expanded Network Analysis. In Visualising Network Information (pp. 6-1 – 6-10). Meeting Proceedings RTO-MP-IST-063, Paper 6. Neuilly-sur-Seine...Even to this day, current research groups are working to develop an approach that involves taking all available text, video, imagery and audio and

Integrated Multimedia Based Intelligent Group Decision Support System for Electrical Power Network

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Ajay Kumar Saxena

2002-05-01

Full Text Available Electrical Power Network in recent time requires an intelligent, virtual environment based decision process for the coordination of all its individual elements and the interrelated tasks. Its ultimate goal is to achieve maximum productivity and efficiency through the efficient and effective application of generation, transmission, distribution, pricing and regulatory systems. However, the complexity of electrical power network and the presence of conflicting multiple goals and objectives postulated by various groups emphasized the need of an intelligent group decision support system approach in this field. In this paper, an Integrated Multimedia based Intelligent Group Decision Support System (IM1GDSS is presented, and its main components are analyzed and discussed. In particular attention is focused on the Data Base, Model Base, Central Black Board (CBB and Multicriteria Futuristic Decision Process (MFDP module. The model base interacts with Electrical Power Network Load Forecasting and Planning (EPNLFP Module; Resource Optimization, Modeling and Simulation (ROMAS Module; Electrical Power Network Control and Evaluation Process (EPNCAEP Module, and MFDP Module through CBB for strategic planning, management control, operational planning and transaction processing. The richness of multimedia channels adds a totally new dimension in a group decision making for Electrical Power Network. The proposed IMIGDSS is a user friendly, highly interactive group decision making system, based on efficient intelligent and multimedia communication support for group discussions, retrieval of content and multi criteria decision analysis.

Group colocation behavior in technological social networks.

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Chloë Brown

Full Text Available We analyze two large datasets from technological networks with location and social data: user location records from an online location-based social networking service, and anonymized telecommunications data from a European cellphone operator, in order to investigate the differences between individual and group behavior with respect to physical location. We discover agreements between the two datasets: firstly, that individuals are more likely to meet with one friend at a place they have not visited before, but tend to meet at familiar locations when with a larger group. We also find that groups of individuals are more likely to meet at places that their other friends have visited, and that the type of a place strongly affects the propensity for groups to meet there. These differences between group and solo mobility has potential technological applications, for example, in venue recommendation in location-based social networks.

Group handoff management in low power microcell-femtocell network

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Debashis De

2017-02-01

Full Text Available This paper presents an analytical model of group based hand-off management based on bird flocking behavior. In the proposed scheme, a number of mobile devices form a group if these devices move together for a long time duration. Although call delivery or call generation are performed individually, hand-off is performed in a group. Dynamic group formation, group division and group merging methods are proposed in this paper. From the simulation results it is demonstrated that approximately 75%, 65% and 90% reduction in power, cost and latency consumption can be obtained respectively using group hand-off management. Thus the proposed scheme is referred as green, economic and fast hand-off strategy. In this paper instead of a macrocell network, a microcell-femtocell network is considered as the transmission power of a microcell or a femtocell base station is much less than a macrocell base station. Simulation results present that the microcell-femtocell network achieves approximately 25–55% and 35–55% reduction in power transmission, and 50–65% and 15–45% reduction in path loss than only a macrocell network and macrocell-femtocell network respectively. Thus microcell-femtocell network is a power-efficient network.

Robustness and Vulnerability of Networks with Dynamical Dependency Groups.

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Bai, Ya-Nan; Huang, Ning; Wang, Lei; Wu, Zhi-Xi

2016-11-28

The dependency property and self-recovery of failure nodes both have great effects on the robustness of networks during the cascading process. Existing investigations focused mainly on the failure mechanism of static dependency groups without considering the time-dependency of interdependent nodes and the recovery mechanism in reality. In this study, we present an evolving network model consisting of failure mechanisms and a recovery mechanism to explore network robustness, where the dependency relations among nodes vary over time. Based on generating function techniques, we provide an analytical framework for random networks with arbitrary degree distribution. In particular, we theoretically find that an abrupt percolation transition exists corresponding to the dynamical dependency groups for a wide range of topologies after initial random removal. Moreover, when the abrupt transition point is above the failure threshold of dependency groups, the evolving network with the larger dependency groups is more vulnerable; when below it, the larger dependency groups make the network more robust. Numerical simulations employing the Erdős-Rényi network and Barabási-Albert scale free network are performed to validate our theoretical results.

Analysis of core-periphery organization in protein contact networks reveals groups of structurally and functionally critical residues.

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Isaac, Arnold Emerson; Sinha, Sitabhra

2015-10-01

The representation of proteins as networks of interacting amino acids, referred to as protein contact networks (PCN), and their subsequent analyses using graph theoretic tools, can provide novel insights into the key functional roles of specific groups of residues. We have characterized the networks corresponding to the native states of 66 proteins (belonging to different families) in terms of their core-periphery organization. The resulting hierarchical classification of the amino acid constituents of a protein arranges the residues into successive layers - having higher core order - with increasing connection density, ranging from a sparsely linked periphery to a densely intra-connected core (distinct from the earlier concept of protein core defined in terms of the three-dimensional geometry of the native state, which has least solvent accessibility). Our results show that residues in the inner cores are more conserved than those at the periphery. Underlining the functional importance of the network core, we see that the receptor sites for known ligand molecules of most proteins occur in the innermost core. Furthermore, the association of residues with structural pockets and cavities in binding or active sites increases with the core order. From mutation sensitivity analysis, we show that the probability of deleterious or intolerant mutations also increases with the core order. We also show that stabilization centre residues are in the innermost cores, suggesting that the network core is critically important in maintaining the structural stability of the protein. A publicly available Web resource for performing core-periphery analysis of any protein whose native state is known has been made available by us at http://www.imsc.res.in/ ~sitabhra/proteinKcore/index.html.

Pro-eating disorder communities on social networking sites: a content analysis.

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Juarascio, Adrienne S; Shoaib, Amber; Timko, C Alix

2010-01-01

The purpose of this study was to assess the number of pro-ana groups on social networking sites and to analyze their content. A general inductive approach was used to analyze the content. Two main themes emerged from the content analysis: social support and eating disorder specific content. Themes were similar across all groups; however, a linguistic analysis indicated differences between groups on the two different networking sites. There was an absence of content typically found on Internet sites. Pro-ana groups on social networking sites are focused on social interactions, and lack eating disorder specific content found on Internet sites.

The case of the religious network group.

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Friedman, R

1999-01-01

GenCorp, a Connecticut-based paper-goods manufacturer, has long supported employee-organized network groups. Its social support group for African-Americans, in fact, has been a particular success, having provided black employees with opportunities to further enhance their careers and helped the company retain top talent, meet its EEO goals, and gain favorable publicity. So when Alice Lawrence, a top accountant at GenCorp, called general manager Bill Thompson about the Christian network group being organized in one of the company's southern plants, Bill hardly flinched. After all, the Christian group was being organized by Russell Kramer, one of the company's most effective plant managers. What could be the problem there? But a couple of years ago, Alice noted, Russell had sent around a companywide letter that talked about the sinful nature of homosexuality. And that letter has made her and other gay and lesbian employees terribly uneasy. To complicate matters, the issue of "Christian rights" in the workplace was being widely discussed on radio talk shows, and several books on the topic had recently been published. An employee had even called the new region's head of human resources to get clarification on the topic. Up until now, GenCorp hadn't placed a lot of restrictions on network groups. But the emergence of a religious group was raising new questions for GenCorp's managers. Should the company accept religious groups or try to stop them? What policy, if any, should GenCorp adopt toward these network groups? Five experts comment on this fictional case study.

3rd International Conference on Network Analysis

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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...

Dynamical networks of influence in small group discussions.

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Moussaïd, Mehdi; Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network-a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences.

Group Recommendation Systems Based on External Social-Trust Networks

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Guang Fang

2018-01-01

Full Text Available With the development of social networks and online mobile communities, group recommendation systems support users’ interaction with similar interests or purposes with others. We often provide some advices to the close friends, such as listening to favorite music and sharing favorite dishes. However, users’ personalities have been ignored by the traditional group recommendation systems while the majority is satisfied. In this paper, a method of group recommendation based on external social-trust networks is proposed, which builds a group profile by analyzing not only users’ preferences, but also the social relationships between members inside and outside of the group. We employ the users’ degree of disagreement to adjust group preference rating by external information of social-trust network. Moreover, having a discussion about different social network utilization ratio, we proposed a method to work for smaller group size. The experimental results show that the proposed method has consistently higher precision and leads to satisfactory recommendations for groups.

A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies.

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Liang, Xiaoyun; Vaughan, David N; Connelly, Alan; Calamante, Fernando

2018-05-01

The conventional way to estimate functional networks is primarily based on Pearson correlation along with classic Fisher Z test. In general, networks are usually calculated at the individual-level and subsequently aggregated to obtain group-level networks. However, such estimated networks are inevitably affected by the inherent large inter-subject variability. A joint graphical model with Stability Selection (JGMSS) method was recently shown to effectively reduce inter-subject variability, mainly caused by confounding variations, by simultaneously estimating individual-level networks from a group. However, its benefits might be compromised when two groups are being compared, given that JGMSS is blinded to other groups when it is applied to estimate networks from a given group. We propose a novel method for robustly estimating networks from two groups by using group-fused multiple graphical-lasso combined with stability selection, named GMGLASS. Specifically, by simultaneously estimating similar within-group networks and between-group difference, it is possible to address inter-subject variability of estimated individual networks inherently related with existing methods such as Fisher Z test, and issues related to JGMSS ignoring between-group information in group comparisons. To evaluate the performance of GMGLASS in terms of a few key network metrics, as well as to compare with JGMSS and Fisher Z test, they are applied to both simulated and in vivo data. As a method aiming for group comparison studies, our study involves two groups for each case, i.e., normal control and patient groups; for in vivo data, we focus on a group of patients with right mesial temporal lobe epilepsy.

Identifying groups of critical edges in a realistic electrical network by multi-objective genetic algorithms

International Nuclear Information System (INIS)

Zio, E.; Golea, L.R.; Rocco S, C.M.

2012-01-01

In this paper, an analysis of the vulnerability of the Italian high-voltage (380 kV) electrical transmission network (HVIET) is carried out for the identification of the groups of links (or edges, or arcs) most critical considering the network structure and flow. Betweenness centrality and network connection efficiency variations are considered as measures of the importance of the network links. The search of the most critical ones is carried out within a multi-objective optimization problem aimed at the maximization of the importance of the groups and minimization of their dimension. The problem is solved using a genetic algorithm. The analysis is based only on information on the topology of the network and leads to the identification of the most important single component, couples of components, triplets and so forth. The comparison of the results obtained with those reported by previous analyses indicates that the proposed approach provides useful complementary information.

The role of bridging organizations in environmental management: examining social networks in working groups

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Adam A. Kowalski

2015-06-01

Full Text Available The linkage of diverse sets of actors and knowledge systems across management levels and institutional boundaries often poses one of the greatest challenges in adaptive management of natural resources. Bridging organizations can facilitate interactions among actors in management settings by lowering the transaction costs of collaboration. The Center for Ocean Solutions (COS is an example of a bridging organization that is focused on linking actors within the ocean sciences and governance arena through the use of working groups. This research examines how network connections between group members affect working group functionality and, more specifically, whether cohesive network structures allow groups to more effectively achieve their goals and objectives. A mixed-methods approach, incorporating both qualitative and quantitative data collection and analysis methods, is employed to understand the structural characteristics of COS working groups. The study finds that cohesive network structures are not associated with increased working group functionality. Strong, centralized leadership is a better predictor of working group success in achieving goals and objectives.

Dual adjacency matrix : exploring link groups in dense networks

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Dinkla, K.; Henry Riche, N.; Westenberg, M.A.

2015-01-01

Node grouping is a common way of adding structure and information to networks that aids their interpretation. However, certain networks benefit from the grouping of links instead of nodes. Link communities, for example, are a form of link groups that describe high-quality overlapping node

Research collaboration in groups and networks: differences across academic fields.

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Kyvik, Svein; Reymert, Ingvild

2017-01-01

The purpose of this paper is to give a macro-picture of collaboration in research groups and networks across all academic fields in Norwegian research universities, and to examine the relative importance of membership in groups and networks for individual publication output. To our knowledge, this is a new approach, which may provide valuable information on collaborative patterns in a particular national system, but of clear relevance to other national university systems. At the system level, conducting research in groups and networks are equally important, but there are large differences between academic fields. The research group is clearly most important in the field of medicine and health, while undertaking research in an international network is most important in the natural sciences. Membership in a research group and active participation in international networks are likely to enhance publication productivity and the quality of research.

GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.

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Hosseini, S M Hadi; Hoeft, Fumiko; Kesler, Shelli R

2012-01-01

In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT) that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI) that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC) and functional data analyses (FDA), in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL) and healthy matched Controls (CON). The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.

GAT: a graph-theoretical analysis toolbox for analyzing between-group differences in large-scale structural and functional brain networks.

Directory of Open Access Journals (Sweden)

S M Hadi Hosseini

Full Text Available In recent years, graph theoretical analyses of neuroimaging data have increased our understanding of the organization of large-scale structural and functional brain networks. However, tools for pipeline application of graph theory for analyzing topology of brain networks is still lacking. In this report, we describe the development of a graph-analysis toolbox (GAT that facilitates analysis and comparison of structural and functional network brain networks. GAT provides a graphical user interface (GUI that facilitates construction and analysis of brain networks, comparison of regional and global topological properties between networks, analysis of network hub and modules, and analysis of resilience of the networks to random failure and targeted attacks. Area under a curve (AUC and functional data analyses (FDA, in conjunction with permutation testing, is employed for testing the differences in network topologies; analyses that are less sensitive to the thresholding process. We demonstrated the capabilities of GAT by investigating the differences in the organization of regional gray-matter correlation networks in survivors of acute lymphoblastic leukemia (ALL and healthy matched Controls (CON. The results revealed an alteration in small-world characteristics of the brain networks in the ALL survivors; an observation that confirm our hypothesis suggesting widespread neurobiological injury in ALL survivors. Along with demonstration of the capabilities of the GAT, this is the first report of altered large-scale structural brain networks in ALL survivors.

Functional Module Analysis for Gene Coexpression Networks with Network Integration.

Science.gov (United States)

Zhang, Shuqin; Zhao, Hongyu; Ng, Michael K

2015-01-01

Network has been a general tool for studying the complex interactions between different genes, proteins, and other small molecules. Module as a fundamental property of many biological networks has been widely studied and many computational methods have been proposed to identify the modules in an individual network. However, in many cases, a single network is insufficient for module analysis due to the noise in the data or the tuning of parameters when building the biological network. The availability of a large amount of biological networks makes network integration study possible. By integrating such networks, more informative modules for some specific disease can be derived from the networks constructed from different tissues, and consistent factors for different diseases can be inferred. In this paper, we have developed an effective method for module identification from multiple networks under different conditions. The problem is formulated as an optimization model, which combines the module identification in each individual network and alignment of the modules from different networks together. An approximation algorithm based on eigenvector computation is proposed. Our method outperforms the existing methods, especially when the underlying modules in multiple networks are different in simulation studies. We also applied our method to two groups of gene coexpression networks for humans, which include one for three different cancers, and one for three tissues from the morbidly obese patients. We identified 13 modules with three complete subgraphs, and 11 modules with two complete subgraphs, respectively. The modules were validated through Gene Ontology enrichment and KEGG pathway enrichment analysis. We also showed that the main functions of most modules for the corresponding disease have been addressed by other researchers, which may provide the theoretical basis for further studying the modules experimentally.

Social networks improve leaderless group navigation by facilitating long-distance communication

Directory of Open Access Journals (Sweden)

Nikolai W. F. BODE, A. Jamie WOOD, Daniel W. FRANKS

2012-04-01

Full Text Available Group navigation is of great importance for many animals, such as migrating flocks of birds or shoals of fish. One theory states that group membership can improve navigational accuracy compared to limited or less accurate individual navigational ability in groups without leaders (“Many-wrongs principle”. Here, we simulate leaderless group navigation that includes social connections as preferential interactions between individuals. Our results suggest that underlying social networks can reduce navigational errors of groups and increase group cohesion. We use network summary statistics, in particular network motifs, to study which characteristics of networks lead to these improvements. It is networks in which preferences between individuals are not clustered, but spread evenly across the group that are advantageous in group navigation by effectively enhancing long-distance information exchange within groups. We suggest that our work predicts a base-line for the type of social structure we might expect to find in group-living animals that navigate without leaders [Current Zoology 58 (2: 329-341, 2012].

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study.

Science.gov (United States)

Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias; Salanti, Georgia

2018-02-28

strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing the treatments of interest continued to be published for eight comparisons after strong evidence had become evident in network meta-analysis. In comparative effectiveness research, prospectively planned living network meta-analyses produced strong evidence against the null hypothesis more often and earlier than conventional, pairwise meta-analyses. 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.

A permutation testing framework to compare groups of brain networks.

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Simpson, Sean L; Lyday, Robert G; Hayasaka, Satoru; Marsh, Anthony P; Laurienti, Paul J

2013-01-01

Brain network analyses have moved to the forefront of neuroimaging research over the last decade. However, methods for statistically comparing groups of networks have lagged behind. These comparisons have great appeal for researchers interested in gaining further insight into complex brain function and how it changes across different mental states and disease conditions. Current comparison approaches generally either rely on a summary metric or on mass-univariate nodal or edge-based comparisons that ignore the inherent topological properties of the network, yielding little power and failing to make network level comparisons. Gleaning deeper insights into normal and abnormal changes in complex brain function demands methods that take advantage of the wealth of data present in an entire brain network. Here we propose a permutation testing framework that allows comparing groups of networks while incorporating topological features inherent in each individual network. We validate our approach using simulated data with known group differences. We then apply the method to functional brain networks derived from fMRI data.

Why social network analysis is important to Air Force applications

Science.gov (United States)

Havig, Paul R.; McIntire, John P.; Geiselman, Eric; Mohd-Zaid, Fairul

2012-06-01

Social network analysis is a powerful tool used to help analysts discover relationships amongst groups of people as well as individuals. It is the mathematics behind such social networks as Facebook and MySpace. These networks alone cause a huge amount of data to be generated and the issue is only compounded once one adds in other electronic media such as e-mails and twitter. In this paper we outline the basics of social network analysis and how it may be used in current and future Air Force applications.

Grouping by association: using associative networks for document categorization

NARCIS (Netherlands)

Bloom, Niels

2015-01-01

In this thesis we describe a method of using associative networks for automatic doc- ument grouping. Associative networks are networks of ideas or concepts in which each concept is linked to concepts that are semantically similar to it. By activating concepts in the network based on the text of a

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.

Statistical assessment of crosstalk enrichment between gene groups in biological networks.

Science.gov (United States)

McCormack, Theodore; Frings, Oliver; Alexeyenko, Andrey; Sonnhammer, Erik L L

2013-01-01

Analyzing groups of functionally coupled genes or proteins in the context of global interaction networks has become an important aspect of bioinformatic investigations. Assessing the statistical significance of crosstalk enrichment between or within groups of genes can be a valuable tool for functional annotation of experimental gene sets. Here we present CrossTalkZ, a statistical method and software to assess the significance of crosstalk enrichment between pairs of gene or protein groups in large biological networks. We demonstrate that the standard z-score is generally an appropriate and unbiased statistic. We further evaluate the ability of four different methods to reliably recover crosstalk within known biological pathways. We conclude that the methods preserving the second-order topological network properties perform best. Finally, we show how CrossTalkZ can be used to annotate experimental gene sets using known pathway annotations and that its performance at this task is superior to gene enrichment analysis (GEA). CrossTalkZ (available at http://sonnhammer.sbc.su.se/download/software/CrossTalkZ/) is implemented in C++, easy to use, fast, accepts various input file formats, and produces a number of statistics. These include z-score, p-value, false discovery rate, and a test of normality for the null distributions.

Economic assessment group on power transmission and distribution networks tariffs

International Nuclear Information System (INIS)

2000-06-01

Facing the new law on the electric power market liberalization, the french government created an experts group to analyze solutions and assessment methods of the electrical networks costs and tariffs and to control their efficiency. This report presents the analysis and the conclusions of the group. It concerns the three main subjects: the regulation context, the tariffing of the electric power transmission and distribution (the cost and efficiency of the various options) and the tariffing of the electric power supply to the eligible consumers. The authors provide a guideline for a tariffing policy. (A.L.B.)

A Group Neighborhood Average Clock Synchronization Protocol for Wireless Sensor Networks

Science.gov (United States)

Lin, Lin; Ma, Shiwei; Ma, Maode

2014-01-01

Clock synchronization is a very important issue for the applications of wireless sensor networks. The sensors need to keep a strict clock so that users can know exactly what happens in the monitoring area at the same time. This paper proposes a novel internal distributed clock synchronization solution using group neighborhood average. Each sensor node collects the offset and skew rate of the neighbors. Group averaging of offset and skew rate value are calculated instead of conventional point-to-point averaging method. The sensor node then returns compensated value back to the neighbors. The propagation delay is considered and compensated. The analytical analysis of offset and skew compensation is presented. Simulation results validate the effectiveness of the protocol and reveal that the protocol allows sensor networks to quickly establish a consensus clock and maintain a small deviation from the consensus clock. PMID:25120163

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.

2012-01-01

Aims 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. Design Egocentric social network analysis was used to formally characterize the relationships between social network characteristics and gambling pathology. Setting Laboratory-based questionnaire and interview administration. Participants Forty frequent gamblers (22 non-pathological gamblers, 18 pathological gamblers) were recruited from the community. Findings 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 nonpathological gamblers (NPGs). PGs had more individuals in their network with whom they personally gambled, smoked, and drank with 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. Conclusions 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. PMID:23072641

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.

Orthoscape: a cytoscape application for grouping and visualization KEGG based gene networks by taxonomy and homology principles.

Science.gov (United States)

Mustafin, Zakhar Sergeevich; Lashin, Sergey Alexandrovich; Matushkin, Yury Georgievich; Gunbin, Konstantin Vladimirovich; Afonnikov, Dmitry Arkadievich

2017-01-27

There are many available software tools for visualization and analysis of biological networks. Among them, Cytoscape ( http://cytoscape.org/ ) is one of the most comprehensive packages, with many plugins and applications which extends its functionality by providing analysis of protein-protein interaction, gene regulatory and gene co-expression networks, metabolic, signaling, neural as well as ecological-type networks including food webs, communities networks etc. Nevertheless, only three plugins tagged 'network evolution' found in Cytoscape official app store and in literature. We have developed a new Cytoscape 3.0 application Orthoscape aimed to facilitate evolutionary analysis of gene networks and visualize the results. Orthoscape aids in analysis of evolutionary information available for gene sets and networks by highlighting: (1) the orthology relationships between genes; (2) the evolutionary origin of gene network components; (3) the evolutionary pressure mode (diversifying or stabilizing, negative or positive selection) of orthologous groups in general and/or branch-oriented mode. The distinctive feature of Orthoscape is the ability to control all data analysis steps via user-friendly interface. Orthoscape allows its users to analyze gene networks or separated gene sets in the context of evolution. At each step of data analysis, Orthoscape also provides for convenient visualization and data manipulation.

Small diameter symmetric networks from linear groups

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Campbell, Lowell; Carlsson, Gunnar E.; Dinneen, Michael J.; Faber, Vance; Fellows, Michael R.; Langston, Michael A.; Moore, James W.; Multihaupt, Andrew P.; Sexton, Harlan B.

1992-01-01

In this note is reported a collection of constructions of symmetric networks that provide the largest known values for the number of nodes that can be placed in a network of a given degree and diameter. Some of the constructions are in the range of current potential engineering significance. The constructions are Cayley graphs of linear groups obtained by experimental computation.

GGOS working group on ground networks and communications

Science.gov (United States)

Pearlman, M.; Altamimi, Z.; Beck, N.; Forsberg, R.; Gurtner, W.; Kenyon, S.; Behrend, D.; Lemoine, F. G.; Ma, C.; Noll, C. E.;

Analysis of the airport network of India as a complex weighted network

Science.gov (United States)

Bagler, Ganesh

2008-05-01

Transportation infrastructure of a country is one of the most important indicators of its economic growth. Here we study the Airport Network of India (ANI) which represents India’s domestic civil aviation infrastructure as a complex network. We find that ANI, a network of domestic airports connected by air links, is a small-world network characterized by a truncated power-law degree distribution and has a signature of hierarchy. We investigate ANI as a weighted network to explore its various properties and compare them with their topological counterparts. The traffic in ANI, as in the World-wide Airport Network (WAN), is found to be accumulated on interconnected groups of airports and is concentrated between large airports. In contrast to WAN, ANI is found to be having disassortative mixing which is offset by the traffic dynamics. The analysis indicates possible mechanism of formation of a national transportation network, which is different from that on a global scale.

A Group Neighborhood Average Clock Synchronization Protocol for Wireless Sensor Networks

Directory of Open Access Journals (Sweden)

Lin Lin

2014-08-01

Full Text Available Clock synchronization is a very important issue for the applications of wireless sensor networks. The sensors need to keep a strict clock so that users can know exactly what happens in the monitoring area at the same time. This paper proposes a novel internal distributed clock synchronization solution using group neighborhood average. Each sensor node collects the offset and skew rate of the neighbors. Group averaging of offset and skew rate value are calculated instead of conventional point-to-point averaging method. The sensor node then returns compensated value back to the neighbors. The propagation delay is considered and compensated. The analytical analysis of offset and skew compensation is presented. Simulation results validate the effectiveness of the protocol and reveal that the protocol allows sensor networks to quickly establish a consensus clock and maintain a small deviation from the consensus clock.

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

s-core network decomposition: A generalization of k-core analysis to weighted networks

Science.gov (United States)

Eidsaa, Marius; Almaas, Eivind

2013-12-01

A broad range of systems spanning biology, technology, and social phenomena may be represented and analyzed as complex networks. Recent studies of such networks using k-core decomposition have uncovered groups of nodes that play important roles. Here, we present s-core analysis, a generalization of k-core (or k-shell) analysis to complex networks where the links have different strengths or weights. We demonstrate the s-core decomposition approach on two random networks (ER and configuration model with scale-free degree distribution) where the link weights are (i) random, (ii) correlated, and (iii) anticorrelated with the node degrees. Finally, we apply the s-core decomposition approach to the protein-interaction network of the yeast Saccharomyces cerevisiae in the context of two gene-expression experiments: oxidative stress in response to cumene hydroperoxide (CHP), and fermentation stress response (FSR). We find that the innermost s-cores are (i) different from innermost k-cores, (ii) different for the two stress conditions CHP and FSR, and (iii) enriched with proteins whose biological functions give insight into how yeast manages these specific stresses.

Why Failing Terrorist Groups Persist Revisited: A Social Network Approach to AQIM Network Resilience

Science.gov (United States)

2017-12-01

the approach and methods used in this analysis to organize, analyze, and explore the geospatial, statistical , and social network data...requirements for the degree of MASTER OF SCIENCE IN INFORMATION STRATEGY AND POLITICAL WARFARE from the NAVAL POSTGRADUATE SCHOOL December...research utilizes both descriptive statistics and regression analysis of social network data to explore the changes within the AQIM network 2012

Modelling animal group fission using social network dynamics.

Directory of Open Access Journals (Sweden)

Cédric Sueur

Full Text Available Group life involves both advantages and disadvantages, meaning that individuals have to compromise between their nutritional needs and their social links. When a compromise is impossible, the group splits in order to reduce conflict of interests and favour positive social interactions between its members. In this study we built a dynamic model of social networks to represent a succession of temporary fissions involving a change in social relations that could potentially lead to irreversible group fission (i.e. no more group fusion. This is the first study that assesses how a social network changes according to group fission-fusion dynamics. We built a model that was based on different parameters: the group size, the influence of nutritional needs compared to social needs, and the changes in the social network after a temporary fission. The results obtained from this theoretical data indicate how the percentage of social relation transfer, the number of individuals and the relative importance of nutritional requirements and social links influence the average number of days before irreversible fission occurs. The greater the nutritional needs and the higher the transfer of social relations during temporary fission, the fewer days will be observed before an irreversible fission. It is crucial to bridge the gap between the individual and the population level if we hope to understand how simple, local interactions may drive ecological systems.

Network formation under heterogeneous costs: The multiple group model

NARCIS (Netherlands)

Kamphorst, J.J.A.; van der Laan, G.

2007-01-01

It is widely recognized that the shape of networks influences both individual and aggregate behavior. This raises the question which types of networks are likely to arise. In this paper we investigate a model of network formation, where players are divided into groups and the costs of a link between

Trust and compactness in social network groups.

Science.gov (United States)

De Meo, Pasquale; Ferrara, Emilio; Rosaci, Domenico; Sarné, Giuseppe M L

2015-02-01

Understanding the dynamics behind group formation and evolution in social networks is considered an instrumental milestone to better describe how individuals gather and form communities, how they enjoy and share the platform contents, how they are driven by their preferences/tastes, and how their behaviors are influenced by peers. In this context, the notion of compactness of a social group is particularly relevant. While the literature usually refers to compactness as a measure to merely determine how much members of a group are similar among each other, we argue that the mutual trustworthiness between the members should be considered as an important factor in defining such a term. In fact, trust has profound effects on the dynamics of group formation and their evolution: individuals are more likely to join with and stay in a group if they can trust other group members. In this paper, we propose a quantitative measure of group compactness that takes into account both the similarity and the trustworthiness among users, and we present an algorithm to optimize such a measure. We provide empirical results, obtained from the real social networks EPINIONS and CIAO, that compare our notion of compactness versus the traditional notion of user similarity, clearly proving the advantages of our approach.

Social network analysis for startups finding connections on the social web

CERN Document Server

Tsvetovat, Maksim

2011-01-01

Does your startup rely on social network analysis? This concise guide provides a statistical framework to help you identify social processes hidden among the tons of data now available. Social network analysis (SNA) is a discipline that predates Facebook and Twitter by 30 years. Through expert SNA researchers, you''ll learn concepts and techniques for recognizing patterns in social media, political groups, companies, cultural trends, and interpersonal networks. You''ll also learn how to use Python and other open source tools-such as NetworkX, NumPy, and Matplotlib-to gather, analyze, and vis

Evaluating the Quality of Evidence from a Network Meta-Analysis

Science.gov (United States)

Salanti, Georgia; Del Giovane, Cinzia; Chaimani, Anna; Caldwell, Deborah M.; Higgins, Julian P. T.

2014-01-01

Systematic reviews that collate data about the relative effects of multiple interventions via network meta-analysis are highly informative for decision-making purposes. A network meta-analysis provides two types of findings for a specific outcome: the relative treatment effect for all pairwise comparisons, and a ranking of the treatments. It is important to consider the confidence with which these two types of results can enable clinicians, policy makers and patients to make informed decisions. We propose an approach to determining confidence in the output of a network meta-analysis. Our proposed approach is based on methodology developed by the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group for pairwise meta-analyses. The suggested framework for evaluating a network meta-analysis acknowledges (i) the key role of indirect comparisons (ii) the contributions of each piece of direct evidence to the network meta-analysis estimates of effect size; (iii) the importance of the transitivity assumption to the validity of network meta-analysis; and (iv) the possibility of disagreement between direct evidence and indirect evidence. We apply our proposed strategy to a systematic review comparing topical antibiotics without steroids for chronically discharging ears with underlying eardrum perforations. The proposed framework can be used to determine confidence in the results from a network meta-analysis. Judgements about evidence from a network meta-analysis can be different from those made about evidence from pairwise meta-analyses. PMID:24992266

Consensus formation on coevolving networks: groups' formation and structure

International Nuclear Information System (INIS)

Kozma, Balazs; Barrat, Alain

2008-01-01

We study the effect of adaptivity on a social model of opinion dynamics and consensus formation. We analyse how the adaptivity of the network of contacts between agents to the underlying social dynamics affects the size and topological properties of groups and the convergence time to the stable final state. We find that, while on static networks these properties are determined by percolation phenomena, on adaptive networks the rewiring process leads to different behaviors: adaptive rewiring fosters group formation by enhancing communication between agents of similar opinion, though it also makes possible the division of clusters. We show how the convergence time is determined by the characteristic time of link rearrangement. We finally investigate how the adaptivity yields nontrivial correlations between the internal topology and the size of the groups of agreeing agents

Spontaneous formation of dynamical groups in an adaptive networked system

International Nuclear Information System (INIS)

Li Menghui; Guan Shuguang; Lai, C-H

2010-01-01

In this work, we investigate a model of an adaptive networked dynamical system, where the coupling strengths among phase oscillators coevolve with the phase states. It is shown that in this model the oscillators can spontaneously differentiate into two dynamical groups after a long time evolution. Within each group, the oscillators have similar phases, while oscillators in different groups have approximately opposite phases. The network gradually converts from the initial random structure with a uniform distribution of connection strengths into a modular structure that is characterized by strong intra-connections and weak inter-connections. Furthermore, the connection strengths follow a power-law distribution, which is a natural consequence of the coevolution of the network and the dynamics. Interestingly, it is found that if the inter-connections are weaker than a certain threshold, the two dynamical groups will almost decouple and evolve independently. These results are helpful in further understanding the empirical observations in many social and biological networks.

How can social network analysis contribute to social behavior research in applied ethology?

Science.gov (United States)

Makagon, Maja M; McCowan, Brenda; Mench, Joy A

2012-05-01

Social network analysis is increasingly used by behavioral ecologists and primatologists to describe the patterns and quality of interactions among individuals. We provide an overview of this methodology, with examples illustrating how it can be used to study social behavior in applied contexts. Like most kinds of social interaction analyses, social network analysis provides information about direct relationships (e.g. dominant-subordinate relationships). However, it also generates a more global model of social organization that determines how individual patterns of social interaction relate to individual and group characteristics. A particular strength of this approach is that it provides standardized mathematical methods for calculating metrics of sociality across levels of social organization, from the population and group levels to the individual level. At the group level these metrics can be used to track changes in social network structures over time, evaluate the effect of the environment on social network structure, or compare social structures across groups, populations or species. At the individual level, the metrics allow quantification of the heterogeneity of social experience within groups and identification of individuals who may play especially important roles in maintaining social stability or information flow throughout the network.

Graph theoretical analysis of functional network for comprehension of sign language.

Science.gov (United States)

Liu, Lanfang; Yan, Xin; Liu, Jin; Xia, Mingrui; Lu, Chunming; Emmorey, Karen; Chu, Mingyuan; Ding, Guosheng

2017-09-15

Signed languages are natural human languages using the visual-motor modality. Previous neuroimaging studies based on univariate activation analysis show that a widely overlapped cortical network is recruited regardless whether the sign language is comprehended (for signers) or not (for non-signers). Here we move beyond previous studies by examining whether the functional connectivity profiles and the underlying organizational structure of the overlapped neural network may differ between signers and non-signers when watching sign language. Using graph theoretical analysis (GTA) and fMRI, we compared the large-scale functional network organization in hearing signers with non-signers during the observation of sentences in Chinese Sign Language. We found that signed sentences elicited highly similar cortical activations in the two groups of participants, with slightly larger responses within the left frontal and left temporal gyrus in signers than in non-signers. Crucially, further GTA revealed substantial group differences in the topologies of this activation network. Globally, the network engaged by signers showed higher local efficiency (t (24) =2.379, p=0.026), small-worldness (t (24) =2.604, p=0.016) and modularity (t (24) =3.513, p=0.002), and exhibited different modular structures, compared to the network engaged by non-signers. Locally, the left ventral pars opercularis served as a network hub in the signer group but not in the non-signer group. These findings suggest that, despite overlap in cortical activation, the neural substrates underlying sign language comprehension are distinguishable at the network level from those for the processing of gestural action. Copyright © 2017 Elsevier B.V. All rights reserved.

Empirical Studies on the Network of Social Groups: The Case of Tencent QQ.

Science.gov (United States)

You, Zhi-Qiang; Han, Xiao-Pu; Lü, Linyuan; Yeung, Chi Ho

2015-01-01

Participation in social groups are important but the collective behaviors of human as a group are difficult to analyze due to the difficulties to quantify ordinary social relation, group membership, and to collect a comprehensive dataset. Such difficulties can be circumvented by analyzing online social networks. In this paper, we analyze a comprehensive dataset released from Tencent QQ, an instant messenger with the highest market share in China. Specifically, we analyze three derivative networks involving groups and their members-the hypergraph of groups, the network of groups and the user network-to reveal social interactions at microscopic and mesoscopic level. Our results uncover interesting behaviors on the growth of user groups, the interactions between groups, and their relationship with member age and gender. These findings lead to insights which are difficult to obtain in social networks based on personal contacts.

Network Analysis Tools: from biological networks to clusters and pathways.

Science.gov (United States)

Brohée, Sylvain; Faust, Karoline; Lima-Mendez, Gipsi; Vanderstocken, Gilles; van Helden, Jacques

2008-01-01

Network Analysis Tools (NeAT) is a suite of computer tools that integrate various algorithms for the analysis of biological networks: comparison between graphs, between clusters, or between graphs and clusters; network randomization; analysis of degree distribution; network-based clustering and path finding. The tools are interconnected to enable a stepwise analysis of the network through a complete analytical workflow. In this protocol, we present a typical case of utilization, where the tasks above are combined to decipher a protein-protein interaction network retrieved from the STRING database. The results returned by NeAT are typically subnetworks, networks enriched with additional information (i.e., clusters or paths) or tables displaying statistics. Typical networks comprising several thousands of nodes and arcs can be analyzed within a few minutes. The complete protocol can be read and executed in approximately 1 h.

Ecological network analysis for a virtual water network.

Science.gov (United States)

Fang, Delin; Chen, Bin

2015-06-02

The notions of virtual water flows provide important indicators to manifest the water consumption and allocation between different sectors via product transactions. However, the configuration of virtual water network (VWN) still needs further investigation to identify the water interdependency among different sectors as well as the network efficiency and stability in a socio-economic system. Ecological network analysis is chosen as a useful tool to examine the structure and function of VWN and the interactions among its sectors. A balance analysis of efficiency and redundancy is also conducted to describe the robustness (RVWN) of VWN. Then, network control analysis and network utility analysis are performed to investigate the dominant sectors and pathways for virtual water circulation and the mutual relationships between pairwise sectors. A case study of the Heihe River Basin in China shows that the balance between efficiency and redundancy is situated on the left side of the robustness curve with less efficiency and higher redundancy. The forestation, herding and fishing sectors and industrial sectors are found to be the main controllers. The network tends to be more mutualistic and synergic, though some competitive relationships that weaken the virtual water circulation still exist.

Application of neural networks to group technology

Science.gov (United States)

Caudell, Thomas P.; Smith, Scott D. G.; Johnson, G. C.; Wunsch, Donald C., II

1991-08-01

Adaptive resonance theory (ART) neural networks are being developed for application to the industrial engineering problem of group technology--the reuse of engineering designs. Two- and three-dimensional representations of engineering designs are input to ART-1 neural networks to produce groups or families of similar parts. These representations, in their basic form, amount to bit maps of the part, and can become very large when the part is represented in high resolution. This paper describes an enhancement to an algorithmic form of ART-1 that allows it to operate directly on compressed input representations and to generate compressed memory templates. The performance of this compressed algorithm is compared to that of the regular algorithm on real engineering designs and a significant savings in memory storage as well as a speed up in execution is observed. In additions, a `neural database'' system under development is described. This system demonstrates the feasibility of training an ART-1 network to first cluster designs into families, and then to recall the family when presented a similar design. This application is of large practical value to industry, making it possible to avoid duplication of design efforts.

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling.

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Junha Shin

Full Text Available Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life-Archaea, Bacteria, and Eukaryota-suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co

Bayesian latent feature modeling for modeling bipartite networks with overlapping groups

DEFF Research Database (Denmark)

Jørgensen, Philip H.; Mørup, Morten; Schmidt, Mikkel Nørgaard

2016-01-01

Bi-partite networks are commonly modelled using latent class or latent feature models. Whereas the existing latent class models admit marginalization of parameters specifying the strength of interaction between groups, existing latent feature models do not admit analytical marginalization...... by the notion of community structure such that the edge density within groups is higher than between groups. Our model further assumes that entities can have different propensities of generating links in one of the modes. The proposed framework is contrasted on both synthetic and real bi-partite networks...... feature representations in bipartite networks provides a new framework for accounting for structure in bi-partite networks using binary latent feature representations providing interpretable representations that well characterize structure as quantified by link prediction....

Prototyping and Simulation of Robot Group Intelligence using Kohonen Networks.

Science.gov (United States)

Wang, Zhijun; Mirdamadi, Reza; Wang, Qing

2016-01-01

Intelligent agents such as robots can form ad hoc networks and replace human being in many dangerous scenarios such as a complicated disaster relief site. This project prototypes and builds a computer simulator to simulate robot kinetics, unsupervised learning using Kohonen networks, as well as group intelligence when an ad hoc network is formed. Each robot is modeled using an object with a simple set of attributes and methods that define its internal states and possible actions it may take under certain circumstances. As the result, simple, reliable, and affordable robots can be deployed to form the network. The simulator simulates a group of robots as an unsupervised learning unit and tests the learning results under scenarios with different complexities. The simulation results show that a group of robots could demonstrate highly collaborative behavior on a complex terrain. This study could potentially provide a software simulation platform for testing individual and group capability of robots before the design process and manufacturing of robots. Therefore, results of the project have the potential to reduce the cost and improve the efficiency of robot design and building.

On the chromospheric network structure around deVeloped groups of sunspots

International Nuclear Information System (INIS)

Kartashova, L.G.

1980-01-01

The chromospheric network structure around several developed groups of sunspots were studied on the basis of the observations in the Hsub(α) line. The resolution on the filtergrams was of 2. The following was found: 1) in the neighbourhood of the groups of sunspots 70% (from 870) of network cells stretch along fibrils direction (with accuracy 30 deg), and 15% of cells stretch approximately across that (at angles 70-90 deg); 2) out of the boundary of the main radial fibrils structure the groups of sunspots is often rounded by the system of network cells stretched approximately perpendicular to radial direction

Primary health care teams and the patient perspective: a social network analysis.

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Cheong, Lynn H M; Armour, Carol L; Bosnic-Anticevich, Sinthia Z

2013-01-01

Multidisciplinary care (MDC) has been proposed as a potential strategy to address the rising challenges of modern health issues. However, it remains unclear as to how patients' health connections may impact on multidisciplinary processes and outcomes. This research aims to gain a deeper understanding of patients' potential role in MDC: i) describe patients' health networks, ii) compare different care groups, iii) gain an understanding of the nature and extent of their interactions, and iv) identify the role of pharmacists within patient networks. In-depth, semi-structured interviews were conducted with asthma patients from Sydney, Australia. Participants were recruited from a range of standard asthma health care access points (community group) and a specialized multidisciplinary asthma clinic (clinic group). Quantitative social network analysis provided structural insight into asthma networks while qualitative social network analysis assisted in interpretation of network data. A total of 47 interviews were conducted (26 community group participants and 21 clinic group participants). Although participants' asthma networks consisted of a range of health care professionals (HCPs), these did not reflect or encourage MDC. Not only did participants favor minimal interaction with any HCP, they preferred sole-charge care and were found to strongly rely on lay individuals such as family and friends. While general practitioners and respiratory specialists were participants' principal choice of HCP, community pharmacists were less regarded. Limited opportunities were presented for HCPs to collaborate, particularly pharmacists. As patients' choices of HCPs may strongly influence collaborative processes and outcomes, this research highlights the need to consider patient perspectives in the development of MDC models in primary care. Copyright © 2013 Elsevier Inc. All rights reserved.

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.

Network analysis of perception-action coupling in infants

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Naama eRotem-Kohavi

2014-04-01

Full Text Available The functional networks that support action observation are of great interest in understanding the development of social cognition and motor learning. How infants learn to represent and understand the world around them remains one of the most intriguing questions in developmental cognitive neuroscience. Recently, mathematical measures derived from graph theory have been used to study connectivity networks in the developing brain. Thus far, this type of analysis in infancy has only been applied to the resting state. In this study, we recorded electroencephalography (EEG from infants (ages 4-11 months of age and adults while they observed three types of actions: a reaching for an object, b walking and c object motion. Graph theory based analysis was applied to these data to evaluate changes in brain networks. Global metrics that provide measures of the structural properties of the network (characteristic path, density, global efficiency, and modularity were calculated for each group and for each condition. We found statistically significant differences in measures for the observation of walking condition only. Specifically, in comparison to adults, infants showed increased density and global efficiency in combination with decreased modularity during observation of an action that is not within their motor repertoire (i.e. independent walking, suggesting a less structured organization. There were no group differences in global metric measures for observation of object motion or for observation of actions that are within the repertoire of infants (i.e. reaching. These preliminary results suggest that infants and adults may share a basic functional network for action observation that is sculpted by experience. Motor experience may lead to a shift towards a more efficient functional network.

Characteristics of group networks in the KOSPI and the KOSDAQ

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Kim, Kyungsik; Ko, Jeung-Su; Yi, Myunggi

2012-02-01

We investigate the main feature of group networks in the KOSPI and KOSDAQ of Korean financial markets and analyze daily cross-correlations between price fluctuations for the 5-year time period from 2006 to 2010. We discuss the stabilities by undressing the market-wide effect using the Markowitz multi-factor model and the network-based approach. In particular we ascertain the explicit list of significant firms in the few largest eigenvectors from the undressed correlation matrix. Finally, we show the structure of group correlation by applying a network-based approach. In addition, the relation between market capitalizations and businesses is examined.

Friendship Group Composition and Juvenile Institutional Misconduct.

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Reid, Shannon E

2017-02-01

The present study examines both the patterns of friendship networks and how these network characteristics relate to the risk factors of institutional misconduct for incarcerated youth. Using friendship networks collected from males incarcerated with California's Division of Juvenile Justice (DJJ), latent profile analysis was utilized to create homogeneous groups of friendship patterns based on alter attributes and network structure. The incarcerated youth provided 144 egocentric networks reporting 558 social network relationships. Latent profile analysis identified three network profiles: expected group (67%), new breed group (20%), and model citizen group (13%). The three network profiles were integrated into a multiple group analysis framework to examine the relative influence of individual-level risk factors on their rate of institutional misconduct. The analysis finds variation in predictors of institutional misconduct across profile types. These findings suggest that the close friendships of incarcerated youth are patterned across the individual characteristics of the youth's friends and that the friendship network can act as a moderator for individual risk factors for institutional misconduct.

Social Network Analysis Reveals the Negative Effects of Attention-Deficit/Hyperactivity Disorder (ADHD Symptoms on Friend-Based Student Networks.

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Jun Won Kim

Full Text Available Social network analysis has emerged as a promising tool in modern social psychology. This method can be used to examine friend-based social relationships in terms of network theory, with nodes representing individual students and ties representing relationships between students (e.g., friendships and kinships. Using social network analysis, we investigated whether greater severity of ADHD symptoms is correlated with weaker peer relationships among elementary school students.A total of 562 sixth-graders from two elementary schools (300 males provided the names of their best friends (maximum 10 names. Their teachers rated each student's ADHD symptoms using an ADHD rating scale.The results showed that 10.2% of the students were at high risk for ADHD. Significant group differences were observed between the high-risk students and other students in two of the three network parameters (degree, centrality and closeness used to assess friendship quality, with the high-risk group showing significantly lower values of degree and closeness compared to the other students. Moreover, negative correlations were found between the ADHD rating and two social network analysis parameters.Our findings suggest that the severity of ADHD symptoms is strongly correlated with the quality of social and interpersonal relationships in students with ADHD symptoms.

Social Network Analysis Reveals the Negative Effects of Attention-Deficit/Hyperactivity Disorder (ADHD) Symptoms on Friend-Based Student Networks.

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Kim, Jun Won; Kim, Bung-Nyun; Kim, Johanna Inhyang; Lee, Young Sik; Min, Kyung Joon; Kim, Hyun-Jin; Lee, Jaewon

2015-01-01

Social network analysis has emerged as a promising tool in modern social psychology. This method can be used to examine friend-based social relationships in terms of network theory, with nodes representing individual students and ties representing relationships between students (e.g., friendships and kinships). Using social network analysis, we investigated whether greater severity of ADHD symptoms is correlated with weaker peer relationships among elementary school students. A total of 562 sixth-graders from two elementary schools (300 males) provided the names of their best friends (maximum 10 names). Their teachers rated each student's ADHD symptoms using an ADHD rating scale. The results showed that 10.2% of the students were at high risk for ADHD. Significant group differences were observed between the high-risk students and other students in two of the three network parameters (degree, centrality and closeness) used to assess friendship quality, with the high-risk group showing significantly lower values of degree and closeness compared to the other students. Moreover, negative correlations were found between the ADHD rating and two social network analysis parameters. Our findings suggest that the severity of ADHD symptoms is strongly correlated with the quality of social and interpersonal relationships in students with ADHD symptoms.

Support surfaces for pressure ulcer prevention: A network meta-analysis

Science.gov (United States)

Dumville, Jo C.; Cullum, Nicky

2018-01-01

Background Pressure ulcers are a prevalent and global issue and support surfaces are widely used for preventing ulceration. However, the diversity of available support surfaces and the lack of direct comparisons in RCTs make decision-making difficult. Objectives To determine, using network meta-analysis, the relative effects of different support surfaces in reducing pressure ulcer incidence and comfort and to rank these support surfaces in order of their effectiveness. Methods We conducted a systematic review, using a literature search up to November 2016, to identify randomised trials comparing support surfaces for pressure ulcer prevention. Two reviewers independently performed study selection, risk of bias assessment and data extraction. We grouped the support surfaces according to their characteristics and formed evidence networks using these groups. We used network meta-analysis to estimate the relative effects and effectiveness ranking of the groups for the outcomes of pressure ulcer incidence and participant comfort. GRADE was used to assess the certainty of evidence. Main results We included 65 studies in the review. The network for assessing pressure ulcer incidence comprised evidence of low or very low certainty for most network contrasts. There was moderate-certainty evidence that powered active air surfaces and powered hybrid air surfaces probably reduce pressure ulcer incidence compared with standard hospital surfaces (risk ratios (RR) 0.42, 95% confidence intervals (CI) 0.29 to 0.63; 0.22, 0.07 to 0.66, respectively). The network for comfort suggested that powered active air-surfaces are probably slightly less comfortable than standard hospital mattresses (RR 0.80, 95% CI 0.69 to 0.94; moderate-certainty evidence). Conclusions This is the first network meta-analysis of the effects of support surfaces for pressure ulcer prevention. Powered active air-surfaces probably reduce pressure ulcer incidence, but are probably less comfortable than standard

Support surfaces for pressure ulcer prevention: A network meta-analysis.

Science.gov (United States)

Shi, Chunhu; Dumville, Jo C; Cullum, Nicky

2018-01-01

Pressure ulcers are a prevalent and global issue and support surfaces are widely used for preventing ulceration. However, the diversity of available support surfaces and the lack of direct comparisons in RCTs make decision-making difficult. To determine, using network meta-analysis, the relative effects of different support surfaces in reducing pressure ulcer incidence and comfort and to rank these support surfaces in order of their effectiveness. We conducted a systematic review, using a literature search up to November 2016, to identify randomised trials comparing support surfaces for pressure ulcer prevention. Two reviewers independently performed study selection, risk of bias assessment and data extraction. We grouped the support surfaces according to their characteristics and formed evidence networks using these groups. We used network meta-analysis to estimate the relative effects and effectiveness ranking of the groups for the outcomes of pressure ulcer incidence and participant comfort. GRADE was used to assess the certainty of evidence. We included 65 studies in the review. The network for assessing pressure ulcer incidence comprised evidence of low or very low certainty for most network contrasts. There was moderate-certainty evidence that powered active air surfaces and powered hybrid air surfaces probably reduce pressure ulcer incidence compared with standard hospital surfaces (risk ratios (RR) 0.42, 95% confidence intervals (CI) 0.29 to 0.63; 0.22, 0.07 to 0.66, respectively). The network for comfort suggested that powered active air-surfaces are probably slightly less comfortable than standard hospital mattresses (RR 0.80, 95% CI 0.69 to 0.94; moderate-certainty evidence). This is the first network meta-analysis of the effects of support surfaces for pressure ulcer prevention. Powered active air-surfaces probably reduce pressure ulcer incidence, but are probably less comfortable than standard hospital surfaces. Most prevention evidence was of low or

Support surfaces for pressure ulcer prevention: A network meta-analysis.

Directory of Open Access Journals (Sweden)

Chunhu Shi

Full Text Available Pressure ulcers are a prevalent and global issue and support surfaces are widely used for preventing ulceration. However, the diversity of available support surfaces and the lack of direct comparisons in RCTs make decision-making difficult.To determine, using network meta-analysis, the relative effects of different support surfaces in reducing pressure ulcer incidence and comfort and to rank these support surfaces in order of their effectiveness.We conducted a systematic review, using a literature search up to November 2016, to identify randomised trials comparing support surfaces for pressure ulcer prevention. Two reviewers independently performed study selection, risk of bias assessment and data extraction. We grouped the support surfaces according to their characteristics and formed evidence networks using these groups. We used network meta-analysis to estimate the relative effects and effectiveness ranking of the groups for the outcomes of pressure ulcer incidence and participant comfort. GRADE was used to assess the certainty of evidence.We included 65 studies in the review. The network for assessing pressure ulcer incidence comprised evidence of low or very low certainty for most network contrasts. There was moderate-certainty evidence that powered active air surfaces and powered hybrid air surfaces probably reduce pressure ulcer incidence compared with standard hospital surfaces (risk ratios (RR 0.42, 95% confidence intervals (CI 0.29 to 0.63; 0.22, 0.07 to 0.66, respectively. The network for comfort suggested that powered active air-surfaces are probably slightly less comfortable than standard hospital mattresses (RR 0.80, 95% CI 0.69 to 0.94; moderate-certainty evidence.This is the first network meta-analysis of the effects of support surfaces for pressure ulcer prevention. Powered active air-surfaces probably reduce pressure ulcer incidence, but are probably less comfortable than standard hospital surfaces. Most prevention evidence was

African Network Operators Group (AfNOG) Training Workshops and ...

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

The African Network Operators Group (AfNOG) is a forum for technical cooperation and coordination between African network operators and engineers from the region's universities, research institutions and industry. This year, AfNOG's training workshops and meetings will be held in Rabat, Morocco, between 24 May and 6 ...

Multifractal analysis of complex networks

International Nuclear Information System (INIS)

Wang Dan-Ling; Yu Zu-Guo; Anh V

2012-01-01

Complex networks have recently attracted much attention in diverse areas of science and technology. Many networks such as the WWW and biological networks are known to display spatial heterogeneity which can be characterized by their fractal dimensions. Multifractal analysis is a useful way to systematically describe the spatial heterogeneity of both theoretical and experimental fractal patterns. In this paper, we introduce a new box-covering algorithm for multifractal analysis of complex networks. This algorithm is used to calculate the generalized fractal dimensions D q of some theoretical networks, namely scale-free networks, small world networks, and random networks, and one kind of real network, namely protein—protein interaction networks of different species. Our numerical results indicate the existence of multifractality in scale-free networks and protein—protein interaction networks, while the multifractal behavior is not clear-cut for small world networks and random networks. The possible variation of D q due to changes in the parameters of the theoretical network models is also discussed. (general)

Ecological network analysis: network construction

NARCIS (Netherlands)

Fath, B.D.; Scharler, U.M.; Ulanowicz, R.E.; Hannon, B.

2007-01-01

Ecological network analysis (ENA) is a systems-oriented methodology to analyze within system interactions used to identify holistic properties that are otherwise not evident from the direct observations. Like any analysis technique, the accuracy of the results is as good as the data available, but

Pattern classification and recognition of invertebrate functional groups using self-organizing neural networks.

Science.gov (United States)

Zhang, WenJun

2007-07-01

Self-organizing neural networks can be used to mimic non-linear systems. The main objective of this study is to make pattern classification and recognition on sampling information using two self-organizing neural network models. Invertebrate functional groups sampled in the irrigated rice field were classified and recognized using one-dimensional self-organizing map and self-organizing competitive learning neural networks. Comparisons between neural network models, distance (similarity) measures, and number of neurons were conducted. The results showed that self-organizing map and self-organizing competitive learning neural network models were effective in pattern classification and recognition of sampling information. Overall the performance of one-dimensional self-organizing map neural network was better than self-organizing competitive learning neural network. The number of neurons could determine the number of classes in the classification. Different neural network models with various distance (similarity) measures yielded similar classifications. Some differences, dependent upon the specific network structure, would be found. The pattern of an unrecognized functional group was recognized with the self-organizing neural network. A relative consistent classification indicated that the following invertebrate functional groups, terrestrial blood sucker; terrestrial flyer; tourist (nonpredatory species with no known functional role other than as prey in ecosystem); gall former; collector (gather, deposit feeder); predator and parasitoid; leaf miner; idiobiont (acarine ectoparasitoid), were classified into the same group, and the following invertebrate functional groups, external plant feeder; terrestrial crawler, walker, jumper or hunter; neustonic (water surface) swimmer (semi-aquatic), were classified into another group. It was concluded that reliable conclusions could be drawn from comparisons of different neural network models that use different distance

Brain Structure Network Analysis in Patients with Obstructive Sleep Apnea.

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Luo, Yun-Gang; Wang, Defeng; Liu, Kai; Weng, Jian; Guan, Yuefeng; Chan, Kate C C; Chu, Winnie C W; Shi, Lin

2015-01-01

Childhood obstructive sleep apnea (OSA) is a sleeping disorder commonly affecting school-aged children and is characterized by repeated episodes of blockage of the upper airway during sleep. In this study, we performed a graph theoretical analysis on the brain morphometric correlation network in 25 OSA patients (OSA group; 5 female; mean age, 10.1 ± 1.8 years) and investigated the topological alterations in global and regional properties compared with 20 healthy control individuals (CON group; 6 females; mean age, 10.4 ± 1.8 years). A structural correlation network based on regional gray matter volume was constructed respectively for each group. Our results revealed a significantly decreased mean local efficiency in the OSA group over the density range of 0.32-0.44 (p gyrus, and a tendency of decreased degree in the right lingual and inferior frontal (orbital part) gyrus (p < 0.005, uncorrected). We also found that the network hubs in OSA and controls were distributed differently. To the best of our knowledge, this is the first study that characterizes the brain structure network in OSA patients and invests the alteration of topological properties of gray matter volume structural network. This study may help to provide new evidence for understanding the neuropathophysiology of OSA from a topological perspective.

Investigation and analysis of network psychology of college students

Institute of Scientific and Technical Information of China (English)

Zhang Xiaoyan

2013-01-01

Based on basic situational research and analysis carried out on 638 college students using network,we found that as many as 20 percent of the students are not only largely dependent on internet,but also addicted to it.Further biography characteristics analyses for different individuals on the four dimensions of the network forced addiction,tolerance,and time management and interpersonal relationship and health,show that there are significant differences in grades,gender with different education levels of their parents.Further researches on discrepancy that addicted groups have in network entertainment addiction,network information,cyber porn,network relations and network transactions addictions also illustrate that significant discrepancies exist in gender,net age,different discipline and other factors.Finally we put forward some correlative measures to solve the problems of college students network psychology from individuals,schools,and society levels.

Deterministic bound for avionics switched networks according to networking features using network calculus

Directory of Open Access Journals (Sweden)

Feng HE

2017-12-01

Full Text Available The state of the art avionics system adopts switched networks for airborne communications. A major concern in the design of the networks is the end-to-end guarantee ability. Analytic methods have been developed to compute the worst-case delays according to the detailed configurations of flows and networks within avionics context, such as network calculus and trajectory approach. It still lacks a relevant method to make a rapid performance estimation according to some typically switched networking features, such as networking scale, bandwidth utilization and average flow rate. The goal of this paper is to establish a deterministic upper bound analysis method by using these networking features instead of the complete network configurations. Two deterministic upper bounds are proposed from network calculus perspective: one is for a basic estimation, and another just shows the benefits from grouping strategy. Besides, a mathematic expression for grouping ability is established based on the concept of network connecting degree, which illustrates the possibly minimal grouping benefit. For a fully connected network with 4 switches and 12 end systems, the grouping ability coming from grouping strategy is 15–20%, which just coincides with the statistical data (18–22% from the actual grouping advantage. Compared with the complete network calculus analysis method for individual flows, the effectiveness of the two deterministic upper bounds is no less than 38% even with remarkably varied packet lengths. Finally, the paper illustrates the design process for an industrial Avionics Full DupleX switched Ethernet (AFDX networking case according to the two deterministic upper bounds and shows that a better control for network connecting, when designing a switched network, can improve the worst-case delays dramatically. Keywords: Deterministic bound, Grouping ability, Network calculus, Networking features, Switched networks

Information flow analysis of interactome networks.

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Patrycja Vasilyev Missiuro

2009-04-01

Full Text Available Recent studies of cellular networks have revealed modular organizations of genes and proteins. For example, in interactome networks, a module refers to a group of interacting proteins that form molecular complexes and/or biochemical pathways and together mediate a biological process. However, it is still poorly understood how biological information is transmitted between different modules. We have developed information flow analysis, a new computational approach that identifies proteins central to the transmission of biological information throughout the network. In the information flow analysis, we represent an interactome network as an electrical circuit, where interactions are modeled as resistors and proteins as interconnecting junctions. Construing the propagation of biological signals as flow of electrical current, our method calculates an information flow score for every protein. Unlike previous metrics of network centrality such as degree or betweenness that only consider topological features, our approach incorporates confidence scores of protein-protein interactions and automatically considers all possible paths in a network when evaluating the importance of each protein. We apply our method to the interactome networks of Saccharomyces cerevisiae and Caenorhabditis elegans. We find that the likelihood of observing lethality and pleiotropy when a protein is eliminated is positively correlated with the protein's information flow score. Even among proteins of low degree or low betweenness, high information scores serve as a strong predictor of loss-of-function lethality or pleiotropy. The correlation between information flow scores and phenotypes supports our hypothesis that the proteins of high information flow reside in central positions in interactome networks. We also show that the ranks of information flow scores are more consistent than that of betweenness when a large amount of noisy data is added to an interactome. Finally, we

Internet Group Management Protocol for IPTV Services in Passive Optical Network

Science.gov (United States)

Lee, Eunjo; Park, Sungkwon

We propose a new Internet group management protocol (IGMP) which can be used in passive optical network (PON) especially for IPTV services which dramatically reduces the channel change response time caused by traditional IGMP. In this paper, the newly proposed IGMP is introduced in detail and performance analysis is also included. Simulation results demonstrated the performance of the newly proposed IGMP, whereby, viewers can watch the shared IPTV channels without the channel change response time when channel request reaches a threshold.

Using structural equation modeling for network meta-analysis.

Science.gov (United States)

Tu, Yu-Kang; Wu, Yun-Chun

2017-07-14

Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison

Leadership of healthcare commissioning networks in England: a mixed-methods study on clinical commissioning groups

Science.gov (United States)

Zachariadis, Markos; Oborn, Eivor; Barrett, Michael; Zollinger-Read, Paul

2013-01-01

Objective To explore the relational challenges for general practitioner (GP) leaders setting up new network-centric commissioning organisations in the recent health policy reform in England, we use innovation network theory to identify key network leadership practices that facilitate healthcare innovation. Design Mixed-method, multisite and case study research. Setting Six clinical commissioning groups and local clusters in the East of England area, covering in total 208 GPs and 1 662 000 population. Methods Semistructured interviews with 56 lead GPs, practice managers and staff from the local health authorities (primary care trusts, PCT) as well as various healthcare professionals; 21 observations of clinical commissioning group (CCG) board and executive meetings; electronic survey of 58 CCG board members (these included GPs, practice managers, PCT employees, nurses and patient representatives) and subsequent social network analysis. Main outcome measures Collaborative relationships between CCG board members and stakeholders from their healthcare network; clarifying the role of GPs as network leaders; strengths and areas for development of CCGs. Results Drawing upon innovation network theory provides unique insights of the CCG leaders’ activities in establishing best practices and introducing new clinical pathways. In this context we identified three network leadership roles: managing knowledge flows, managing network coherence and managing network stability. Knowledge sharing and effective collaboration among GPs enable network stability and the alignment of CCG objectives with those of the wider health system (network coherence). Even though activities varied between commissioning groups, collaborative initiatives were common. However, there was significant variation among CCGs around the level of engagement with providers, patients and local authorities. Locality (sub) groups played an important role because they linked commissioning decisions with

Consensus group sessions are useful to reconcile stakeholders’ perspectives about network performance evaluation

Directory of Open Access Journals (Sweden)

Marie-Eve Lamontagne

2010-12-01

Full Text Available Background: Having a common vision among network stakeholders is an important ingredient to developing a performance evaluation process. Consensus methods may be a viable means to reconcile the perceptions of different stakeholders about the dimensions to include in a performance evaluation framework.Objectives: To determine whether individual organizations within traumatic brain injury (TBI networks differ in perceptions about the importance of performance dimensions for the evaluation of TBI networks and to explore the extent to which group consensus sessions could reconcile these perceptions.Methods: We used TRIAGE, a consensus technique that combines an individual and a group data collection phase to explore the perceptions of network stakeholders and to reach a consensus within structured group discussions.Results: One hundred and thirty-nine professionals from 43 organizations within eight TBI networks participated in the individual data collection; 62 professionals from these same organisations contributed to the group data collection. The extent of consensus based on questionnaire results (e.g. individual data collection was low, however, 100% agreement was obtained for each network during the consensus group sessions. The median importance scores and mean ranks attributed to the dimensions by individuals compared to groups did not differ greatly. Group discussions were found useful in understanding the reasons motivating the scoring, for resolving differences among participants, and for harmonizing their values.Conclusion: Group discussions, as part of a consensus technique, appear to be a useful process to reconcile diverging perceptions of network performance among stakeholders.

Hyperconnectivity in juvenile myoclonic epilepsy: a network analysis.

Science.gov (United States)

Caeyenberghs, K; Powell, H W R; Thomas, R H; Brindley, L; Church, C; Evans, J; Muthukumaraswamy, S D; Jones, D K; Hamandi, K

2015-01-01

Juvenile myoclonic epilepsy (JME) is a common idiopathic (genetic) generalized epilepsy (IGE) syndrome characterized by impairments in executive and cognitive control, affecting independent living and psychosocial functioning. There is a growing consensus that JME is associated with abnormal function of diffuse brain networks, typically affecting frontal and fronto-thalamic areas. Using diffusion MRI and a graph theoretical analysis, we examined bivariate (network-based statistic) and multivariate (global and local) properties of structural brain networks in patients with JME (N = 34) and matched controls. Neuropsychological assessment was performed in a subgroup of 14 patients. Neuropsychometry revealed impaired visual memory and naming in JME patients despite a normal full scale IQ (mean = 98.6). Both JME patients and controls exhibited a small world topology in their white matter networks, with no significant differences in the global multivariate network properties between the groups. The network-based statistic approach identified one subnetwork of hyperconnectivity in the JME group, involving primary motor, parietal and subcortical regions. Finally, there was a significant positive correlation in structural connectivity with cognitive task performance. Our findings suggest that structural changes in JME patients are distributed at a network level, beyond the frontal lobes. The identified subnetwork includes key structures in spike wave generation, along with primary motor areas, which may contribute to myoclonic jerks. We conclude that analyzing the affected subnetworks may provide new insights into understanding seizure generation, as well as the cognitive deficits observed in JME patients.

A working group for Japanese nuclear data measurement network

International Nuclear Information System (INIS)

Watanabe, Yukinobu

2013-01-01

A new working group in the Japanese Nuclear Data Committee has been established to make a cooperative network among researchers involved in nuclear data measurements and to discuss the strategy for nuclear data measurements. The working group activities are reported. (author)

Large-Scale Network Analysis of Whole-Brain Resting-State Functional Connectivity in Spinal Cord Injury: A Comparative Study.

Science.gov (United States)

Kaushal, Mayank; Oni-Orisan, Akinwunmi; Chen, Gang; Li, Wenjun; Leschke, Jack; Ward, Doug; Kalinosky, Benjamin; Budde, Matthew; Schmit, Brian; Li, Shi-Jiang; Muqeet, Vaishnavi; Kurpad, Shekar

2017-09-01

Network analysis based on graph theory depicts the brain as a complex network that allows inspection of overall brain connectivity pattern and calculation of quantifiable network metrics. To date, large-scale network analysis has not been applied to resting-state functional networks in complete spinal cord injury (SCI) patients. To characterize modular reorganization of whole brain into constituent nodes and compare network metrics between SCI and control subjects, fifteen subjects with chronic complete cervical SCI and 15 neurologically intact controls were scanned. The data were preprocessed followed by parcellation of the brain into 116 regions of interest (ROI). Correlation analysis was performed between every ROI pair to construct connectivity matrices and ROIs were categorized into distinct modules. Subsequently, local efficiency (LE) and global efficiency (GE) network metrics were calculated at incremental cost thresholds. The application of a modularity algorithm organized the whole-brain resting-state functional network of the SCI and the control subjects into nine and seven modules, respectively. The individual modules differed across groups in terms of the number and the composition of constituent nodes. LE demonstrated statistically significant decrease at multiple cost levels in SCI subjects. GE did not differ significantly between the two groups. The demonstration of modular architecture in both groups highlights the applicability of large-scale network analysis in studying complex brain networks. Comparing modules across groups revealed differences in number and membership of constituent nodes, indicating modular reorganization due to neural plasticity.

Network clustering coefficient approach to DNA sequence analysis

Energy Technology Data Exchange (ETDEWEB)

Gerhardt, Guenther J.L. [Universidade Federal do Rio Grande do Sul-Hospital de Clinicas de Porto Alegre, Rua Ramiro Barcelos 2350/sala 2040/90035-003 Porto Alegre (Brazil); Departamento de Fisica e Quimica da Universidade de Caxias do Sul, Rua Francisco Getulio Vargas 1130, 95001-970 Caxias do Sul (Brazil); Lemke, Ney [Programa Interdisciplinar em Computacao Aplicada, Unisinos, Av. Unisinos, 950, 93022-000 Sao Leopoldo, RS (Brazil); Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, Campus Universitario, 59072 970 Natal, RN (Brazil)]. E-mail: corso@dfte.ufrn.br

2006-05-15

In this work we propose an alternative DNA sequence analysis tool based on graph theoretical concepts. The methodology investigates the path topology of an organism genome through a triplet network. In this network, triplets in DNA sequence are vertices and two vertices are connected if they occur juxtaposed on the genome. We characterize this network topology by measuring the clustering coefficient. We test our methodology against two main bias: the guanine-cytosine (GC) content and 3-bp (base pairs) periodicity of DNA sequence. We perform the test constructing random networks with variable GC content and imposed 3-bp periodicity. A test group of some organisms is constructed and we investigate the methodology in the light of the constructed random networks. We conclude that the clustering coefficient is a valuable tool since it gives information that is not trivially contained in 3-bp periodicity neither in the variable GC content.

Community detection in networks with unequal groups.

Science.gov (United States)

Zhang, Pan; Moore, Cristopher; Newman, M E J

2016-01-01

Recently, a phase transition has been discovered in the network community detection problem below which no algorithm can tell which nodes belong to which communities with success any better than a random guess. This result has, however, so far been limited to the case where the communities have the same size or the same average degree. Here we consider the case where the sizes or average degrees differ. This asymmetry allows us to assign nodes to communities with better-than-random success by examining their local neighborhoods. Using the cavity method, we show that this removes the detectability transition completely for networks with four groups or fewer, while for more than four groups the transition persists up to a critical amount of asymmetry but not beyond. The critical point in the latter case coincides with the point at which local information percolates, causing a global transition from a less-accurate solution to a more-accurate one.

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

Identification of Conserved Moieties in Metabolic Networks by Graph Theoretical Analysis of Atom Transition Networks

Science.gov (United States)

Haraldsdóttir, Hulda S.; Fleming, Ronan M. T.

2016-01-01

Conserved moieties are groups of atoms that remain intact in all reactions of a metabolic network. Identification of conserved moieties gives insight into the structure and function of metabolic networks and facilitates metabolic modelling. All moiety conservation relations can be represented as nonnegative integer vectors in the left null space of the stoichiometric matrix corresponding to a biochemical network. Algorithms exist to compute such vectors based only on reaction stoichiometry but their computational complexity has limited their application to relatively small metabolic networks. Moreover, the vectors returned by existing algorithms do not, in general, represent conservation of a specific moiety with a defined atomic structure. Here, we show that identification of conserved moieties requires data on reaction atom mappings in addition to stoichiometry. We present a novel method to identify conserved moieties in metabolic networks by graph theoretical analysis of their underlying atom transition networks. Our method returns the exact group of atoms belonging to each conserved moiety as well as the corresponding vector in the left null space of the stoichiometric matrix. It can be implemented as a pipeline of polynomial time algorithms. Our implementation completes in under five minutes on a metabolic network with more than 4,000 mass balanced reactions. The scalability of the method enables extension of existing applications for moiety conservation relations to genome-scale metabolic networks. We also give examples of new applications made possible by elucidating the atomic structure of conserved moieties. PMID:27870845

Footwear Supply Network Management for Specific Target Groups

OpenAIRE

Franchini, Valentina

2013-01-01

This research is a part of CoReNet (Customer-ORiented and Eco-friendly NETworks for healthy fashionable goods), an European 7th Framework Program project, whose objective is to implement innovative methods and tools to fulfil needs and expectations of specific target groups – elderly, obese, disabled and diabetic people – by improving the supply network structure of the European Textile, Clothing and Footwear Industry (TCFI) to produce small series of functional and fashionable clothes and fo...

Quantifying Individual Brain Connectivity with Functional Principal Component Analysis for Networks.

Science.gov (United States)

Petersen, Alexander; Zhao, Jianyang; Carmichael, Owen; Müller, Hans-Georg

2016-09-01

In typical functional connectivity studies, connections between voxels or regions in the brain are represented as edges in a network. Networks for different subjects are constructed at a given graph density and are summarized by some network measure such as path length. Examining these summary measures for many density values yields samples of connectivity curves, one for each individual. This has led to the adoption of basic tools of functional data analysis, most commonly to compare control and disease groups through the average curves in each group. Such group differences, however, neglect the variability in the sample of connectivity curves. In this article, the use of functional principal component analysis (FPCA) is demonstrated to enrich functional connectivity studies by providing increased power and flexibility for statistical inference. Specifically, individual connectivity curves are related to individual characteristics such as age and measures of cognitive function, thus providing a tool to relate brain connectivity with these variables at the individual level. This individual level analysis opens a new perspective that goes beyond previous group level comparisons. Using a large data set of resting-state functional magnetic resonance imaging scans, relationships between connectivity and two measures of cognitive function-episodic memory and executive function-were investigated. The group-based approach was implemented by dichotomizing the continuous cognitive variable and testing for group differences, resulting in no statistically significant findings. To demonstrate the new approach, FPCA was implemented, followed by linear regression models with cognitive scores as responses, identifying significant associations of connectivity in the right middle temporal region with both cognitive scores.

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

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).

A systematic review protocol: social network analysis of tobacco use.

Science.gov (United States)

Maddox, Raglan; Davey, Rachel; Lovett, Ray; van der Sterren, Anke; Corbett, Joan; Cochrane, Tom

2014-08-08

Tobacco use is the single most preventable cause of death in the world. Evidence indicates that behaviours such as tobacco use can influence social networks, and that social network structures can influence behaviours. Social network analysis provides a set of analytic tools to undertake methodical analysis of social networks. We will undertake a systematic review to provide a comprehensive synthesis of the literature regarding social network analysis and tobacco use. The review will answer the following research questions: among participants who use tobacco, does social network structure/position influence tobacco use? Does tobacco use influence peer selection? Does peer selection influence tobacco use? We will follow the Preferred Reporting Items for Systemic Reviews and Meta-Analyses (PRISMA) guidelines and search the following databases for relevant articles: CINAHL (Cumulative Index to Nursing and Allied Health Literature); Informit Health Collection; PsycINFO; PubMed/MEDLINE; Scopus/Embase; Web of Science; and the Wiley Online Library. Keywords include tobacco; smoking; smokeless; cigarettes; cigar and 'social network' and reference lists of included articles will be hand searched. Studies will be included that provide descriptions of social network analysis of tobacco use.Qualitative, quantitative and mixed method data that meets the inclusion criteria for the review, including methodological rigour, credibility and quality standards, will be synthesized using narrative synthesis. Results will be presented using outcome statistics that address each of the research questions. This systematic review will provide a timely evidence base on the role of social network analysis of tobacco use, forming a basis for future research, policy and practice in this area. This systematic review will synthesise the evidence, supporting the hypothesis that social network structures can influence tobacco use. This will also include exploring the relationship between social

Fibrin network pattern changes of platelet-rich fibrin in young versus old age group of individuals: A cell block cytology study

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Shravanthi Raghav Yajamanya

2016-01-01

Full Text Available Background: To evaluate variations in fibrin network patterns of the platelet-rich fibrin (PRF in different age groups. Materials and Methods: Ninety-five patients were divided into three age groups: Group 1: (20–39 years; Group 2: (40–59 years; and Group 3: (60 years and above. PRF was prepared from blood samples of all patients and were subjected to cell block cytology method of histological analysis and slides were prepared to histologically assess the age-related changes in (i fibrin network patterns in terms of density and (ii entrapment of platelets and white blood cells (WBCs within fibrin meshwork. Results: Two types of fibrin network pattern arrangements noticed: Dense and loose types in three age groups. However, there was a noticeable decrease in the dense type of fibrin network with progressing age and increase in the loose type of fibrin arrangement. Furthermore, variation in a number of platelets and WBCs entrapped within fibrin network in relation to age was noticed. Conclusion: From the current study it can be concluded that age can be considered as one of the influencing factors on quality of PRF in terms of fibrin network patterns and hence, platelet and WBCs entrapment within these fibrin networks.

A Social Network Analysis of Occupational Segregation

DEFF Research Database (Denmark)

Buhai, Ioan Sebastian; van der Leij, Marco

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 for job search, then expected homophily in the contact network structure...

The Emergence of Embedded Relations and Group Formation in Networks of Competition

Science.gov (United States)

Thye, Shane R.; Lawler, Edward J.; Yoon, Jeongkoo

2011-01-01

This study examines how and when small networks of self-interested agents generate a group tie or affiliation at the network level. A group affiliation is formed when actors (a) perceive themselves as members of a group and (b) share resources with each other despite an underlying competitive structure. We apply a concept of structural cohesion to…

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...

Evolution of opinions on social networks in the presence of competing committed groups.

Science.gov (United States)

Xie, Jierui; Emenheiser, Jeffrey; Kirby, Matthew; Sreenivasan, Sameet; Szymanski, Boleslaw K; Korniss, Gyorgy

2012-01-01

Public opinion is often affected by the presence of committed groups of individuals dedicated to competing points of view. Using a model of pairwise social influence, we study how the presence of such groups within social networks affects the outcome and the speed of evolution of the overall opinion on the network. Earlier work indicated that a single committed group within a dense social network can cause the entire network to quickly adopt the group's opinion (in times scaling logarithmically with the network size), so long as the committed group constitutes more than about 10% of the population (with the findings being qualitatively similar for sparse networks as well). Here we study the more general case of opinion evolution when two groups committed to distinct, competing opinions A and B, and constituting fractions pA and pB of the total population respectively, are present in the network. We show for stylized social networks (including Erdös-Rényi random graphs and Barabási-Albert scale-free networks) that the phase diagram of this system in parameter space (pA,pB) consists of two regions, one where two stable steady-states coexist, and the remaining where only a single stable steady-state exists. These two regions are separated by two fold-bifurcation (spinodal) lines which meet tangentially and terminate at a cusp (critical point). We provide further insights to the phase diagram and to the nature of the underlying phase transitions by investigating the model on infinite (mean-field limit), finite complete graphs and finite sparse networks. For the latter case, we also derive the scaling exponent associated with the exponential growth of switching times as a function of the distance from the critical point.

Constructing Social Networks from Unstructured Group Dialog in Virtual Worlds

Science.gov (United States)

Shah, Fahad; Sukthankar, Gita

Virtual worlds and massively multi-player online games are rich sources of information about large-scale teams and groups, offering the tantalizing possibility of harvesting data about group formation, social networks, and network evolution. However these environments lack many of the cues that facilitate natural language processing in other conversational settings and different types of social media. Public chat data often features players who speak simultaneously, use jargon and emoticons, and only erratically adhere to conversational norms. In this paper, we present techniques for inferring the existence of social links from unstructured conversational data collected from groups of participants in the Second Life virtual world. We present an algorithm for addressing this problem, Shallow Semantic Temporal Overlap (SSTO), that combines temporal and language information to create directional links between participants, and a second approach that relies on temporal overlap alone to create undirected links between participants. Relying on temporal overlap is noisy, resulting in a low precision and networks with many extraneous links. In this paper, we demonstrate that we can ameliorate this problem by using network modularity optimization to perform community detection in the noisy networks and severing cross-community links. Although using the content of the communications still results in the best performance, community detection is effective as a noise reduction technique for eliminating the extra links created by temporal overlap alone.

Identifying a system of predominant negative symptoms: Network analysis of three randomized clinical trials.

Science.gov (United States)

Levine, Stephen Z; Leucht, Stefan

2016-12-01

Reasons for the recent mixed success of research into negative symptoms may be informed by conceptualizing negative symptoms as a system that is identifiable from network analysis. We aimed to identify: (I) negative symptom systems; (I) central negative symptoms within each system; and (III) differences between the systems, based on network analysis of negative symptoms for baseline, endpoint and change. Patients with chronic schizophrenia and predominant negative symptoms participated in three clinical trials that compared placebo and amisulpride to 60days (n=487). Networks analyses were computed from the Scale for the Assessment of Negative Symptoms (SANS) scores for baseline and endpoint for severity, and estimated change based on mixed models. Central symptoms to each network were identified. The networks were contrasted for connectivity with permutation tests. Network analysis showed that the baseline and endpoint symptom severity systems formed symptom groups of Affect, Poor responsiveness, Lack of interest, and Apathy-inattentiveness. The baseline and endpoint networks did not significantly differ in terms of connectivity, but both significantly (Psymptom group split into three other groups. The most central symptoms were Decreased Spontaneous Movements at baseline and endpoint, and Poverty of Speech for estimated change. Results provide preliminary evidence for: (I) a replicable negative symptom severity system; and (II) symptoms with high centrality (e.g., Decreased Spontaneous Movement), that may be future treatment targets following replication to ensure the curent results generalize to other samples. Copyright © 2016 Elsevier B.V. All rights reserved.

Brain Structure Network Analysis in Patients with Obstructive Sleep Apnea.

Directory of Open Access Journals (Sweden)

Yun-Gang Luo

Full Text Available Childhood obstructive sleep apnea (OSA is a sleeping disorder commonly affecting school-aged children and is characterized by repeated episodes of blockage of the upper airway during sleep. In this study, we performed a graph theoretical analysis on the brain morphometric correlation network in 25 OSA patients (OSA group; 5 female; mean age, 10.1 ± 1.8 years and investigated the topological alterations in global and regional properties compared with 20 healthy control individuals (CON group; 6 females; mean age, 10.4 ± 1.8 years. A structural correlation network based on regional gray matter volume was constructed respectively for each group. Our results revealed a significantly decreased mean local efficiency in the OSA group over the density range of 0.32-0.44 (p < 0.05. Regionally, the OSAs showed a tendency of decreased betweenness centrality in the left angular gyrus, and a tendency of decreased degree in the right lingual and inferior frontal (orbital part gyrus (p < 0.005, uncorrected. We also found that the network hubs in OSA and controls were distributed differently. To the best of our knowledge, this is the first study that characterizes the brain structure network in OSA patients and invests the alteration of topological properties of gray matter volume structural network. This study may help to provide new evidence for understanding the neuropathophysiology of OSA from a topological perspective.

CATHENA 4. A thermalhydraulics network analysis code

International Nuclear Information System (INIS)

Aydemir, N.U.; Hanna, B.N.

2009-01-01

Canadian Algorithm for THErmalhydraulic Network Analysis (CATHENA) is a one-dimensional, non-equilibrium, two-phase, two fluid network analysis code that has been in use for over two decades by various groups in Canada and around the world. The objective of the present paper is to describe the design, application and future development plans for the CATHENA 4 thermalhydraulics network analysis code, which is a modernized version of the present frozen CATHENA 3 code. The new code is designed in modular form, using the Fortran 95 (F95) programming language. The semi-implicit numerical integration scheme of CATHENA 3 is re-written to implement a fully-implicit methodology using Newton's iterative solution scheme suitable for nonlinear equations. The closure relations, as a first step, have been converted from the existing CATHENA 3 implementation to F95 but modularized to achieve ease of maintenance. The paper presents the field equations, followed by a description of the Newton's scheme used. The finite-difference form of the field equations is given, followed by a discussion of convergence criteria. Two applications of CATHENA 4 are presented to demonstrate the temporal and spatial convergence of the new code for problems with known solutions or available experimental data. (author)

System Biology Approach: Gene Network Analysis for Muscular Dystrophy.

Science.gov (United States)

Censi, Federica; Calcagnini, Giovanni; Mattei, Eugenio; Giuliani, Alessandro

2018-01-01

Phenotypic changes at different organization levels from cell to entire organism are associated to changes in the pattern of gene expression. These changes involve the entire genome expression pattern and heavily rely upon correlation patterns among genes. The classical approach used to analyze gene expression data builds upon the application of supervised statistical techniques to detect genes differentially expressed among two or more phenotypes (e.g., normal vs. disease). The use of an a posteriori, unsupervised approach based on principal component analysis (PCA) and the subsequent construction of gene correlation networks can shed a light on unexpected behaviour of gene regulation system while maintaining a more naturalistic view on the studied system.In this chapter we applied an unsupervised method to discriminate DMD patient and controls. The genes having the highest absolute scores in the discrimination between the groups were then analyzed in terms of gene expression networks, on the basis of their mutual correlation in the two groups. The correlation network structures suggest two different modes of gene regulation in the two groups, reminiscent of important aspects of DMD pathogenesis.

Distributed Group-Based Mobility Management Scheme in Wireless Body Area Networks

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Moneeb Gohar

2017-01-01

Full Text Available For group-based mobility management in 6LoWPAN-based wireless body area networks (WBAN, some schemes using the Proxy Mobile IPv6 (PMIP have been proposed. However, the existing PMIP-based mobility schemes tend to induce large registration delay and handover delay. To overcome such limitations, we propose a new distributed group-based mobility management scheme, in which the Local Mobility Anchor (LMA function is implemented by each Mobile Access Gateway (MAG and the handover operation is performed between two neighboring MAGs without the help of LMA. Besides, each MAG maintains the information of the group of mobile sensors and aggregates the Authentication-Authorization-Accounting (AAA query messages for a group of mobile sensors as a “single” message to decrease the control overhead. By numerical analysis, it is shown that the proposed scheme can reduce the registration and handover delays, compared to the existing PMIP-based mobility schemes.

Construction and Analysis of Functional Networks in the Gut Microbiome of Type 2 Diabetes Patients.

Science.gov (United States)

Li, Lianshuo; Wang, Zicheng; He, Peng; Ma, Shining; Du, Jie; Jiang, Rui

2016-10-01

Although networks of microbial species have been widely used in the analysis of 16S rRNA sequencing data of a microbiome, the construction and analysis of a complete microbial gene network are in general problematic because of the large number of microbial genes in metagenomics studies. To overcome this limitation, we propose to map microbial genes to functional units, including KEGG orthologous groups and the evolutionary genealogy of genes: Non-supervised Orthologous Groups (eggNOG) orthologous groups, to enable the construction and analysis of a microbial functional network. We devised two statistical methods to infer pairwise relationships between microbial functional units based on a deep sequencing dataset of gut microbiome from type 2 diabetes (T2D) patients as well as healthy controls. Networks containing such functional units and their significant interactions were constructed subsequently. We conducted a variety of analyses of global properties, local properties, and functional modules in the resulting functional networks. Our data indicate that besides the observations consistent with the current knowledge, this study provides novel biological insights into the gut microbiome associated with T2D. Copyright © 2016. Production and hosting by Elsevier Ltd.

Artificial Neural Network Analysis System

Science.gov (United States)

2001-02-27

Contract No. DASG60-00-M-0201 Purchase request no.: Foot in the Door-01 Title Name: Artificial Neural Network Analysis System Company: Atlantic... Artificial Neural Network Analysis System 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER 6. AUTHOR(S) Powell, Bruce C 5d. PROJECT NUMBER 5e. TASK NUMBER...34) 27-02-2001 Report Type N/A Dates Covered (from... to) ("DD MON YYYY") 28-10-2000 27-02-2001 Title and Subtitle Artificial Neural Network Analysis

Modeling the Role of Networks and Individual Differences in Inter-Group Violence.

Directory of Open Access Journals (Sweden)

Alexander Isakov

Full Text Available There is significant heterogeneity within and between populations in their propensity to engage in conflict. Most research has neglected the role of within-group effects in social networks in contributing to between-group violence and focused instead on the precursors and consequences of violence, or on the role of between-group ties. Here, we explore the role of individual variation and of network structure within a population in promoting and inhibiting group violence towards other populations. Motivated by ethnographic observations of collective behavior in a small-scale society, we describe a model with differentiated roles for individuals embedded within friendship networks. Using a simple model based on voting-like dynamics, we explore several strategies for influencing group-level behavior. When we consider changing population level attitude changes and introducing control nodes separately, we find that a particularly effective control strategy relies on exploiting network degree. We also suggest refinements to our model such as tracking fine-grained information spread dynamics that can lead to further enrichment in using evolutionary game theory models for sociological phenomena.

Group Authentication Scheme for Neighbourhood Area Networks (NANs in Smart Grids

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Bashar Alohali

2016-05-01

Full Text Available A Neighbourhood Area Network is a functional component of the Smart Grid that interconnects the end user domain with the Energy Services Provider (ESP domain. It forms the “edge” of the provider network, interconnecting homes instrumented with Smart Meters (SM with the ESP. The SM is a dual interface, wireless communication device through which information is transacted across the user (a home and ESP domains. The security risk to the ESP increases since the components within the home, interconnected to the ESP via the SM, are not managed by the ESP. Secure operation of the SM is a necessary requirement. The SM should be resilient to attacks, which might be targeted either directly or via the network in the home. This paper presents and discusses a security scheme for groups of SMs in a Neighbourhood Area Network that enable entire groups to authenticate themselves, rather than one at a time. The results show that a significant improvement in terms of resilience against node capture attacks, replay attacks, confidentiality, authentication for groups of SMs in a NAN that enable entire groups to authenticate themselves, rather than one at a time.

Network-based group variable selection for detecting expression quantitative trait loci (eQTL

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Zhang Xuegong

2011-06-01

Full Text Available Abstract Background Analysis of expression quantitative trait loci (eQTL aims to identify the genetic loci associated with the expression level of genes. Penalized regression with a proper penalty is suitable for the high-dimensional biological data. Its performance should be enhanced when we incorporate biological knowledge of gene expression network and linkage disequilibrium (LD structure between loci in high-noise background. Results We propose a network-based group variable selection (NGVS method for QTL detection. Our method simultaneously maps highly correlated expression traits sharing the same biological function to marker sets formed by LD. By grouping markers, complex joint activity of multiple SNPs can be considered and the dimensionality of eQTL problem is reduced dramatically. In order to demonstrate the power and flexibility of our method, we used it to analyze two simulations and a mouse obesity and diabetes dataset. We considered the gene co-expression network, grouped markers into marker sets and treated the additive and dominant effect of each locus as a group: as a consequence, we were able to replicate results previously obtained on the mouse linkage dataset. Furthermore, we observed several possible sex-dependent loci and interactions of multiple SNPs. Conclusions The proposed NGVS method is appropriate for problems with high-dimensional data and high-noise background. On eQTL problem it outperforms the classical Lasso method, which does not consider biological knowledge. Introduction of proper gene expression and loci correlation information makes detecting causal markers more accurate. With reasonable model settings, NGVS can lead to novel biological findings.

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...... approaches. The paper finally highlights some of the applications of social network analysis and their implications for trade policies....

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

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Kim Hyun

2011-12-01

Full Text Available Abstract Background Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. Results We herein introduce a framework for network modularization and Bayesian network analysis (FMB to investigate organism’s metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. Conclusions After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network.

Science.gov (United States)

Kim, Hyun Uk; Kim, Tae Yong; Lee, Sang Yup

2011-01-01

Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology. We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model. After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis.

Burning through organizational boundaries? Examining inter-organizational communication networks in policy-mandated collaborative bushfire planning groups

Science.gov (United States)

Rachel F. Brummel; Kristen C. Nelson; Pamela J. Jakes

2012-01-01

Collaboration can enhance cooperation across geographic and organizational scales, effectively "burning through" those boundaries. Using structured social network analysis (SNA) and qualitative in-depth interviews, this study examined three collaborative bushfire planning groups in New South Wales, Australia and asked: How does participation in policy-...

Using Epistemic Network Analysis to understand core topics as planned learning objectives

DEFF Research Database (Denmark)

Allsopp, Benjamin Brink; Dreyøe, Jonas; Misfeldt, Morten

Epistemic Network Analysis is a tool developed by the epistemic games group at the University of Wisconsin Madison for tracking the relations between concepts in students discourse (Shaffer 2017). In our current work we are applying this tool to learning objectives in teachers digital preparation....... The danish mathematics curriculum is organised in six competencies and three topics. In the recently implemented learning platforms teacher choose which of the mathematical competencies that serves as objective for a specific lesson or teaching sequence. Hence learning objectives for lessons and teaching...... sequences are defining a network of competencies, where two competencies are closely related of they often are part of the same learning objective or teaching sequence. We are currently using Epistemic Network Analysis to study these networks. In the poster we will include examples of different networks...

Analysis of Recurrent Analog Neural Networks

Directory of Open Access Journals (Sweden)

Z. Raida

1998-06-01

Full Text Available In this paper, an original rigorous analysis of recurrent analog neural networks, which are built from opamp neurons, is presented. The analysis, which comes from the approximate model of the operational amplifier, reveals causes of possible non-stable states and enables to determine convergence properties of the network. Results of the analysis are discussed in order to enable development of original robust and fast analog networks. In the analysis, the special attention is turned to the examination of the influence of real circuit elements and of the statistical parameters of processed signals to the parameters of the network.

Cooperative behavior evolution of small groups on interconnected networks

International Nuclear Information System (INIS)

Huang, Keke; Cheng, Yuan; Zheng, Xiaoping; Yang, Yeqing

2015-01-01

Highlights: • Small groups are modeled on interconnected networks. • Players face different dilemmas inside and outside small groups. • Impact of the ratio and strength of link on the behavioral evolution are studied. - Abstract: Understanding the behavioral evolution in evacuation is significant for guiding and controlling the evacuation process. Based on the fact that the population consists of many small groups, here we model the small groups which are separated in space but linked by other methods, such as kinship, on interconnected networks. Namely, the players in the same layer belong to an identical small group, while the players located in different layers belong to different small groups. And the players of different layers establish interaction by edge crossed layers. In addition, players face different dilemmas inside and outside small groups, in detail, the players in the same layer play prisoner’s dilemma, but players in different layers play harmony game. By means of numerous simulations, we study the impact of the ratio and strength of link on the behavioral evolution. Because the framework of this work takes the space distribution into account, which is close to the realistic life, we hope that it can provide a new insight to reveal the law of behavioral evolution of evacuation population.

Social Network Analysis as a Methodological Approach to Explore Health Systems: A Case Study Exploring Support among Senior Managers/Executives in a Hospital Network.

Science.gov (United States)

De Brún, Aoife; McAuliffe, Eilish

2018-03-13

Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network- and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.

Social Network Analysis as a Methodological Approach to Explore Health Systems: A Case Study Exploring Support among Senior Managers/Executives in a Hospital Network

Directory of Open Access Journals (Sweden)

Aoife De Brún

2018-03-01

Full Text Available Health systems research recognizes the complexity of healthcare, and the interacting and interdependent nature of components of a health system. To better understand such systems, innovative methods are required to depict and analyze their structures. This paper describes social network analysis as a methodology to depict, diagnose, and evaluate health systems and networks therein. Social network analysis is a set of techniques to map, measure, and analyze social relationships between people, teams, and organizations. Through use of a case study exploring support relationships among senior managers in a newly established hospital group, this paper illustrates some of the commonly used network- and node-level metrics in social network analysis, and demonstrates the value of these maps and metrics to understand systems. Network analysis offers a valuable approach to health systems and services researchers as it offers a means to depict activity relevant to network questions of interest, to identify opinion leaders, influencers, clusters in the network, and those individuals serving as bridgers across clusters. The strengths and limitations inherent in the method are discussed, and the applications of social network analysis in health services research are explored.

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.

An investigation and comparison on network performance analysis

OpenAIRE

Lanxiaopu, Mi

2012-01-01

This thesis is generally about network performance analysis. It contains two parts. The theory part summarizes what network performance is and inducts the methods of doing network performance analysis. To answer what network performance is, a study into what network services are is done. And based on the background research, there are two important network performance metrics: Network delay and Throughput should be included in network performance analysis. Among the methods of network a...

Structural brain network analysis in families multiply affected with bipolar I disorder.

Science.gov (United States)

Forde, Natalie J; O'Donoghue, Stefani; Scanlon, Cathy; Emsell, Louise; Chaddock, Chris; Leemans, Alexander; Jeurissen, Ben; Barker, Gareth J; Cannon, Dara M; Murray, Robin M; McDonald, Colm

2015-10-30

Disrupted structural connectivity is associated with psychiatric illnesses including bipolar disorder (BP). Here we use structural brain network analysis to investigate connectivity abnormalities in multiply affected BP type I families, to assess the utility of dysconnectivity as a biomarker and its endophenotypic potential. Magnetic resonance diffusion images for 19 BP type I patients in remission, 21 of their first degree unaffected relatives, and 18 unrelated healthy controls underwent tractography. With the automated anatomical labelling atlas being used to define nodes, a connectivity matrix was generated for each subject. Network metrics were extracted with the Brain Connectivity Toolbox and then analysed for group differences, accounting for potential confounding effects of age, gender and familial association. Whole brain analysis revealed no differences between groups. Analysis of specific mainly frontal regions, previously implicated as potentially endophenotypic by functional magnetic resonance imaging analysis of the same cohort, revealed a significant effect of group in the right medial superior frontal gyrus and left middle frontal gyrus driven by reduced organisation in patients compared with controls. The organisation of whole brain networks of those affected with BP I does not differ from their unaffected relatives or healthy controls. In discreet frontal regions, however, anatomical connectivity is disrupted in patients but not in their unaffected relatives. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

The Effects of Social Network Centrality on Group Satisfaction

National Research Council Canada - National Science Library

Choi, Peter M

2007-01-01

.... To determine the relationship between social network centrality and work group satisfaction, a longitudinal field study was conducted on 440 active duty enlisted military members in a leadership...

Social network analysis of duplicative prescriptions: One-month analysis of medical facilities in Japan.

Science.gov (United States)

Takahashi, Yoshimitsu; Ishizaki, Tatsuro; Nakayama, Takeo; Kawachi, Ichiro

2016-03-01

Duplicative prescriptions refer to situations in which patients receive medications for the same condition from two or more sources. Health officials in Japan have expressed concern about medical "waste" resulting from this practices. We sought to conduct descriptive analysis of duplicative prescriptions using social network analysis and to report their prevalence across ages. We analyzed a health insurance claims database including 1.24 million people from December 2012. Through social network analysis, we examined the duplicative prescription networks, representing each medical facility as nodes, and individual prescriptions for patients as edges. The prevalence of duplicative prescription for any drug class was strongly correlated with its frequency of prescription (r=0.90). Among patients aged 0-19, cough and colds drugs showed the highest prevalence of duplicative prescriptions (10.8%). Among people aged 65 and over, antihypertensive drugs had the highest frequency of prescriptions, but the prevalence of duplicative prescriptions was low (0.2-0.3%). Social network analysis revealed clusters of facilities connected via duplicative prescriptions, e.g., psychotropic drugs showed clustering due to a few patients receiving drugs from 10 or more facilities. Overall, the prevalence of duplicative prescriptions was quite low - less than 10% - although the extent of the problem varied by drug class and age group. Our approach illustrates the potential utility of using a social network approach to understand these practices. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Northern emporia and maritime networks. Modelling past communication using archaeological network analysis

DEFF Research Database (Denmark)

Sindbæk, Søren Michael

2015-01-01

preserve patterns of thisinteraction. Formal network analysis and modelling holds the potential to identify anddemonstrate such patterns, where traditional methods often prove inadequate. Thearchaeological study of communication networks in the past, however, calls for radically different analytical...... this is not a problem of network analysis, but network synthesis: theclassic problem of cracking codes or reconstructing black-box circuits. It is proposedthat archaeological approaches to network synthesis must involve a contextualreading of network data: observations arising from individual contexts, morphologies...

Smoking-based selection and influence in gender-segregated friendship networks: a social network analysis of adolescent smoking.

Science.gov (United States)

Mercken, Liesbeth; Snijders, Tom A B; Steglich, Christian; Vertiainen, Erkki; de Vries, Hein

2010-07-01

The main goal of this study was to examine differences between adolescent male and female friendship networks regarding smoking-based selection and influence processes using newly developed social network analysis methods that allow the current state of continuously changing friendship networks to act as a dynamic constraint for changes in smoking behaviour, while allowing current smoking behaviour to be simultaneously a dynamic constraint for changes in friendship networks. Longitudinal design with four measurements. Nine junior high schools in Finland. A total of 1163 adolescents (mean age = 13.6 years) who participated in the control group of the ESFA (European Smoking prevention Framework Approach) study, including 605 males and 558 females. Smoking behaviour of adolescents, parents, siblings and friendship ties. Smoking-based selection of friends was found in male as well as female networks. However, support for influence among friends was found only in female networks. Furthermore, females and males were both influenced by parental smoking behaviour. In Finnish adolescents, both male and female smokers tend to select other smokers as friends but it appears that only females are influenced to smoke by their peer group. This suggests that prevention campaigns targeting resisting peer pressure may be more effective in adolescent girls than boys.

HIGH: A Hexagon-based Intelligent Grouping Approach in Wireless Sensor Networks

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FAN, C.-S.

2016-02-01

Full Text Available In a random deployment or uniform deployment strategy, sensor nodes are scattered randomly or uniformly in the sensing field, respectively. Hence, the coverage ratio cannot be guaranteed. The coverage ratio of uniform deployment, in general, is larger than that of the random deployment strategy. However, a random deployment or uniform deployment strategy may cause unbalanced traffic pattern in wireless sensor networks (WSNs. Therefore, cluster heads (CHs around the sink have larger loads than those farther away from the sink. That is, CHs close to the sink exhaust their energy earlier. In order to overcome the above problem, we propose a Hexagon-based Intelligent Grouping approacH in WSNs (called HIGH. The coverage, energy consumption and data routing issues are well investigated and taken into consideration in the proposed HIGH scheme. The simulation results validate our theoretical analysis and show that the proposed HIGH scheme achieves a satisfactory coverage ratio, balances the energy consumption among sensor nodes, and extends network lifetime significantly.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

Science.gov (United States)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Expanded flux variability analysis on metabolic network of Escherichia coli

Institute of Scientific and Technical Information of China (English)

CHEN Tong; XIE ZhengWei; OUYANG Qi

2009-01-01

Flux balance analysis,based on the mass conservation law in a cellular organism,has been extensively employed to study the interplay between structures and functions of cellular metabolic networks.Consequently,the phenotypes of the metabolism can be well elucidated.In this paper,we introduce the Expanded Flux Variability Analysis (EFVA) to characterize the intrinsic nature of metabolic reactions,such as flexibility,modularity and essentiality,by exploring the trend of the range,the maximum and the minimum flux of reactions.We took the metabolic network of Escherichia coli as an example and analyzed the variability of reaction fluxes under different growth rate constraints.The average variability of all reactions decreases dramatically when the growth rate increases.Consider the noise effect on the metabolic system,we thus argue that the microorganism may practically grow under a suboptimal state.Besides,under the EFVA framework,the reactions are easily to be grouped into catabolic and anabolic groups.And the anabolic groups can be further assigned to specific biomass constitute.We also discovered the growth rate dependent essentiality of reactions.

Capacity Analysis of Wireless Mesh Networks

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M. I. Gumel

2012-06-01

Full Text Available The next generation wireless networks experienced a great 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 offers broadband wireless access to community and enterprise users, the problems that limit the network capacity must be addressed to exploit the optimum network performance. The wireless mesh network capacity analysis shows that the throughput of each mesh node degrades in order of l/n with increasing number of nodes (n in a linear topology. The degradation is found to be higher in a fully mesh network as a result of increase in interference and MAC layer contention in the network.

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…

Connecting the Dots: Social Network Structure, Conflict, and Group Cognitive Complexity

Science.gov (United States)

Curseu, Petru L.; Janssen, Steffie E. A.; Raab, Jorg

2012-01-01

The current paper combines arguments from the social capital and group cognition literature to explain two different processes through which communication network structures and intra group conflict influence groups' cognitive complexity (GCC). We test in a sample of 44 groups the mediating role of intra group conflict in the relationship between…

Integrative analysis of many weighted co-expression networks using tensor computation.

Directory of Open Access Journals (Sweden)

Wenyuan Li

2011-06-01

Full Text Available The rapid accumulation of biological networks poses new challenges and calls for powerful integrative analysis tools. Most existing methods capable of simultaneously analyzing a large number of networks were primarily designed for unweighted networks, and cannot easily be extended to weighted networks. However, it is known that transforming weighted into unweighted networks by dichotomizing the edges of weighted networks with a threshold generally leads to information loss. We have developed a novel, tensor-based computational framework for mining recurrent heavy subgraphs in a large set of massive weighted networks. Specifically, we formulate the recurrent heavy subgraph identification problem as a heavy 3D subtensor discovery problem with sparse constraints. We describe an effective approach to solving this problem by designing a multi-stage, convex relaxation protocol, and a non-uniform edge sampling technique. We applied our method to 130 co-expression networks, and identified 11,394 recurrent heavy subgraphs, grouped into 2,810 families. We demonstrated that the identified subgraphs represent meaningful biological modules by validating against a large set of compiled biological knowledge bases. We also showed that the likelihood for a heavy subgraph to be meaningful increases significantly with its recurrence in multiple networks, highlighting the importance of the integrative approach to biological network analysis. Moreover, our approach based on weighted graphs detects many patterns that would be overlooked using unweighted graphs. In addition, we identified a large number of modules that occur predominately under specific phenotypes. This analysis resulted in a genome-wide mapping of gene network modules onto the phenome. Finally, by comparing module activities across many datasets, we discovered high-order dynamic cooperativeness in protein complex networks and transcriptional regulatory networks.

75 FR 79440 - Financial Crimes Enforcement Network; Bank Secrecy Act Advisory Group; Solicitation of...

Science.gov (United States)

2010-12-20

... DEPARTMENT OF THE TREASURY Financial Crimes Enforcement Network; Bank Secrecy Act Advisory Group; Solicitation of Application for Membership AGENCY: Financial Crimes Enforcement Network, Department of the... financial institutions and trade groups for membership on the Bank Secrecy Act Advisory Group. New members...

Review of Recent Methodological Developments in Group-Randomized Trials: Part 2-Analysis.

Science.gov (United States)

Turner, Elizabeth L; Prague, Melanie; Gallis, John A; Li, Fan; Murray, David M

2017-07-01

In 2004, Murray et al. reviewed methodological developments in the design and analysis of group-randomized trials (GRTs). We have updated that review with developments in analysis of the past 13 years, with a companion article to focus on developments in design. We discuss developments in the topics of the earlier review (e.g., methods for parallel-arm GRTs, individually randomized group-treatment trials, and missing data) and in new topics, including methods to account for multiple-level clustering and alternative estimation methods (e.g., augmented generalized estimating equations, targeted maximum likelihood, and quadratic inference functions). In addition, we describe developments in analysis of alternative group designs (including stepped-wedge GRTs, network-randomized trials, and pseudocluster randomized trials), which require clustering to be accounted for in their design and analysis.

Network analysis of a regional fishery: Implications for management of natural resources, and recruitment and retention of anglers

Science.gov (United States)

Martin, Dustin R.; Shizuka, Daizaburo; Chizinski, Christopher J.; Pope, Kevin L.

2017-01-01

Angler groups and water-body types interact to create a complex social-ecological system. Network analysis could inform detailed mechanistic models on, and provide managers better information about, basic patterns of fishing activity. Differences in behavior and reservoir selection among angler groups in a regional fishery, the Salt Valley fishery in southeastern Nebraska, USA, were assessed using a combination of cluster and network analyses. The four angler groups assessed ranged from less active, unskilled anglers (group One) to highly active, very skilled anglers (group Four). Reservoir use patterns and the resulting network communities of these four angler groups differed; the number of reservoir communities for these groups ranged from two to three and appeared to be driven by reservoir location (group One), reservoir size and its associated attributes (groups Two and Four), or an interaction between reservoir size and location (group Three). Network analysis is a useful tool to describe differences in participation among angler groups within a regional fishery, and provides new insights about possible recruitment of anglers. For example, group One anglers fished reservoirs closer to home and had a greater probability of dropping out if local reservoir access were restricted.

Seeking support on facebook: a content analysis of breast cancer groups.

Science.gov (United States)

Bender, Jacqueline L; Jimenez-Marroquin, Maria-Carolina; Jadad, Alejandro R

2011-02-04

Social network sites have been growing in popularity across broad segments of Internet users, and are a convenient means to exchange information and support. Research on their use for health-related purposes is limited. This study aimed to characterize the purpose, use, and creators of Facebook groups related to breast cancer. We searched Facebook (www.Facebook.com) using the term breast cancer. We restricted our analysis to groups that were related to breast cancer, operated in English, and were publicly available. Two of us independently extracted information on the administrator and purpose of the group, as well as the number of user-generated contributions. We developed a coding scheme to guide content analysis. We found 620 breast cancer groups on Facebook containing a total of 1,090,397 members. The groups were created for fundraising (277/620, 44.7%), awareness (236, 38.1%), product or service promotion related to fundraising or awareness (61, 9%), or patient/caregiver support (46, 7%). The awareness groups as a whole contained by far the most members (n = 957,289). The majority of groups (532, 85.8%) had 25 wall posts or fewer. The support oriented groups, 47% (27/57) of which were established by high school or college students, were associated with the greatest number of user-generated contributions. Facebook groups have become a popular tool for awareness-raising, fundraising, and support-seeking related to breast cancer attracting over one million users. Given their popularity and reach, further research is warranted to explore the implications of social network sites as a health resource across various health conditions, cultures, ages, and socioeconomic groups.

Analysis of Network Parameters Influencing Performance of Hybrid Multimedia Networks

Directory of Open Access Journals (Sweden)

Dominik Kovac

2013-10-01

Full Text Available Multimedia networks is an emerging subject that currently attracts the attention of research and industrial communities. This environment provides new entertainment services and business opportunities merged with all well-known network services like VoIP calls or file transfers. Such a heterogeneous system has to be able satisfy all network and end-user requirements which are increasing constantly. Therefore the simulation tools enabling deep analysis in order to find the key performance indicators and factors which influence the overall quality for specific network service the most are highly needed. This paper provides a study on the network parameters like communication technology, routing protocol, QoS mechanism, etc. and their effect on the performance of hybrid multimedia network. The analysis was performed in OPNET Modeler environment and the most interesting results are discussed at the end of this paper

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.

Demography-based adaptive network model reproduces the spatial organization of human linguistic groups

Science.gov (United States)

Capitán, José A.; Manrubia, Susanna

2015-12-01

The distribution of human linguistic groups presents a number of interesting and nontrivial patterns. The distributions of the number of speakers per language and the area each group covers follow log-normal distributions, while population and area fulfill an allometric relationship. The topology of networks of spatial contacts between different linguistic groups has been recently characterized, showing atypical properties of the degree distribution and clustering, among others. Human demography, spatial conflicts, and the construction of networks of contacts between linguistic groups are mutually dependent processes. Here we introduce an adaptive network model that takes all of them into account and successfully reproduces, using only four model parameters, not only those features of linguistic groups already described in the literature, but also correlations between demographic and topological properties uncovered in this work. Besides their relevance when modeling and understanding processes related to human biogeography, our adaptive network model admits a number of generalizations that broaden its scope and make it suitable to represent interactions between agents based on population dynamics and competition for space.

What Online Networks Offer: "Online Network Compositions and Online Learning Experiences of Three Ethnic Groups"

Science.gov (United States)

Lecluijze, Suzanne Elisabeth; de Haan, Mariëtte; Ünlüsoy, Asli

2015-01-01

This exploratory study examines ethno-cultural diversity in youth's narratives regarding their "online" learning experiences while also investigating how these narratives can be understood from the analysis of their online network structure and composition. Based on ego-network data of 79 respondents this study compared the…

The networked minority: How a small group prevailed in a local windfarm conflict

International Nuclear Information System (INIS)

Anderson, Carmel

2013-01-01

This paper aims to explain through a qualitative case study how a small protest group prevailed during a local windfarm conflict in south-eastern Australia. A social capital analytical framework was developed to analyse the data. The analysis found that two communities inhabited the area for which the windfarm development was proposed. The public participation process failed to address the concerns of both communities and led to the emergence of a social network of resistance. The network had high stocks of bridging social capital, which enabled an effective protest that led to the abandonment of the development. Their effectiveness was inadvertently aided by the windfarm supporters who were unable to act collectively to defend their interests because socio-economic changes in the community among other factors had led to a depletion of their social capital. In this context, different democratic participatory processes were needed to address the concerns of the two communities. Guidance and tools for researching and developing the types of participatory processes needed for vulnerable communities with low social capital and those similar to the social network with high social capital are provided. These will inform community-appropriate public participation processes and participatory planning policy. - Highlights: ► A case study of a local social network's resistance to a windfarm is undertaken. ► The link between high social capital and resistance is confirmed. ► Successful protest groups can be aided by passive windfarm supporters. ► Protesters are likely to participate in well-designed participatory processes. ► Guidance for developing community-specific participatory processes is provided

Network meta-analysis: an introduction for pharmacists.

Science.gov (United States)

Xu, Yina; Amiche, Mohamed Amine; Tadrous, Mina

2018-05-21

Network meta-analysis is a new tool used to summarize and compare studies for multiple interventions, irrespective of whether these interventions have been directly evaluated against each other. Network meta-analysis is quickly becoming the standard in conducting therapeutic reviews and clinical guideline development. However, little guidance is available to help pharmacists review network meta-analysis studies in their practice. Major institutions such as the Cochrane Collaboration, Agency for Healthcare Research and Quality, Canadian Agency for Drugs and Technologies in Health, and National Institute for Health and Care Excellence Decision Support Unit have endorsed utilizing network meta-analysis to establish therapeutic evidence and inform decision making. Our objective is to introduce this novel technique to pharmacy practitioners, and highlight key assumptions behind network meta-analysis studies.

Meta-analytically informed network analysis of resting state FMRI reveals hyperconnectivity in an introspective socio-affective network in depression.

Directory of Open Access Journals (Sweden)

Leonhard Schilbach

Full Text Available Alterations of social cognition and dysfunctional interpersonal expectations are thought to play an important role in the etiology of depression and have, thus, become a key target of psychotherapeutic interventions. The underlying neurobiology, however, remains elusive. Based upon the idea of a close link between affective and introspective processes relevant for social interactions and alterations thereof in states of depression, we used a meta-analytically informed network analysis to investigate resting-state functional connectivity in an introspective socio-affective (ISA network in individuals with and without depression. Results of our analysis demonstrate significant differences between the groups with depressed individuals showing hyperconnectivity of the ISA network. These findings demonstrate that neurofunctional alterations exist in individuals with depression in a neural network relevant for introspection and socio-affective processing, which may contribute to the interpersonal difficulties that are linked to depressive symptomatology.

Discriminating response groups in metabolic and regulatory pathway networks.

Science.gov (United States)

Van Hemert, John L; Dickerson, Julie A

2012-04-01

Analysis of omics experiments generates lists of entities (genes, metabolites, etc.) selected based on specific behavior, such as changes in response to stress or other signals. Functional interpretation of these lists often uses category enrichment tests using functional annotations like Gene Ontology terms and pathway membership. This approach does not consider the connected structure of biochemical pathways or the causal directionality of events. The Omics Response Group (ORG) method, described in this work, interprets omics lists in the context of metabolic pathway and regulatory networks using a statistical model for flow within the networks. Statistical results for all response groups are visualized in a novel Pathway Flow plot. The statistical tests are based on the Erlang distribution model under the assumption of independent and identically Exponential-distributed random walk flows through pathways. As a proof of concept, we applied our method to an Escherichia coli transcriptomics dataset where we confirmed common knowledge of the E.coli transcriptional response to Lipid A deprivation. The main response is related to osmotic stress, and we were also able to detect novel responses that are supported by the literature. We also applied our method to an Arabidopsis thaliana expression dataset from an abscisic acid study. In both cases, conventional pathway enrichment tests detected nothing, while our approach discovered biological processes beyond the original studies. We created a prototype for an interactive ORG web tool at http://ecoserver.vrac.iastate.edu/pathwayflow (source code is available from https://subversion.vrac.iastate.edu/Subversion/jlv/public/jlv/pathwayflow). The prototype is described along with additional figures and tables in Supplementary Material. julied@iastate.edu Supplementary data are available at Bioinformatics online.

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...

76 FR 19466 - Masco Builder Cabinet Group Including On-Site Leased Workers From Reserves Network, Reliable...

Science.gov (United States)

2011-04-07

... Builder Cabinet Group Including On-Site Leased Workers From Reserves Network, Reliable Staffing, and Third Dimension Waverly, OH; Masco Builder Cabinet Group Including On-Site Leased Workers From Reserves Network... Group including on-site leased workers from Reserves Network, Jackson, Ohio. The workers produce...

Changes of Personal Network Composition and Inter-Group Ties from 1987 to 2005 in Hungary

Directory of Open Access Journals (Sweden)

VARGA V., Attila

2014-04-01

Full Text Available The following paper presents the changes and stability of assortative mixing, and inter-group ties in Hungary from 1987 to 2005. The demographic categories under investigation are age, sex, and education. The analysis has a special focus on the rearrangement of the context of tie formation, and the inequality of receiving choices into personal networks along social categories. The most substantial change during the period, is the strong decrease in gender homophily, and some strengthening of intergenerationalties. Both of these findings are in line with the observation that personal networks are recruited more often among the members of the nuclear-family. This latter phenomenon is probably due to the shrinking network size. However, this set of finding is prone to the methodological criticism formulated in the US context, that these observations are in fact the result of the interviewer effect. Finally, the study found stable patterns of educational network prestige, and describes the changes of social capital attached to categories of gender and age.

RILARA: Ibero-American laboratories network of radioactivity analysis in food

International Nuclear Information System (INIS)

Romero, M. Lourdes; Fernandez, Isis M.; Aguirre, Jaime; Melo, Ana C. de; Flores, Yasmine; Igliki, Amanda; Osores, Jose M.; Vasquez, L. Ramiro

2008-01-01

The Ibero-American Laboratories Network of Radioactivity Analysis in Food (RILARA), is a thematic network that was established in the year 2007 with the financial support of the Ibero-American Program of Science and Technology for Development (CYTED). The network brings together laboratories from Argentina, Brazil, Cuba, Ecuador, Spain, Mexico, Peru and Venezuela. The main objective of thematic networks is the transfer of knowledge among the research groups and to foster the cooperation as a working method. Their mission is to create a collaboration framework that allows in the future developing new common actions. The main objective of RILARA is to guarantee the radioactive innocuousness in foodstuffs, to protect consumer's health. Besides, the network aims to facilitate the international trade among Ibero-American countries, by strengthening technical cooperation of radioactivity analysis laboratories in food and by maintaining a continuous exchange of information related to the topic. This paper presents how this network was conceived, its objectives and specific goals. Also actions taken to achieve a stable and continuous interaction among the Ibero-American laboratories controlling radioactivity in food are specified. The completion of these actions is expected to provide technological transfer among countries/Institutions and staff methodology training at developing laboratories. (author)

Gene coexpression network analysis as a source of functional annotation for rice genes.

Directory of Open Access Journals (Sweden)

Kevin L Childs

Full Text Available With the existence of large publicly available plant gene expression data sets, many groups have undertaken data analyses to construct gene coexpression networks and functionally annotate genes. Often, a large compendium of unrelated or condition-independent expression data is used to construct gene networks. Condition-dependent expression experiments consisting of well-defined conditions/treatments have also been used to create coexpression networks to help examine particular biological processes. Gene networks derived from either condition-dependent or condition-independent data can be difficult to interpret if a large number of genes and connections are present. However, algorithms exist to identify modules of highly connected and biologically relevant genes within coexpression networks. In this study, we have used publicly available rice (Oryza sativa gene expression data to create gene coexpression networks using both condition-dependent and condition-independent data and have identified gene modules within these networks using the Weighted Gene Coexpression Network Analysis method. We compared the number of genes assigned to modules and the biological interpretability of gene coexpression modules to assess the utility of condition-dependent and condition-independent gene coexpression networks. For the purpose of providing functional annotation to rice genes, we found that gene modules identified by coexpression analysis of condition-dependent gene expression experiments to be more useful than gene modules identified by analysis of a condition-independent data set. We have incorporated our results into the MSU Rice Genome Annotation Project database as additional expression-based annotation for 13,537 genes, 2,980 of which lack a functional annotation description. These results provide two new types of functional annotation for our database. Genes in modules are now associated with groups of genes that constitute a collective functional

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.

A comparison of national guidelines for network meta-analysis.

Science.gov (United States)

Laws, Andrew; Kendall, Robyn; Hawkins, Neil

2014-07-01

Within technology appraisals, it is necessary to compare the complete set of treatments that may be used in the patient group under consideration. Randomized controlled trials are a key source of evidence for these comparisons. The techniques of network meta-analysis allow the networks of trial evidence to be evaluated to obtain estimates of comparative efficacy between sets of treatments. These techniques may be the only source of estimates of comparative effectiveness if trials directly comparing the treatments of interest have not been conducted, and may provide useful additional evidence if both direct and indirect comparisons exist. We examined both published and draft guidelines from reimbursement and health technology appraisal bodies, and considered their recommendations using appropriate methodology for the conduct of indirect comparisons and the assessments of their validity. Guidelines from 33 countries were reviewed. Of these, guidelines from 9 countries-Australia, Belgium, Canada, France, Germany, Scotland, Spain, South Africa, and the United Kingdom (England and Wales)-included detailed recommendations on the conduct of network meta-analysis. The recommendations were summarized. No two recommendations from the multiple national guidelines are mutually exclusive. It is possible to perform one network meta-analysis for submission to multiple national jurisdictions. Copyright © 2014 International Society for Pharmacoeconomics and Outcomes Research (ISPOR). Published by Elsevier Inc. All rights reserved.

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

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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.

OA20 The positioning of family, friends, community, and service providers in support networks for caring at end-of-life: a social network analysis.

Science.gov (United States)

Leonard, Rosemary; Horsfall, Debbie; Rosenberg, John; Noonan, Kerrie

2015-04-01

Although there is ample evidence of the risk to carers from the burden of caring, there is also evidence that a caring network can relieve the burden on the principal carer, strengthen community relationships, and increase 'Death Literacy' in the community. There is often an assumption that, in caring networks, family and service providers are central and friends and community are marginal. We examined whether this is the case in practice using SNA. To identify the relative positioning of family, friends, community, and service providers in caring networks. In interviews with carers (N = 23) and focus groups with caring networks (N = 13) participants were asked to list the people in the caring network and rate the strength of their relationships to them (0 no relationship to 3 strong relationship). SNA in UCInet was used to map the networks, examine density (number and strength of relationships) across time (when caring began to the present) and across relationship types (family, friends, community, and service providers) supplemented by qualitative data. The analysis revealed significant increases in the density of the networks over time. The density of relationships with friends was similar to that other family. Community and service providers had significantly lower density. Qualitative analysis revealed that often service providers were not seen as part of the networks. To avoid carer burnout, it is important not to make assumptions about where carers obtain support but work with each carer to mobilise any support that is available. © 2015, 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.

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.

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.

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

Science.gov (United States)

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. PMID:27258007

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.

NAP: The Network Analysis Profiler, a web tool for easier topological analysis and comparison of medium-scale biological networks.

Science.gov (United States)

Theodosiou, Theodosios; Efstathiou, Georgios; Papanikolaou, Nikolas; Kyrpides, Nikos C; Bagos, Pantelis G; Iliopoulos, Ioannis; Pavlopoulos, Georgios A

2017-07-14

Nowadays, due to the technological advances of high-throughput techniques, Systems Biology has seen a tremendous growth of data generation. With network analysis, looking at biological systems at a higher level in order to better understand a system, its topology and the relationships between its components is of a great importance. Gene expression, signal transduction, protein/chemical interactions, biomedical literature co-occurrences, are few of the examples captured in biological network representations where nodes represent certain bioentities and edges represent the connections between them. Today, many tools for network visualization and analysis are available. Nevertheless, most of them are standalone applications that often (i) burden users with computing and calculation time depending on the network's size and (ii) focus on handling, editing and exploring a network interactively. While such functionality is of great importance, limited efforts have been made towards the comparison of the topological analysis of multiple networks. Network Analysis Provider (NAP) is a comprehensive web tool to automate network profiling and intra/inter-network topology comparison. It is designed to bridge the gap between network analysis, statistics, graph theory and partially visualization in a user-friendly way. It is freely available and aims to become a very appealing tool for the broader community. It hosts a great plethora of topological analysis methods such as node and edge rankings. Few of its powerful characteristics are: its ability to enable easy profile comparisons across multiple networks, find their intersection and provide users with simplified, high quality plots of any of the offered topological characteristics against any other within the same network. It is written in R and Shiny, it is based on the igraph library and it is able to handle medium-scale weighted/unweighted, directed/undirected and bipartite graphs. NAP is available at http://bioinformatics.med.uoc.gr/NAP .

Network structure exploration in networks with node attributes

Science.gov (United States)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

Analysis of the partnership network in the clean development mechanism

International Nuclear Information System (INIS)

Kang, Moon Jung; Park, Jihyoun

2013-01-01

The clean development mechanism (CDM) is a global collaborative action proposed at the Kyoto Protocol in response to climate change issues. The CDM contributes to cost-efficient reduction of greenhouse gas emissions in industrialized countries and promotes sustainable development in developing countries. Its fundamental framework is based on partnerships between industrialized and developing countries. This study employs social network analysis to investigate the dynamics of the partnership networks observed in 3816 CDM projects registered in the database of the United Nations Framework Convention on Climate Change over the period of 2005 to 2011. Our three main findings can be summarized as follows. First, the CDM partnership network is a small world; however, its density tends to decrease as the number of participants for a CDM project decreases. Second, the partnership networks’ leading groups tend to shift from partner countries into host countries. Third, a host country that pursues more partnership-based projects takes better control of resources and knowledge-flow in the ego-network formed around that country, and can thus better utilize global resources for its CDM projects. - Highlights: ► We investigate dynamics of the international partnership networks of CDM projects. ► The density of CDM networks tends to decrease by time. ► The partnership networks’ leading groups tend to shift into host countries. ► A host country with more partnerships better utilizes global knowledge resources.

Environmental lovers group: a networking of Ciliwung Depok Community

Science.gov (United States)

Mambo Tampi, Daniel; Sumabrata, Jachrizal; Darmajanti, Linda

2018-03-01

The dynamic of urban development needs is social space to bout the rights of urban society. An environmental lovers group was evoked by the environmental and Social Issues cause natural and man-made disruptions. Komunitas Ciliwung Depok (KCD) is located under Grand Depok City’s Bridge to keep an environment such as land conversion and garbage. This study aims to determine the motivation of KCD’s group and to know the social network in Ciliwung riverbank along Depok city. Data were gathered from in-depth interviews, observation and documentation within 4 months. KCD invited the local people to keep and maintain an environment of Ciliwung riverbank. Their strength lies on the actors of KCD founder and involvement of human resources, with the support of public and private sectors facilities. Activities with the local people’s participation through social medias such as Facebook as a media of communication to get volunteers’ aspirations and involvement. The conclusions of this research are KCD’s Network materialized by individual motivation, group awareness and trust between the actors to realizing the common goal.

Communication Network Analysis Methods.

Science.gov (United States)

Farace, Richard V.; Mabee, Timothy

This paper reviews a variety of analytic procedures that can be applied to network data, discussing the assumptions and usefulness of each procedure when applied to the complexity of human communication. Special attention is paid to the network properties measured or implied by each procedure. Factor analysis and multidimensional scaling are among…

Renormalization group theory for percolation in time-varying networks.

Science.gov (United States)

Karschau, Jens; Zimmerling, Marco; Friedrich, Benjamin M

2018-05-22

Motivated by multi-hop communication in unreliable wireless networks, we present a percolation theory for time-varying networks. We develop a renormalization group theory for a prototypical network on a regular grid, where individual links switch stochastically between active and inactive states. The question whether a given source node can communicate with a destination node along paths of active links is equivalent to a percolation problem. Our theory maps the temporal existence of multi-hop paths on an effective two-state Markov process. We show analytically how this Markov process converges towards a memoryless Bernoulli process as the hop distance between source and destination node increases. Our work extends classical percolation theory to the dynamic case and elucidates temporal correlations of message losses. Quantification of temporal correlations has implications for the design of wireless communication and control protocols, e.g. in cyber-physical systems such as self-organized swarms of drones or smart traffic networks.

Improving assessment of personality disorder traits through social network analysis.

Science.gov (United States)

Clifton, Allan; Turkheimer, Eric; Oltmanns, Thomas F

2007-10-01

When assessing personality disorder traits, not all judges make equally valid judgments of all targets. The present study uses social network analysis to investigate factors associated with reliability and validity in peer assessment. Participants were groups of military recruits (N=809) who acted as both targets and judges in a round-robin design. Participants completed self- and informant versions of the Multisource Assessment of Personality Pathology. Social network matrices were constructed based on reported acquaintance, and cohesive subgroups were identified. Judges who shared a mutual subgroup were more reliable and had higher self-peer agreement than those who did not. Partitioning networks into two subgroups achieved more consistent improvements than multiple subgroups. We discuss implications for multiple informant assessments.

The network researchers' network

DEFF Research Database (Denmark)

Henneberg, Stephan C.; Jiang, Zhizhong; Naudé, Peter

2009-01-01

The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987). In thi......The Industrial Marketing and Purchasing (IMP) Group is a network of academic researchers working in the area of business-to-business marketing. The group meets every year to discuss and exchange ideas, with a conference having been held every year since 1984 (there was no meeting in 1987......). 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...

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.

Resources Sharing and Access Control in Group-oriented Networks : Fednet and Related Paradigms

NARCIS (Netherlands)

Ibrohimovna, K.M.; Heemstra de Groot, S.

2009-01-01

A Personal Network (PN) is a network composed of devices of a person that can communicate with each other independently from their geographical location. Extra functionality in PNs enables the cooperation amongst different persons forming a group-oriented network called a Federation of Personal

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.

Examination of a Social-Networking Site Activities Scale (SNSAS) Using Rasch Analysis

Science.gov (United States)

Alhaythami, Hassan; Karpinski, Aryn; Kirschner, Paul; Bolden, Edward

2017-01-01

This study examined the psychometric properties of a social-networking site (SNS) activities scale (SNSAS) using Rasch Analysis. Items were also examined with Rasch Principal Components Analysis (PCA) and Differential Item Functioning (DIF) across groups of university students (i.e., males and females from the United States [US] and Europe; N =…

Signed directed social network analysis applied to group conflict

DEFF Research Database (Denmark)

Zheng, Quan; Skillicorn, David; Walther, Olivier

2015-01-01

Real-world social networks contain relationships of multiple different types, but this richness is often ignored in graph-theoretic modelling. We show how two recently developed spectral embedding techniques, for directed graphs (relationships are asymmetric) and for signed graphs (relationships...... are both positive and negative), can be combined. This combination is particularly appropriate for intelligence, terrorism, and law enforcement applications. We illustrate by applying the novel embedding technique to datasets describing conflict in North-West Africa, and show how unusual interactions can...

Network analysis for the visualization and analysis of qualitative data.

Science.gov (United States)

Pokorny, Jennifer J; Norman, Alex; Zanesco, Anthony P; Bauer-Wu, Susan; Sahdra, Baljinder K; Saron, Clifford D

2018-03-01

We present a novel manner in which to visualize the coding of qualitative data that enables representation and analysis of connections between codes using graph theory and network analysis. Network graphs are created from codes applied to a transcript or audio file using the code names and their chronological location. The resulting network is a representation of the coding data that characterizes the interrelations of codes. This approach enables quantification of qualitative codes using network analysis and facilitates examination of associations of network indices with other quantitative variables using common statistical procedures. Here, as a proof of concept, we applied this method to a set of interview transcripts that had been coded in 2 different ways and the resultant network graphs were examined. The creation of network graphs allows researchers an opportunity to view and share their qualitative data in an innovative way that may provide new insights and enhance transparency of the analytical process by which they reach their conclusions. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Using Social Network Analysis to Better Understand Compulsive Exercise Behavior Among a Sample of Sorority Members.

Science.gov (United States)

Patterson, Megan S; Goodson, Patricia

2017-05-01

Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people. SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET. QAP regression analysis revealed a statistically significant model (R 2 = .375, P exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model. In our sample, women who are connected to "important" or "powerful" people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

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.

Network Analysis in Community Psychology: Looking Back, Looking Forward

OpenAIRE

Neal, Zachary P.; Neal, Jennifer Watling

2017-01-01

Highlights Network analysis is ideally suited for community psychology research because it focuses on context. Use of network analysis in community psychology is growing. Network analysis in community psychology has employed some potentially problematic practices. Recommended practices are identified to improve network analysis in community psychology.

Alignment of global supply networks based on strategic groups of supply chains

Directory of Open Access Journals (Sweden)

Nikos G. Moraitakis

2017-09-01

Full Text Available Background: From a supply chain perspective, often big differences exist between global raw material suppliers’ approaches to supply their respective local markets. The progressing complexity of large centrally managed global supply networks and their often-unknown upstream ramifications increase the likelihood of undetected bottlenecks and inefficiencies. It is therefore necessary to develop an approach to strategically master the upstream complexity of such networks from a holistic supply chain perspective in order to align regional competitive priorities and supply chain structures. The objective of this research is hence to develop an approach for the supply-chain-based alignment of complex global supply networks. Method: We review existing literature from the fields of supply chain and network management, strategic sourcing, and strategic management. Based on the literature review and theoretical and practical considerations we deduce a conceptual approach to consider upstream supply chain structures in supply network alignment initiatives. Results: On the basis of these considerations and current empirical literature we transfer strategic group theory to the supply network management context. The proposed approach introduces strategic groups of supply chains as a segmentation criterion for complex global supply networks which enables the network-wide alignment of competitive priorities. Conclusion: Supply-chain-based segmentation of global supply network structures can effectively reduce the complexity, firms face when aiming to strategically align their supply chains on a holistic level. The results of this research are applicable for certain types of global supply networks and can be used for network alignment and strategy development. The approach can furthermore generate insights useable for negotiation support with suppliers.

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

The International Trade Network: weighted network analysis and modelling

International Nuclear Information System (INIS)

Bhattacharya, K; Mukherjee, G; Manna, S S; Saramäki, J; Kaski, K

2008-01-01

Tools of the theory of critical phenomena, namely the scaling analysis and universality, are argued to be applicable to large complex web-like network structures. Using a detailed analysis of the real data of the International Trade Network we argue that the scaled link weight distribution has an approximate log-normal distribution which remains robust over a period of 53 years. Another universal feature is observed in the power-law growth of the trade strength with gross domestic product, the exponent being similar for all countries. Using the 'rich-club' coefficient measure of the weighted networks it has been shown that the size of the rich-club controlling half of the world's trade is actually shrinking. While the gravity law is known to describe well the social interactions in the static networks of population migration, international trade, etc, here for the first time we studied a non-conservative dynamical model based on the gravity law which excellently reproduced many empirical features of the ITN

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.

Complex Network Analysis of Guangzhou Metro

OpenAIRE

Yasir Tariq Mohmand; Fahad Mehmood; Fahd Amjad; Nedim Makarevic

2015-01-01

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...

Networks of reader and country status: an analysis of Mendeley reader statistics

Directory of Open Access Journals (Sweden)

Robin Haunschild

2015-11-01

Full Text Available The number of papers published in journals indexed by the Web of Science core collection is steadily increasing. In recent years, nearly two million new papers were published each year; somewhat more than one million papers when primary research papers are considered only (articles and reviews are the document types where primary research is usually reported or reviewed. However, who reads these papers? More precisely, which groups of researchers from which (self-assigned scientific disciplines and countries are reading these papers? Is it possible to visualize readership patterns for certain countries, scientific disciplines, or academic status groups? One popular method to answer these questions is a network analysis. In this study, we analyze Mendeley readership data of a set of 1,133,224 articles and 64,960 reviews with publication year 2012 to generate three different networks: (1 The network based on disciplinary affiliations of Mendeley readers contains four groups: (i biology, (ii social sciences and humanities (including relevant computer sciences, (iii bio-medical sciences, and (iv natural sciences and engineering. In all four groups, the category with the addition “miscellaneous” prevails. (2 The network of co-readers in terms of professional status shows that a common interest in papers is mainly shared among PhD students, Master’s students, and postdocs. (3 The country network focusses on global readership patterns: a group of 53 nations is identified as core to the scientific enterprise, including Russia and China as well as two thirds of the OECD (Organisation for Economic Co-operation and Development countries.

Sport psychology group consultation using social networking web sites.

Science.gov (United States)

Dietrich, Frederick; Shipherd, Amber M; Gershgoren, Lael; Filho, Edson Medeiros; Basevitch, Itay

2012-08-01

A social networking Web site, Facebook, was used to deliver long-term sport psychology consultation services to student-athletes (i.e., soccer players) in 30- to 60-min weekly sessions. Additional short-term team building, group cohesion, communication, anger management, injury rehabilitation, mental toughness, commitment, and leadership workshops were provided. Cohesion and overall relationships between both the student-athletes and the sport psychology consultants benefited from this process. Social networking Web sites offer a practical way of providing sport psychology consulting services that does not require use of major resources. (c) 2012 APA, all rights reserved.

Pareto distance for multi-layer network analysis

DEFF Research Database (Denmark)

Magnani, Matteo; Rossi, Luca

2013-01-01

services, e.g., Facebook, Twitter, LinkedIn and Foursquare. As a result, the analysis of on-line social networks requires a wider scope and, more technically speaking, models for the representation of this fragmented scenario. The recent introduction of more realistic layered models has however determined......Social Network Analysis has been historically applied to single networks, e.g., interaction networks between co-workers. However, the advent of on-line social network sites has emphasized the stratified structure of our social experience. Individuals usually spread their identities over multiple...

UMA/GAN network architecture analysis

Science.gov (United States)

Yang, Liang; Li, Wensheng; Deng, Chunjian; Lv, Yi

2009-07-01

This paper is to critically analyze the architecture of UMA which is one of Fix Mobile Convergence (FMC) solutions, and also included by the third generation partnership project(3GPP). In UMA/GAN network architecture, UMA Network Controller (UNC) is the key equipment which connects with cellular core network and mobile station (MS). UMA network could be easily integrated into the existing cellular networks without influencing mobile core network, and could provides high-quality mobile services with preferentially priced indoor voice and data usage. This helps to improve subscriber's experience. On the other hand, UMA/GAN architecture helps to integrate other radio technique into cellular network which includes WiFi, Bluetooth, and WiMax and so on. This offers the traditional mobile operators an opportunity to integrate WiMax technique into cellular network. In the end of this article, we also give an analysis of potential influence on the cellular core networks ,which is pulled by UMA network.

Agricultural science in the wild: a social network analysis of farmer knowledge exchange.

Science.gov (United States)

Wood, Brennon A; Blair, Hugh T; Gray, David I; Kemp, Peter D; Kenyon, Paul R; Morris, Steve T; Sewell, Alison M

2014-01-01

Responding to demands for transformed farming practices requires new forms of knowledge. Given their scale and complexity, agricultural problems can no longer be solved by linear transfers in which technology developed by specialists passes to farmers by way of extension intermediaries. Recent research on alternative approaches has focused on the innovation systems formed by interactions between heterogeneous actors. Rather than linear transfer, systems theory highlights network facilitation as a specialized function. This paper contributes to our understanding of such facilitation by investigating the networks in which farmers discuss science. We report findings based on the study of a pastoral farming experiment collaboratively undertaken by a group of 17 farmers and five scientists. Analysis of prior contact and alter sharing between the group's members indicates strongly tied and decentralized networks. Farmer knowledge exchanges about the experiment have been investigated using a mix of quantitative and qualitative methods. Network surveys identified who the farmers contacted for knowledge before the study began and who they had talked to about the experiment by 18 months later. Open-ended interviews collected farmer statements about their most valuable contacts and these statements have been thematically analysed. The network analysis shows that farmers talked about the experiment with 192 people, most of whom were fellow farmers. Farmers with densely tied and occupationally homogeneous contacts grew their networks more than did farmers with contacts that are loosely tied and diverse. Thematic analysis reveals three general principles: farmers value knowledge delivered by persons rather than roles, privilege farming experience, and develop knowledge with empiricist rather than rationalist techniques. Taken together, these findings suggest that farmers deliberate about science in intensive and durable networks that have significant implications for theorizing

Agricultural science in the wild: a social network analysis of farmer knowledge exchange.

Directory of Open Access Journals (Sweden)

Brennon A Wood

Full Text Available Responding to demands for transformed farming practices requires new forms of knowledge. Given their scale and complexity, agricultural problems can no longer be solved by linear transfers in which technology developed by specialists passes to farmers by way of extension intermediaries. Recent research on alternative approaches has focused on the innovation systems formed by interactions between heterogeneous actors. Rather than linear transfer, systems theory highlights network facilitation as a specialized function. This paper contributes to our understanding of such facilitation by investigating the networks in which farmers discuss science. We report findings based on the study of a pastoral farming experiment collaboratively undertaken by a group of 17 farmers and five scientists. Analysis of prior contact and alter sharing between the group's members indicates strongly tied and decentralized networks. Farmer knowledge exchanges about the experiment have been investigated using a mix of quantitative and qualitative methods. Network surveys identified who the farmers contacted for knowledge before the study began and who they had talked to about the experiment by 18 months later. Open-ended interviews collected farmer statements about their most valuable contacts and these statements have been thematically analysed. The network analysis shows that farmers talked about the experiment with 192 people, most of whom were fellow farmers. Farmers with densely tied and occupationally homogeneous contacts grew their networks more than did farmers with contacts that are loosely tied and diverse. Thematic analysis reveals three general principles: farmers value knowledge delivered by persons rather than roles, privilege farming experience, and develop knowledge with empiricist rather than rationalist techniques. Taken together, these findings suggest that farmers deliberate about science in intensive and durable networks that have significant

Historical Network Analysis of the Web

DEFF Research Database (Denmark)

Brügger, Niels

2013-01-01

This article discusses some of the fundamental methodological challenges related to doing historical network analyses of the web based on material in web archives. Since the late 1990s many countries have established extensive national web archives, and software supported network analysis...... 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...... revolve around the specific nature of archived web material. On the basis of an introduction to the processes involved in web archiving as well as of the characteristics of archived web material, the article outlines and scrutinizes some of the major challenges which may arise when doing network analysis...

Dynamic Network Logistic Regression: A Logistic Choice Analysis of Inter- and Intra-Group Blog Citation Dynamics in the 2004 US Presidential Election

OpenAIRE

Almquist, Zack W.; Butts, Carter T.

2013-01-01

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2...

Cooperative Networks: Altruism, Group Solidarity, Reciprocity, and Sanctioning in Ugandan Producer Organizations.

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Baldassarri, Delia

2015-09-01

Repeated interaction and social networks are commonly considered viable solutions to collective action problems. This article identifies and systematically measures four general mechanisms--that is, generalized altruism, group solidarity, reciprocity, and the threat of sanctioning--and tests which of them brings about cooperation in the context of Ugandan producer organizations. Using an innovative methodological framework that combines "lab-in-the-field" experiments with survey interviews and complete social networks data, the article goes beyond the assessment of a relationship between social networks and collective outcomes to study the mechanisms that favor cooperative behavior. The article first establishes a positive relationship between position in the network structure and propensity to cooperate in the producer organization and then uses farmers' behavior in dictator and public goods games to test different mechanisms that may account for such a relationship. Results show that cooperation is induced by patterns of reciprocity that emerge through repeated interaction rather than other-regarding preferences like altruism or group solidarity.

Optimised Design and Analysis of All-Optical Networks

DEFF Research Database (Denmark)

Glenstrup, Arne John

2002-01-01

through various experiments and is shown to produce good results and to be able to scale up to networks of realistic sizes. A novel method, subpath wavelength grouping, for routing connections in a multigranular all-optical network where several wavelengths can be grouped and switched at band and fibre......This PhD thesis presents a suite of methods for optimising design and for analysing blocking probabilities of all-optical networks. It thus contributes methodical knowledge to the field of computer assisted planning of optical networks. A two-stage greenfield optical network design optimiser...... is developed, based on shortest-path algorithms and a comparatively new metaheuristic called simulated allocation. It is able to handle design of all-optical mesh networks with optical cross-connects, considers duct as well as fibre and node costs, and can also design protected networks. The method is assessed...

High-Quality Ultra-Compact Grid Layout of Grouped Networks.

Science.gov (United States)

Yoghourdjian, Vahan; Dwyer, Tim; Gange, Graeme; Kieffer, Steve; Klein, Karsten; Marriott, Kim

2016-01-01

Prior research into network layout has focused on fast heuristic techniques for layout of large networks, or complex multi-stage pipelines for higher quality layout of small graphs. Improvements to these pipeline techniques, especially for orthogonal-style layout, are difficult and practical results have been slight in recent years. Yet, as discussed in this paper, there remain significant issues in the quality of the layouts produced by these techniques, even for quite small networks. This is especially true when layout with additional grouping constraints is required. The first contribution of this paper is to investigate an ultra-compact, grid-like network layout aesthetic that is motivated by the grid arrangements that are used almost universally by designers in typographical layout. Since the time when these heuristic and pipeline-based graph-layout methods were conceived, generic technologies (MIP, CP and SAT) for solving combinatorial and mixed-integer optimization problems have improved massively. The second contribution of this paper is to reassess whether these techniques can be used for high-quality layout of small graphs. While they are fast enough for graphs of up to 50 nodes we found these methods do not scale up. Our third contribution is a large-neighborhood search meta-heuristic approach that is scalable to larger networks.

Flory-Stockmayer analysis on reprocessable polymer networks

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Li, Lingqiao; Chen, Xi; Jin, Kailong; Torkelson, John

Reprocessable polymer networks can undergo structure rearrangement through dynamic chemistries under proper conditions, making them a promising candidate for recyclable crosslinked materials, e.g. tires. This research field has been focusing on various chemistries. However, there has been lacking of an essential physical theory explaining the relationship between abundancy of dynamic linkages and reprocessability. Based on the classical Flory-Stockmayer analysis on network gelation, we developed a similar analysis on reprocessable polymer networks to quantitatively predict the critical condition for reprocessability. Our theory indicates that it is unnecessary for all bonds to be dynamic to make the resulting network reprocessable. As long as there is no percolated permanent network in the system, the material can fully rearrange. To experimentally validate our theory, we used a thiol-epoxy network model system with various dynamic linkage compositions. The stress relaxation behavior of resulting materials supports our theoretical prediction: only 50 % of linkages between crosslinks need to be dynamic for a tri-arm network to be reprocessable. Therefore, this analysis provides the first fundamental theoretical platform for designing and evaluating reprocessable polymer networks. We thank McCormick Research Catalyst Award Fund and ISEN cluster fellowship (L. L.) for funding support.

Security Analysis of DTN Architecture and Bundle Protocol Specification for Space-Based Networks

Science.gov (United States)

Ivancic, William D.

2009-01-01

A Delay-Tolerant Network (DTN) Architecture (Request for Comment, RFC-4838) and Bundle Protocol Specification, RFC-5050, have been proposed for space and terrestrial networks. Additional security specifications have been provided via the Bundle Security Specification (currently a work in progress as an Internet Research Task Force internet-draft) and, for link-layer protocols applicable to Space networks, the Licklider Transport Protocol Security Extensions. This document provides a security analysis of the current DTN RFCs and proposed security related internet drafts with a focus on space-based communication networks, which is a rather restricted subset of DTN networks. Note, the original focus and motivation of DTN work was for the Interplanetary Internet . This document does not address general store-and-forward network overlays, just the current work being done by the Internet Research Task Force (IRTF) and the Consultative Committee for Space Data Systems (CCSDS) Space Internetworking Services Area (SIS) - DTN working group under the DTN and Bundle umbrellas. However, much of the analysis is relevant to general store-and-forward overlays.

Interaction, Critical Thinking, and Social Network Analysis (SNA in Online Courses

Directory of Open Access Journals (Sweden)

Joan Thormann

2013-07-01

Full Text Available This study tried to ascertain a possible relationship between the number of student moderators (1, 2, and 3, online interactions, and critical thinking of K-12 educators enrolled in an online course that was taught from a constructivist approach. The course topic was use of technology in special education. Social network analysis (SNA and measures of critical thinking (Newman, Webb, & Cochrane, 1995 were used to research and assess if there was a difference in interaction and critical thinking between 1, 2, or 3 student moderators who facilitated a forum discussion of an assignment in an online course. The same course was repeated over three years. Each year either 1, 2, or 3 students moderated. The analysis indicated more discussion per non-moderating student with the three student moderated group. Using SNA we found that there was only one noticeable difference among the three groups which was in the value of network centralization. Using critical thinking measures the three student moderator group scored higher in five of the eight critical thinking categories. Variations in instructor presence in the online courses may have influenced these findings.

Relative stability of core groups in pollination networks in a biodiversity hotspot over four years.

Science.gov (United States)

Fang, Qiang; Huang, Shuang-Quan

2012-01-01

Plants and their pollinators form pollination networks integral to the evolution and persistence of species in communities. Previous studies suggest that pollination network structure remains nested while network composition is highly dynamic. However, little is known about temporal variation in the structure and function of plant-pollinator networks, especially in species-rich communities where the strength of pollinator competition is predicted to be high. Here we quantify temporal variation of pollination networks over four consecutive years in an alpine meadow in the Hengduan Mountains biodiversity hotspot in China. We found that ranked positions and idiosyncratic temperatures of both plants and pollinators were more conservative between consecutive years than in non-consecutive years. Although network compositions exhibited high turnover, generalized core groups--decomposed by a k-core algorithm--were much more stable than peripheral groups. Given the high rate of turnover observed, we suggest that identical plants and pollinators that persist for at least two successive years sustain pollination services at the community level. Our data do not support theoretical predictions of a high proportion of specialized links within species-rich communities. Plants were relatively specialized, exhibiting less variability in pollinator composition at pollinator functional group level than at the species level. Both specialized and generalized plants experienced narrow variation in functional pollinator groups. The dynamic nature of pollination networks in the alpine meadow demonstrates the potential for networks to mitigate the effects of fluctuations in species composition in a high biodiversity area.

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

Validation of network communicability metrics for the analysis of brain structural networks.

Directory of Open Access Journals (Sweden)

Jennifer Andreotti

Full Text Available Computational network analysis provides new methods to analyze the brain's structural organization based on diffusion imaging tractography data. Networks are characterized by global and local metrics that have recently given promising insights into diagnosis and the further understanding of psychiatric and neurologic disorders. Most of these metrics are based on the idea that information in a network flows along the shortest paths. In contrast to this notion, communicability is a broader measure of connectivity which assumes that information could flow along all possible paths between two nodes. In our work, the features of network metrics related to communicability were explored for the first time in the healthy structural brain network. In addition, the sensitivity of such metrics was analysed using simulated lesions to specific nodes and network connections. Results showed advantages of communicability over conventional metrics in detecting densely connected nodes as well as subsets of nodes vulnerable to lesions. In addition, communicability centrality was shown to be widely affected by the lesions and the changes were negatively correlated with the distance from lesion site. In summary, our analysis suggests that communicability metrics that may provide an insight into the integrative properties of the structural brain network and that these metrics may be useful for the analysis of brain networks in the presence of lesions. Nevertheless, the interpretation of communicability is not straightforward; hence these metrics should be used as a supplement to the more standard connectivity network metrics.

Network Expansion and Pathway Enrichment Analysis towards Biologically Significant Findings from Microarrays

Directory of Open Access Journals (Sweden)

Wu Xiaogang

2012-06-01

Full Text Available In many cases, crucial genes show relatively slight changes between groups of samples (e.g. normal vs. disease, and many genes selected from microarray differential analysis by measuring the expression level statistically are also poorly annotated and lack of biological significance. In this paper, we present an innovative approach - network expansion and pathway enrichment analysis (NEPEA for integrative microarray analysis. We assume that organized knowledge will help microarray data analysis in significant ways, and the organized knowledge could be represented as molecular interaction networks or biological pathways. Based on this hypothesis, we develop the NEPEA framework based on network expansion from the human annotated and predicted protein interaction (HAPPI database, and pathway enrichment from the human pathway database (HPD. We use a recently-published microarray dataset (GSE24215 related to insulin resistance and type 2 diabetes (T2D as case study, since this study provided a thorough experimental validation for both genes and pathways identified computationally from classical microarray analysis and pathway analysis. We perform our NEPEA analysis for this dataset based on the results from the classical microarray analysis to identify biologically significant genes and pathways. Our findings are not only consistent with the original findings mostly, but also obtained more supports from other literatures.

Core psychopathology in anorexia nervosa and bulimia nervosa: A network analysis.

Science.gov (United States)

Forrest, Lauren N; Jones, Payton J; Ortiz, Shelby N; Smith, April R

2018-04-25

The cognitive-behavioral theory of eating disorders (EDs) proposes that shape and weight overvaluation are the core ED psychopathology. Core symptoms can be statistically identified using network analysis. Existing ED network studies support that shape and weight overvaluation are the core ED psychopathology, yet no studies have estimated AN core psychopathology and concerns exist about the replicability of network analysis findings. The current study estimated ED symptom networks among people with anorexia nervosa (AN) and bulimia nervosa (BN) and among a combined group of people with AN and BN. Participants were girls and women with AN (n = 604) and BN (n = 477) seeking residential ED treatment. ED symptoms were assessed with the Eating Disorder Examination-Questionnaire (EDE-Q); 27 of the EDE-Q items were included as nodes in symptom networks. Core symptoms were determined by expected influence and strength values. In all networks, desiring weight loss, restraint, shape and weight preoccupation, and shape overvaluation emerged as the most important symptoms. In addition, in the AN and combined networks, fearing weight gain emerged as an important symptom. In the BN network, weight overvaluation emerged as another important symptom. Findings support the cognitive-behavioral premise that shape and weight overvaluation are at the core of AN psychopathology. Our BN and combined network findings provide a high degree of replication of previous findings. Clinically, findings highlight the importance of considering shape and weight overvaluation as a severity specifier and primary treatment target for people with EDs. © 2018 Wiley Periodicals, Inc.

Evolution of quantum and classical strategies on networks by group interactions

International Nuclear Information System (INIS)

Li Qiang; Chen Minyou; Iqbal, Azhar; Abbott, Derek

2012-01-01

In this paper, quantum strategies are introduced within evolutionary games in order to investigate the evolution of quantum and classical strategies on networks in the public goods game. Comparing the results of evolution on a scale-free network and a square lattice, we find that a quantum strategy outperforms the classical strategies, regardless of the network. Moreover, a quantum strategy dominates the population earlier in group interactions than it does in pairwise interactions. In particular, if the hub node in a scale-free network is occupied by a cooperator initially, the strategy of cooperation will prevail in the population. However, in other situations, a quantum strategy can defeat the classical ones and finally becomes the dominant strategy in the population. (paper)

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.

PyPathway: Python Package for Biological Network Analysis and Visualization.

Science.gov (United States)

Xu, Yang; Luo, Xiao-Chun

2018-05-01

Life science studies represent one of the biggest generators of large data sets, mainly because of rapid sequencing technological advances. Biological networks including interactive networks and human curated pathways are essential to understand these high-throughput data sets. Biological network analysis offers a method to explore systematically not only the molecular complexity of a particular disease but also the molecular relationships among apparently distinct phenotypes. Currently, several packages for Python community have been developed, such as BioPython and Goatools. However, tools to perform comprehensive network analysis and visualization are still needed. Here, we have developed PyPathway, an extensible free and open source Python package for functional enrichment analysis, network modeling, and network visualization. The network process module supports various interaction network and pathway databases such as Reactome, WikiPathway, STRING, and BioGRID. The network analysis module implements overrepresentation analysis, gene set enrichment analysis, network-based enrichment, and de novo network modeling. Finally, the visualization and data publishing modules enable users to share their analysis by using an easy web application. For package availability, see the first Reference.

BLIG: A New Approach for Sensor Identification, Grouping,and Authorisation in Body Sensor Networks

DEFF Research Database (Denmark)

Andersen, Jacob; Bardram, Jakob Eyvind

2007-01-01

Using body sensor networks (BSN) in critical clinical settings like emergency units in hospitals or in accidents requires that such a network can be deployed, configured, and started in a fast and easy way, while maintaining trust in the network. In this paper we present a novel approach called...... BLIG (Blinking Led Indicated Grouping) for easy deployment of BSNs on patients in critical situations, including mechanisms for uniquely identifying and grouping sensor nodes belonging to a patient in a secure and trusted way. This approach has been designed in close cooperation with users, and easy...

Group Centric Networking: Addressing Information Sharing Requirements at the Tactical Edge

Science.gov (United States)

2016-04-10

Group Centric Networking: Addressing Information Sharing Requirements at the Tactical Edge Bow-Nan Cheng, Greg Kuperman, Patricia Deutsch, Logan...been a large push in the U.S. Department of Defense to move to an all Internet Protocol (IP) infrastructure, particularly on the tactical edge . IP and...lossy links, and scaling to large numbers of users. Unfortunately, these are the exact conditions military tactical edge networks must operate within

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.

Potential benefits and harms of a peer support social network service on the internet for people with depressive tendencies: qualitative content analysis and social network analysis.

Science.gov (United States)

Takahashi, Yoshimitsu; Uchida, Chiyoko; Miyaki, Koichi; Sakai, Michi; Shimbo, Takuro; Nakayama, Takeo

2009-07-23

Internet peer support groups for depression are becoming popular and could be affected by an increasing number of social network services (SNSs). However, little is known about participant characteristics, social relationships in SNSs, and the reasons for usage. In addition, the effects of SNS participation on people with depression are rather unknown. The aim was to explore the potential benefits and harms of an SNS for depression based on a concurrent triangulation design of mixed methods strategy, including qualitative content analysis and social network analysis. A cross-sectional Internet survey of participants, which involved the collection of SNS log files and a questionnaire, was conducted in an SNS for people with self-reported depressive tendencies in Japan in 2007. Quantitative data, which included user demographics, depressive state, and assessment of the SNS (positive vs not positive), were statistically analyzed. Descriptive contents of responses to open-ended questions concerning advantages and disadvantages of SNS participation were analyzed using the inductive approach of qualitative content analysis. Contents were organized into codes, concepts, categories, and a storyline based on the grounded theory approach. Social relationships, derived from data of "friends," were analyzed using social network analysis, in which network measures and the extent of interpersonal association were calculated based on the social network theory. Each analysis and integration of results were performed through a concurrent triangulation design of mixed methods strategy. There were 105 participants. Median age was 36 years, and 51% (36/71) were male. There were 37 valid respondents; their number of friends and frequency of accessing the SNS were significantly higher than for invalid/nonrespondents (P = .008 and P = .003). Among respondents, 90% (28/31) were mildly, moderately, or severely depressed. Assessment of the SNS was performed by determining the access

Potential Benefits and Harms of a Peer Support Social Network Service on the Internet for People With Depressive Tendencies: Qualitative Content Analysis and Social Network Analysis

Science.gov (United States)

Takahashi, Yoshimitsu; Uchida, Chiyoko; Miyaki, Koichi; Sakai, Michi; Shimbo, Takuro

2009-01-01

Background Internet peer support groups for depression are becoming popular and could be affected by an increasing number of social network services (SNSs). However, little is known about participant characteristics, social relationships in SNSs, and the reasons for usage. In addition, the effects of SNS participation on people with depression are rather unknown. Objective The aim was to explore the potential benefits and harms of an SNS for depression based on a concurrent triangulation design of mixed methods strategy, including qualitative content analysis and social network analysis. Methods A cross-sectional Internet survey of participants, which involved the collection of SNS log files and a questionnaire, was conducted in an SNS for people with self-reported depressive tendencies in Japan in 2007. Quantitative data, which included user demographics, depressive state, and assessment of the SNS (positive vs not positive), were statistically analyzed. Descriptive contents of responses to open-ended questions concerning advantages and disadvantages of SNS participation were analyzed using the inductive approach of qualitative content analysis. Contents were organized into codes, concepts, categories, and a storyline based on the grounded theory approach. Social relationships, derived from data of “friends,” were analyzed using social network analysis, in which network measures and the extent of interpersonal association were calculated based on the social network theory. Each analysis and integration of results were performed through a concurrent triangulation design of mixed methods strategy. Results There were 105 participants. Median age was 36 years, and 51% (36/71) were male. There were 37 valid respondents; their number of friends and frequency of accessing the SNS were significantly higher than for invalid/nonrespondents (P = .008 and P = .003). Among respondents, 90% (28/31) were mildly, moderately, or severely depressed. Assessment of the SNS was

Network Analysis in Community Psychology: Looking Back, Looking Forward.

Science.gov (United States)

Neal, Zachary P; Neal, Jennifer Watling

2017-09-01

Network analysis holds promise for community psychology given the field's aim to understand the interplay between individuals and their social contexts. Indeed, because network analysis focuses explicitly on patterns of relationships between actors, its theories and methods are inherently extra-individual in nature and particularly well suited to characterizing social contexts. But, to what extent has community psychology taken advantage of this network analysis as a tool for capturing context? To answer these questions, this study provides a review of the use network analysis in articles published in American Journal of Community Psychology. Looking back, we describe and summarize the ways that network analysis has been employed in community psychology research to understand the range of ways community psychologists have found the technique helpful. Looking forward and paying particular attention to analytic issues identified in past applications, we provide some recommendations drawn from the network analysis literature to facilitate future applications of network analysis in community psychology. © 2017 The Authors. American Journal of Community Psychology published by Wiley Periodicals, Inc. on behalf of Society for Community Research and Action.

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.

Social network analysis: Presenting an underused method for nursing research.

Science.gov (United States)

Parnell, James Michael; Robinson, Jennifer C

2018-06-01

This paper introduces social network analysis as a versatile method with many applications in nursing research. Social networks have been studied for years in many social science fields. The methods continue to advance but remain unknown to most nursing scholars. Discussion paper. English language and interpreted literature was searched from Ovid Healthstar, CINAHL, PubMed Central, Scopus and hard copy texts from 1965 - 2017. Social network analysis first emerged in nursing literature in 1995 and appears minimally through present day. To convey the versatility and applicability of social network analysis in nursing, hypothetical scenarios are presented. The scenarios are illustrative of three approaches to social network analysis and include key elements of social network research design. The methods of social network analysis are underused in nursing research, primarily because they are unknown to most scholars. However, there is methodological flexibility and epistemological versatility capable of supporting quantitative and qualitative research. The analytic techniques of social network analysis can add new insight into many areas of nursing inquiry, especially those influenced by cultural norms. Furthermore, visualization techniques associated with social network analysis can be used to generate new hypotheses. Social network analysis can potentially uncover findings not accessible through methods commonly used in nursing research. Social networks can be analysed based on individual-level attributes, whole networks and subgroups within networks. Computations derived from social network analysis may stand alone to answer a research question or incorporated as variables into robust statistical models. © 2018 John Wiley & Sons Ltd.

A flood-based information flow analysis and network minimization method for gene regulatory networks.

Science.gov (United States)

Pavlogiannis, Andreas; Mozhayskiy, Vadim; Tagkopoulos, Ilias

2013-04-24

Biological networks tend to have high interconnectivity, complex topologies and multiple types of interactions. This renders difficult the identification of sub-networks that are involved in condition- specific responses. In addition, we generally lack scalable methods that can reveal the information flow in gene regulatory and biochemical pathways. Doing so will help us to identify key participants and paths under specific environmental and cellular context. This paper introduces the theory of network flooding, which aims to address the problem of network minimization and regulatory information flow in gene regulatory networks. Given a regulatory biological network, a set of source (input) nodes and optionally a set of sink (output) nodes, our task is to find (a) the minimal sub-network that encodes the regulatory program involving all input and output nodes and (b) the information flow from the source to the sink nodes of the network. Here, we describe a novel, scalable, network traversal algorithm and we assess its potential to achieve significant network size reduction in both synthetic and E. coli networks. Scalability and sensitivity analysis show that the proposed method scales well with the size of the network, and is robust to noise and missing data. The method of network flooding proves to be a useful, practical approach towards information flow analysis in gene regulatory networks. Further extension of the proposed theory has the potential to lead in a unifying framework for the simultaneous network minimization and information flow analysis across various "omics" levels.

Bayesian network analysis reveals alterations to default mode network connectivity in individuals at risk for Alzheimer's disease.

Science.gov (United States)

Li, Rui; Yu, Jing; Zhang, Shouzi; Bao, Feng; Wang, Pengyun; Huang, Xin; Li, Juan

2013-01-01

Alzheimer's disease (AD) is associated with abnormal functioning of the default mode network (DMN). Functional connectivity (FC) changes to the DMN have been found in patients with amnestic mild cognitive impairment (aMCI), which is the prodromal stage of AD. However, whether or not aMCI also alters the effective connectivity (EC) of the DMN remains unknown. We employed a combined group independent component analysis (ICA) and Bayesian network (BN) learning approach to resting-state functional MRI (fMRI) data from 17 aMCI patients and 17 controls, in order to establish the EC pattern of DMN, and to evaluate changes occurring in aMCI. BN analysis demonstrated heterogeneous regional convergence degree across DMN regions, which were organized into two closely interacting subsystems. Compared to controls, the aMCI group showed altered directed connectivity weights between DMN regions in the fronto-parietal, temporo-frontal, and temporo-parietal pathways. The aMCI group also exhibited altered regional convergence degree in the right inferior parietal lobule. Moreover, we found EC changes in DMN regions in aMCI were correlated with regional FC levels, and the connectivity metrics were associated with patients' cognitive performance. This study provides novel sights into our understanding of the functional architecture of the DMN and adds to a growing body of work demonstrating the importance of the DMN as a mechanism of aMCI.

NAPS: Network Analysis of Protein Structures

Science.gov (United States)

Chakrabarty, Broto; Parekh, Nita

2016-01-01

Traditionally, protein structures have been analysed by the secondary structure architecture and fold arrangement. An alternative approach that has shown promise is modelling proteins as a network of non-covalent interactions between amino acid residues. The network representation of proteins provide a systems approach to topological analysis of complex three-dimensional structures irrespective of secondary structure and fold type and provide insights into structure-function relationship. We have developed a web server for network based analysis of protein structures, NAPS, that facilitates quantitative and qualitative (visual) analysis of residue–residue interactions in: single chains, protein complex, modelled protein structures and trajectories (e.g. from molecular dynamics simulations). The user can specify atom type for network construction, distance range (in Å) and minimal amino acid separation along the sequence. NAPS provides users selection of node(s) and its neighbourhood based on centrality measures, physicochemical properties of amino acids or cluster of well-connected residues (k-cliques) for further analysis. Visual analysis of interacting domains and protein chains, and shortest path lengths between pair of residues are additional features that aid in functional analysis. NAPS support various analyses and visualization views for identifying functional residues, provide insight into mechanisms of protein folding, domain-domain and protein–protein interactions for understanding communication within and between proteins. URL:http://bioinf.iiit.ac.in/NAPS/. PMID:27151201

Fibrin network pattern changes of platelet-rich fibrin in young versus old age group of individuals: A cell block cytology study

OpenAIRE

Shravanthi Raghav Yajamanya; Anirban Chatterjee; Chaitanya Nischay Babu; Deepika Karunanithi

2016-01-01

Background: To evaluate variations in fibrin network patterns of the platelet-rich fibrin (PRF) in different age groups. Materials and Methods: Ninety-five patients were divided into three age groups: Group 1: (20?39 years); Group 2: (40?59 years); and Group 3: (60 years and above). PRF was prepared from blood samples of all patients and were subjected to cell block cytology method of histological analysis and slides were prepared to histologically assess the age-related changes in (i) fibrin...

NET-2 Network Analysis Program

International Nuclear Information System (INIS)

Malmberg, A.F.

1974-01-01

The NET-2 Network Analysis Program is a general purpose digital computer program which solves the nonlinear time domain response and the linearized small signal frequency domain response of an arbitrary network of interconnected components. NET-2 is capable of handling a variety of components and has been applied to problems in several engineering fields, including electronic circuit design and analysis, missile flight simulation, control systems, heat flow, fluid flow, mechanical systems, structural dynamics, digital logic, communications network design, solid state device physics, fluidic systems, and nuclear vulnerability due to blast, thermal, gamma radiation, neutron damage, and EMP effects. Network components may be selected from a repertoire of built-in models or they may be constructed by the user through appropriate combinations of mathematical, empirical, and topological functions. Higher-level components may be defined by subnetworks composed of any combination of user-defined components and built-in models. The program provides a modeling capability to represent and intermix system components on many levels, e.g., from hole and electron spatial charge distributions in solid state devices through discrete and integrated electronic components to functional system blocks. NET-2 is capable of simultaneous computation in both the time and frequency domain, and has statistical and optimization capability. Network topology may be controlled as a function of the network solution. (U.S.)

A multi-methodological MR resting state network analysis to assess the changes in brain physiology of children with ADHD.

Directory of Open Access Journals (Sweden)

Benito de Celis Alonso

Full Text Available The purpose of this work was to highlight the neurological differences between the MR resting state networks of a group of children with ADHD (pre-treatment and an age-matched healthy group. Results were obtained using different image analysis techniques. A sample of n = 46 children with ages between 6 and 12 years were included in this study (23 per cohort. Resting state image analysis was performed using ReHo, ALFF and ICA techniques. ReHo and ICA represent connectivity analyses calculated with different mathematical approaches. ALFF represents an indirect measurement of brain activity. The ReHo and ICA analyses suggested differences between the two groups, while the ALFF analysis did not. The ReHo and ALFF analyses presented differences with respect to the results previously reported in the literature. ICA analysis showed that the same resting state networks that appear in healthy volunteers of adult age were obtained for both groups. In contrast, these networks were not identical when comparing the healthy and ADHD groups. These differences affected areas for all the networks except the Right Memory Function network. All techniques employed in this study were used to monitor different cerebral regions which participate in the phenomenological characterization of ADHD patients when compared to healthy controls. Results from our three analyses indicated that the cerebellum and mid-frontal lobe bilaterally for ReHo, the executive function regions in ICA, and the precuneus, cuneus and the clacarine fissure for ALFF, were the "hubs" in which the main inter-group differences were found. These results do not just help to explain the physiology underlying the disorder but open the door to future uses of these methodologies to monitor and evaluate patients with ADHD.

Artificial neural networks for plasma spectroscopy analysis

International Nuclear Information System (INIS)

Morgan, W.L.; Larsen, J.T.; Goldstein, W.H.

1992-01-01

Artificial neural networks have been applied to a variety of signal processing and image recognition problems. Of the several common neural models the feed-forward, back-propagation network is well suited for the analysis of scientific laboratory data, which can be viewed as a pattern recognition problem. The authors present a discussion of the basic neural network concepts and illustrate its potential for analysis of experiments by applying it to the spectra of laser produced plasmas in order to obtain estimates of electron temperatures and densities. Although these are high temperature and density plasmas, the neural network technique may be of interest in the analysis of the low temperature and density plasmas characteristic of experiments and devices in gaseous electronics

The QAP weighted network analysis method and its application in international services trade

Science.gov (United States)

Xu, Helian; Cheng, Long

2016-04-01

Based on QAP (Quadratic Assignment Procedure) correlation and complex network theory, this paper puts forward a new method named QAP Weighted Network Analysis Method. The core idea of the method is to analyze influences among relations in a social or economic group by building a QAP weighted network of networks of relations. In the QAP weighted network, a node depicts a relation and an undirect edge exists between any pair of nodes if there is significant correlation between relations. As an application of the QAP weighted network, we study international services trade by using the QAP weighted network, in which nodes depict 10 kinds of services trade relations. After the analysis of international services trade by QAP weighted network, and by using distance indicators, hierarchy tree and minimum spanning tree, the conclusion shows that: Firstly, significant correlation exists in all services trade, and the development of any one service trade will stimulate the other nine. Secondly, as the economic globalization goes deeper, correlations in all services trade have been strengthened continually, and clustering effects exist in those services trade. Thirdly, transportation services trade, computer and information services trade and communication services trade have the most influence and are at the core in all services trade.

The Analysis of User Behaviour of a Network Management Training Tool using a Neural Network

Directory of Open Access Journals (Sweden)

Helen Donelan

2005-10-01

Full Text Available A novel method for the analysis and interpretation of data that describes the interaction between trainee network managers and a network management training tool is presented. A simulation based approach is currently being used to train network managers, through the use of a simulated network. The motivation is to provide a tool for exposing trainees to a life like situation without disrupting a live network. The data logged by this system describes the detailed interaction between trainee network manager and simulated network. The work presented here provides an analysis of this interaction data that enables an assessment of the capabilities of the trainee network manager as well as an understanding of how the network management tasks are being approached. A neural network architecture is implemented in order to perform an exploratory data analysis of the interaction data. The neural network employs a novel form of continuous self-organisation to discover key features in the data and thus provide new insights into the learning and teaching strategies employed.

Classification of ion mobility spectra by functional groups using neural networks

Science.gov (United States)

Bell, S.; Nazarov, E.; Wang, Y. F.; Eiceman, G. A.

1999-01-01

Neural networks were trained using whole ion mobility spectra from a standardized database of 3137 spectra for 204 chemicals at various concentrations. Performance of the network was measured by the success of classification into ten chemical classes. Eleven stages for evaluation of spectra and of spectral pre-processing were employed and minimums established for response thresholds and spectral purity. After optimization of the database, network, and pre-processing routines, the fraction of successful classifications by functional group was 0.91 throughout a range of concentrations. Network classification relied on a combination of features, including drift times, number of peaks, relative intensities, and other factors apparently including peak shape. The network was opportunistic, exploiting different features within different chemical classes. Application of neural networks in a two-tier design where chemicals were first identified by class and then individually eliminated all but one false positive out of 161 test spectra. These findings establish that ion mobility spectra, even with low resolution instrumentation, contain sufficient detail to permit the development of automated identification systems.

Nourishing networks: A social-ecological analysis of a network intervention for improving household nutrition in Western Kenya.

Science.gov (United States)

DeLorme, Autumn L; Gavenus, Erika R; Salmen, Charles R; Benard, Gor Ouma; Mattah, Brian; Bukusi, Elizabeth; Fiorella, Kathryn J

2018-01-01

A growing body of research emphasizes the need to engage social networks in maternal and child nutrition interventions. However, an understanding of how interventions functionally engage not only mothers but fathers, grandparents, friends, and other social network members remains limited. This study uses an adaptation of a social-ecological model to analyze the multiple levels at which the Kanyakla Nutrition Program operates to change behavior. This study analyzes focus group data (four groups; n = 35, 7 men and 28 women) following the implementation of the Kanyakla Nutrition Program, a novel nutrition intervention engaging social networks to increase nutrition knowledge, shift perceptions, and promote positive practices for infant and young child feeding and community nutrition in general. Participant perspectives indicate that the Kanyakla Nutrition Program contributed to nutrition knowledge and confidence, changed perceptions, and supported infant and child feeding practices at the individual, interpersonal, and institutional levels. However, many respondents report challenges in transcending barriers at the broader community and systems levels of influence, where environmental and economic constraints continue to affect food access. Analysis of the Kanyakla Nutrition Program suggests that for interventions addressing household level determinants of nutrition, simultaneously engaging the household's network of interpersonal and community relationships can play a role in building momentum and consensus to address persistent structural barriers to improved nutrition. Copyright © 2017 Elsevier Ltd. All rights reserved.

Social grooming network in captive chimpanzees: does the wild or captive origin of group members affect sociality?

Science.gov (United States)

Levé, Marine; Sueur, Cédric; Petit, Odile; Matsuzawa, Tetsuro; Hirata, Satoshi

2016-01-01

Many chimpanzees throughout the world are housed in captivity, and there is an increasing effort to recreate social groups by mixing individuals with captive origins with those with wild origins. Captive origins may entail restricted rearing conditions during early infant life, including, for example, no maternal rearing and a limited social life. Early rearing conditions have been linked with differences in tool-use behavior between captive- and wild-born chimpanzees. If physical cognition can be impaired by non-natural rearing, what might be the consequences for social capacities? This study describes the results of network analysis based on grooming interactions in chimpanzees with wild and captive origins living in the Kumamoto Sanctuary in Kumamoto, Japan. Grooming is a complex social activity occupying up to 25% of chimpanzees' waking hours and plays a role in the emergence and maintenance of social relationships. We assessed whether the social centralities and roles of chimpanzees might be affected by their origin (captive vs wild). We found that captive- and wild-origin chimpanzees did not differ in their grooming behavior, but that theoretical removal of individuals from the network had differing impacts depending on the origin of the individual. Contrary to findings that non-natural early rearing has long-term effects on physical cognition, living in social groups seems to compensate for the negative effects of non-natural early rearing. Social network analysis (SNA) and, in particular, theoretical removal analysis, were able to highlight differences between individuals that would have been impossible to show using classical methods. The social environment of captive animals is important to their well-being, and we are only beginning to understand how SNA might help to enhance animal welfare.

AIDS Clinical Trials Group Network

Science.gov (United States)

... Bylaws, SOPs, and Guidelines Leadership and Operations Center Network Coordinating Center Statistical and Data Management Center Performance ... Accessibility Our Mission The mission of the ACTG Network is to cure HIV infection and reduce the ...

The Network Protocol Analysis Technique in Snort

Science.gov (United States)

Wu, Qing-Xiu

Network protocol analysis is a network sniffer to capture data for further analysis and understanding of the technical means necessary packets. Network sniffing is intercepted by packet assembly binary format of the original message content. In order to obtain the information contained. Required based on TCP / IP protocol stack protocol specification. Again to restore the data packets at protocol format and content in each protocol layer. Actual data transferred, as well as the application tier.

An examination of the relationship between athlete leadership and cohesion using social network analysis.

Science.gov (United States)

Loughead, Todd M; Fransen, Katrien; Van Puyenbroeck, Stef; Hoffmann, Matt D; De Cuyper, Bert; Vanbeselaere, Norbert; Boen, Filip

2016-11-01

Two studies investigated the structure of different athlete leadership networks and its relationship to cohesion using social network analysis. In Study 1, we examined the relationship between a general leadership quality network and task and social cohesion as measured by the Group Environment Questionnaire (GEQ). In Study 2, we investigated the leadership networks for four different athlete leadership roles (task, motivational, social and external) and their association with task and social cohesion networks. In Study 1, the results demonstrated that the general leadership quality network was positively related to task and social cohesion. The results from Study 2 indicated positive correlations between the four leadership networks and task and social cohesion networks. Further, the motivational leadership network emerged as the strongest predictor of the task cohesion network, while the social leadership network was the strongest predictor of the social cohesion network. The results complement a growing body of research indicating that athlete leadership has a positive association with cohesion.

Network Approach to Autistic Traits : Group and Subgroup Analyses of ADOS Item Scores

NARCIS (Netherlands)

Anderson, George M.; Montazeri, Farhad; de Bildt, Annelies

2015-01-01

A network conceptualization might contribute to understanding the occurrence and interacting nature of behavioral traits in the autism realm. Networks were constructed based on correlations of item scores of the Autism Diagnostic Observation Schedule for Modules 1, 2 and 3 obtained for a group of

Enabling dynamic network analysis through visualization in TVNViewer

Directory of Open Access Journals (Sweden)

Curtis Ross E

2012-08-01

Full Text Available Abstract Background Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer, a visualization tool for dynamic network analysis. Results In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. Conclusions TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space.

Enabling dynamic network analysis through visualization in TVNViewer

Science.gov (United States)

2012-01-01

Background Many biological processes are context-dependent or temporally specific. As a result, relationships between molecular constituents evolve across time and environments. While cutting-edge machine learning techniques can recover these networks, exploring and interpreting the rewiring behavior is challenging. Information visualization shines in this type of exploratory analysis, motivating the development ofTVNViewer (http://sailing.cs.cmu.edu/tvnviewer), a visualization tool for dynamic network analysis. Results In this paper, we demonstrate visualization techniques for dynamic network analysis by using TVNViewer to analyze yeast cell cycle and breast cancer progression datasets. Conclusions TVNViewer is a powerful new visualization tool for the analysis of biological networks that change across time or space. PMID:22897913

ANALISIS PEMANFAATAN JEJARING SOSIAL UNTUK PENENTUAN KONSENTRASI MAHASISWA DENGAN METODE SOCIAL NETWORK ANALYSIS (Studi Kasus: Universitas Nusantara PGRI Kediri

Directory of Open Access Journals (Sweden)

Risky Aswi R

2015-08-01

Full Text Available Analisis pemanfaatan  jejaring  sosial untuk penentuan konsentrasi mahasiswa dengan metode sosial network analysis. Tujuan dibangun sistem ini untuk menentukan konsentrasi mahasiswa dengan pendekatan Sosial Network Analisis Studi Kasus Jejaring Sosial Facebook dan mampu memanfaatkan facebook sebagai alat untuk menentukan konsentrasi mahasiswa jurusan teknik informatika menjadi tiga bagian yaitu pemrograman, jaringan, dan multimedia. Batasan yang digunakan dalam penelitian ini adalah penelitian ini hanya berlaku pada mahasiswa yang memiliki akun facebook. Penelitian ini menggunakan Social Network Analysis untuk mencari between centrality.Pada tahap pertama setiap akun akan dilihat relasinya setelah itu hubungan antar akun akan dimasukan ke node XL dan akan menghasilkan between centrality. Mahasiswa yang melanjutkan proses selanjutnya adalah mahasiswa yang nilai betweent centralitynya 85% teratas. Setelah itu akan ditambahkan variabel group yang digunakan untuk mengkelompokan peminatan, dan ditambahkan variabel nilai akademis untuk menguatkan pendapat yang diperoleh sebelumnya.Dengan cara melihat between centrality dan menambahkan variabel group untuk membagi siswa sesuai konsentrasi, dan memabahkan variabel nilai akademik untuk memperkuat pendapat. Sosial network analysis terbukti dapat menentukan konsentrasi mahaiswa. Facebook dapat digunakan sebagai alat untuk menentukan konsentrasi mahasiswa, konsentrasi mahasiswa dibagi menjadi pemrograman, jaringan, dan multimedia. Kata Kunci : Jejaring Sosial, Social Network Analysis, between centrality

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.

Visualization and Analysis of Complex Covert Networks

DEFF Research Database (Denmark)

Memon, Bisharat

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......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......-users (intelligence analysts) in harvesting, filtering, storing, managing, structuring, mining, analyzing, interpreting, and visualizing data about offensive networks. The methods and tools proposed and discussed in this work can also be applied to analysis of more generic complex networks....

Graph Theoretical Analysis of Developmental Patterns of the White Matter Network

Directory of Open Access Journals (Sweden)

Zhang eChen

2013-11-01

Full Text Available Understanding the development of human brain organization is critical for gaining insight into how the enhancement of cognitive processes is related to the fine-tuning of the brain network. However, the developmental trajectory of the large-scale white matter (WM network is not fully understood. Here, using graph theory, we examine developmental changes in the organization of WM networks in 180 typically-developing participants. WM networks were constructed using whole brain tractography and 78 cortical regions of interest were extracted from each participant. The subjects were first divided into 5 equal sample size (n=36 groups (early childhood: 6.0-9.7 years; late childhood: 9.8-12.7 years; adolescence: 12.9-17.5 years; young adult: 17.6-21.8 years; adult: 21.9-29.6 years. Most prominent changes in the topological properties of developing brain networks occur at late childhood and adolescence. During late childhood period, the structural brain network showed significant increase in the global efficiency but decrease in modularity, suggesting a shift of topological organization toward a more randomized configuration. However, while preserving most topological features, there was a significant increase in the local efficiency at adolescence, suggesting the dynamic process of rewiring and rebalancing brain connections at different growth stages. In addition, several pivotal hubs were identified that are vital for the global coordination of information flow over the whole brain network across all age groups. Significant increases of nodal efficiency were present in several regions such as precuneus at late childhood. Finally, a stable and functionally/anatomically related modular organization was identified throughout the development of the WM network. This study used network analysis to elucidate the topological changes in brain maturation, paving the way for developing novel methods for analyzing disrupted brain connectivity in

Static analysis of topology-dependent broadcast networks

DEFF Research Database (Denmark)

Nanz, Sebastian; Nielson, Flemming; Nielson, Hanne Riis

2010-01-01

changing network topology is a crucial ingredient. In this paper, we develop a static analysis that automatically constructs an abstract transition system, labelled by actions and connectivity information, to yield a mobility-preserving finite abstraction of the behaviour of a network expressed......Broadcast semantics poses significant challenges over point-to-point communication when it comes to formal modelling and analysis. Current approaches to analysing broadcast networks have focused on fixed connectivities, but this is unsuitable in the case of wireless networks where the dynamically...... in a process calculus with asynchronous local broadcast. Furthermore, we use model checking based on a 3-valued temporal logic to distinguish network behaviour which differs under changing connectivity patterns. (C) 2009 Elsevier Inc. All rights reserved....

The Emergence Of Groups In The Evolution Of Friendship Networks

NARCIS (Netherlands)

Zeggelink, Evelien P.H.; Stokman, Frans N.; Bunt, Gerhard G. van der

1997-01-01

Friendship networks usually show a certain degree of segmentation: subgroups of friends. The explanation of the emergence of such groups from initially dyadic pair friendships is a diflicult but important problem. In this paper we attempt to provide a first contribution to the explanation of

The emergence of groups in the evolution of friendship networks

NARCIS (Netherlands)

Zeggelink, Evelien P.H.; Stokman, Frans N.; Bunt, Gerhard G. van der

1996-01-01

Friendship networks usually show a certain degree of segmentation: subgroups of friends. The explanation of the emergence of such groups from initially dyadic pair friendships is a difficult but important problem. In this paper we attempt to provide a first contribution to the explanation of

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...Release, Distribution Unlimited) N/A The goal of this project was to develop topological methods to detect and localize vulnerabilities of wireless... topology U U U UU 32 Michael Robinson 202-885-3681 Final Report: May 2016 Topological Analysis of Wireless Networks Principal Investigator: Prof. Michael

A networks analysis of terrorism in Africa: implications for Kenya

Directory of Open Access Journals (Sweden)

Steven Kigen Morumbasi

2016-12-01

Full Text Available This paper highlights the challenges that the international community faces in responding to the terrorists and the need to change tactics to respond more effectively to an increasingly nebulous enemy. Terrorism can take different forms and is perpetrated by both state and non-state actors. This research looks into the network structure of terrorism and terrorist groups. In the contemporary setting, terrorist organizations operate transnationally hence the use of the term ‘terrorism without borders’. An enabling factor of terrorism today is the network structure that it has adopted which gives it the ability to both project its reach and prevent easy infiltration. The network structure has also brought about renewed interests in Africa, where global terror networks such as al-Qaeda and the Islamic State compete for influence. Boko Haram in West Africa is an affiliate of the Islamic State and this provides possible linkages with the Islamic State in Libya. Boko Haram refers to itself as the Islamic State’s Western Province. Al-Shabaab has dominated headlines by carrying out deadly attacks in East Africa. The al-Qaeda affiliate has however faced resistance from a section of its members who seek ties with the Islamic State. This resulted in the formation of Jabha East Africa, a group that aligns itself to the Islamic State. The Sinai Peninsula has also witnessed an upsurge of terror attacks perpetrated by the Sinai Province, which views itself as a province of the Islamic State. This surmounts to a complex network structure of terrorist networks in Africa and the growing threat to militant Islam. The special attention is paid to analysis of terrorist challenges in Kenia.

The development of English primary care group governance. A scenario analysis.

Science.gov (United States)

Sheaff, R

1999-01-01

At present there is a policy vacuum about what English Primary Care Groups' (PCGs) governance will be when they develop into Primary Care Trusts (PCTs). Draft legislation leaves many options open, so PCT governance is likely to 'emerge' as PCTs are created. It also remains uncertain how general practitioners (GPs) will react to the formation of PCTs and how the UK government will then respond in turn. A scenario analysis suggests three possible lines of development. The base (likeliest) scenario predicts a mainly networked form of PCT governance. An alternative scenario is of PCT governance resembling the former National Health Service internal market. A third scenario predicts 'franchise model' PCTs employing some GPs and subcontracting others. To different degrees all three scenarios predict that PCTs will retain elements of networked governance. If it fails to make GPs as accountable to NHS management as the UK government wishes, networked governance may prove only a transitional stage before English PCTs adopt either quasi-market or hierarchical governance.

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...... 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...

Let's Face(book) It: Analyzing Interactions in Social Network Groups for Chemistry Learning

Science.gov (United States)

Rap, Shelley; Blonder, Ron

2016-02-01

We examined how social network (SN) groups contribute to the learning of chemistry. The main goal was to determine whether chemistry learning could occur in the group discourse. The emphasis was on groups of students in the 11th and 12th grades who learn chemistry in preparation for their final external examination. A total of 1118 discourse events were tallied in the different groups. We analyzed the different events that were found in chemistry learning Facebook groups (CLFGs). The analysis revealed that seven types of interactions were observed in the CLFGs: The most common interaction (47 %) dealt with organizing learning (e.g., announcements regarding homework, the location of the next class); learning interactions were observed in 22 % of the posts, and links to learning materials and social interactions constituted about 20 % each. The learning events that were ascertained underwent a deeper examination and three different types of chemistry learning interactions were identified. This examination was based on the theoretical framework of the commognitive approach to learning (Sfard in Thinking as communicating. Cambridge University Press, Cambridge, 2008), which will be explained. The identified learning interactions that were observed in the Facebook groups illustrate the potential of SNs to serve as an additional tool for teachers to advance their students' learning of chemistry.

Application of Network Analysis to Identify and Map Relationships between Information Systems in the context of Arctic Sustainability

Science.gov (United States)

Kontar, Y. Y.

2017-12-01

The Arctic Council is an intergovernmental forum promoting cooperation, coordination and interaction among the Arctic States and indigenous communities on issues of sustainable development and environmental protection in the North. The work of the Council is primarily carried out by six Working Groups: Arctic Contaminants Action Program, Arctic Monitoring and Assessment Programme, Conservation of Arctic Flora and Fauna, Emergency Prevention, Preparedness and Response, Protection of the Arctic Marine Environment, and Sustainable Development Working Group. The Working Groups are composed of researchers and representatives from government agencies. Each Working Group issues numerous scientific assessments and reports on a broad field of subjects, from climate change to emergency response in the Arctic. A key goal of these publications is to contribute to policy-making in the Arctic. Complex networks of information systems and the connections between the diverse elements within the systems have been identified via network analysis. This allowed to distinguish data sources that were used in the composition of the primary publications of the Working Groups. Next step is to implement network analysis to identify and map the relationships between the Working Groups and policy makers in the Arctic.

An Efficient Causal Group Communication Protocol for Free Scale Peer-to-Peer Networks

Directory of Open Access Journals (Sweden)

Grigory Evropeytsev

2016-08-01

Full Text Available In peer-to-peer (P2P overlay networks, a group of n (≥2 peer processes have to cooperate with each other. Each peer sends messages to every peer and receives messages from every peer in a group. In group communications, each message sent by a peer is required to be causally delivered to every peer. Most of the protocols designed to ensure causal message order are designed for networks with a plain architecture. These protocols can be adapted to use in free scale and hierarchical topologies; however, the amount of control information is O(n, where n is the number of peers in the system. Some protocols are designed for a free scale or hierarchical networks, but in general they force the whole system to accomplish the same order viewed by a super peer. In this paper, we present a protocol that is specifically designed to work with a free scale peer-to-peer network. By using the information about the network’s architecture and by representing message dependencies on a bit level, the proposed protocol ensures causal message ordering without enforcing super peers order. The designed protocol is simulated and compared with the Immediate Dependency Relation and the Dependency Sequences protocols to show its lower overhead.

The ames network and the task group on WWER's

International Nuclear Information System (INIS)

Davies, L.M.; Duysen, J.C. van; Estorff, U. von; Sycamore, D.

1997-01-01

The European Network on 'Ageing Materials Evaluation and Studies' (AMES) was created in 1993. Its main objectives are (a) to provide information and understanding on neutron irradiation effects in reactor materials in support of designers, operators, regulators and researchers and (b) to establish and discharge projects in the above areas. The Steering Committee is composed of at least one participant from each nuclear European Union country. The JRC's Institute for Advanced Materials of the European Commission plays the role of Operating Agent and Manager of the AMES Network. This paper describes the structure, objectives, and major projects of the AMES network. Particular emphasis is placed upon the work it is intended to perform within the Task Group on 'WWER's of the first AMES project (AMES1) on 'Validation of surveillance practice and mitigation methods'. EC DGXVII is addressing the question of how to facilitate contacts between EU and Russian industries in the framework of nuclear Industrial co-operation, and this project may provide a suitable starting point upon which to develop a basis for further work of mutual interest. (author)

WGCNA: an R package for weighted correlation network analysis.

Science.gov (United States)

Langfelder, Peter; Horvath, Steve

2008-12-29

Correlation networks are increasingly being used in bioinformatics applications. For example, weighted gene co-expression network analysis is a systems biology method for describing the correlation patterns among genes across microarray samples. Weighted correlation network analysis (WGCNA) can be used for finding clusters (modules) of highly correlated genes, for summarizing such clusters using the module eigengene or an intramodular hub gene, for relating modules to one another and to external sample traits (using eigengene network methodology), and for calculating module membership measures. Correlation networks facilitate network based gene screening methods that can be used to identify candidate biomarkers or therapeutic targets. These methods have been successfully applied in various biological contexts, e.g. cancer, mouse genetics, yeast genetics, and analysis of brain imaging data. While parts of the correlation network methodology have been described in separate publications, there is a need to provide a user-friendly, comprehensive, and consistent software implementation and an accompanying tutorial. The WGCNA R software package is a comprehensive collection of R functions for performing various aspects of weighted correlation network analysis. The package includes functions for network construction, module detection, gene selection, calculations of topological properties, data simulation, visualization, and interfacing with external software. Along with the R package we also present R software tutorials. While the methods development was motivated by gene expression data, the underlying data mining approach can be applied to a variety of different settings. The WGCNA package provides R functions for weighted correlation network analysis, e.g. co-expression network analysis of gene expression data. The R package along with its source code and additional material are freely available at http://www.genetics.ucla.edu/labs/horvath/CoexpressionNetwork/Rpackages/WGCNA.

Visual sensory networks and effective information transfer in animal groups.

Science.gov (United States)

Strandburg-Peshkin, Ariana; Twomey, Colin R; Bode, Nikolai W F; Kao, Albert B; Katz, Yael; Ioannou, Christos C; Rosenthal, Sara B; Torney, Colin J; Wu, Hai Shan; Levin, Simon A; Couzin, Iain D

2013-09-09

Social transmission of information is vital for many group-living animals, allowing coordination of motion and effective response to complex environments. Revealing the interaction networks underlying information flow within these groups is a central challenge. Previous work has modeled interactions between individuals based directly on their relative spatial positions: each individual is considered to interact with all neighbors within a fixed distance (metric range), a fixed number of nearest neighbors (topological range), a 'shell' of near neighbors (Voronoi range), or some combination (Figure 1A). However, conclusive evidence to support these assumptions is lacking. Here, we employ a novel approach that considers individual movement decisions to be based explicitly on the sensory information available to the organism. In other words, we consider that while spatial relations do inform interactions between individuals, they do so indirectly, through individuals' detection of sensory cues. We reconstruct computationally the visual field of each individual throughout experiments designed to investigate information propagation within fish schools (golden shiners, Notemigonus crysoleucas). Explicitly considering visual sensing allows us to more accurately predict the propagation of behavioral change in these groups during leadership events. Furthermore, we find that structural properties of visual interaction networks differ markedly from those of metric and topological counterparts, suggesting that previous assumptions may not appropriately reflect information flow in animal groups. Copyright © 2013 Elsevier Ltd. All rights reserved.

Network-assisted crop systems genetics: network inference and integrative analysis.

Science.gov (United States)

Lee, Tak; Kim, Hyojin; Lee, Insuk

2015-04-01

Although next-generation sequencing (NGS) technology has enabled the decoding of many crop species genomes, most of the underlying genetic components for economically important crop traits remain to be determined. Network approaches have proven useful for the study of the reference plant, Arabidopsis thaliana, and the success of network-based crop genetics will also require the availability of a genome-scale functional networks for crop species. In this review, we discuss how to construct functional networks and elucidate the holistic view of a crop system. The crop gene network then can be used for gene prioritization and the analysis of resequencing-based genome-wide association study (GWAS) data, the amount of which will rapidly grow in the field of crop science in the coming years. Copyright © 2015 Elsevier Ltd. All rights reserved.

Effects of a Group Intervention on the Career Network Ties of Finnish Adolescents

Science.gov (United States)

Jokisaari, Markku; Vuori, Jukka

2011-01-01

The authors evaluated how a group-based career intervention affected career network ties among Finnish adolescents as they made educational choices and prepared for their transition to secondary education. They examined the career-related network ties of 868 students during their last year in comprehensive school (junior high school) in a…

New York hospital group striving for brand recognition. HealthStar Network.

Science.gov (United States)

Herreria, J

1998-01-01

HealthStar Network established a new concept in its eastern market--a group of hospitals forming one association. Marketers of HealthStar are conducting a branding campaign to distinguish individual expertise under one umbrella company.

Extracting Intrinsic Functional Networks with Feature-Based Group Independent Component Analysis

Science.gov (United States)

Calhoun, Vince D.; Allen, Elena

2013-01-01

There is increasing use of functional imaging data to understand the macro-connectome of the human brain. Of particular interest is the structure and function of intrinsic networks (regions exhibiting temporally coherent activity both at rest and while a task is being performed), which account for a significant portion of the variance in…

Network Analysis of Rodent Transcriptomes in Spaceflight

Science.gov (United States)

Ramachandran, Maya; Fogle, Homer; Costes, Sylvain

2017-01-01

Network analysis methods leverage prior knowledge of cellular systems and the statistical and conceptual relationships between analyte measurements to determine gene connectivity. Correlation and conditional metrics are used to infer a network topology and provide a systems-level context for cellular responses. Integration across multiple experimental conditions and omics domains can reveal the regulatory mechanisms that underlie gene expression. GeneLab has assembled rich multi-omic (transcriptomics, proteomics, epigenomics, and epitranscriptomics) datasets for multiple murine tissues from the Rodent Research 1 (RR-1) experiment. RR-1 assesses the impact of 37 days of spaceflight on gene expression across a variety of tissue types, such as adrenal glands, quadriceps, gastrocnemius, tibalius anterior, extensor digitorum longus, soleus, eye, and kidney. Network analysis is particularly useful for RR-1 -omics datasets because it reinforces subtle relationships that may be overlooked in isolated analyses and subdues confounding factors. Our objective is to use network analysis to determine potential target nodes for therapeutic intervention and identify similarities with existing disease models. Multiple network algorithms are used for a higher confidence consensus.

Ad Hoc Transient Groups: Instruments for Awareness in Learning Networks

NARCIS (Netherlands)

Fetter, Sibren; Rajagopal, Kamakshi; Berlanga, Adriana; Sloep, Peter

2011-01-01

Fetter, S., Rajagopal, K., Berlanga, A. J., & Sloep, P. B. (2011). Ad Hoc Transient Groups: Instruments for Awareness in Learning Networks. In W. Reinhardt, T. D. Ullmann, P. Scott, V. Pammer, O. Conlan, & A. J. Berlanga (Eds.), Proceedings of the 1st European Workshop on Awareness and Reflection in

Consistency analysis of network traffic repositories

NARCIS (Netherlands)

Lastdrager, Elmer; Lastdrager, E.E.H.; Pras, Aiko

Traffic repositories with TCP/IP header information are very important for network analysis. Researchers often assume that such repositories reliably represent all traffic that has been flowing over the network; little thoughts are made regarding the consistency of these repositories. Still, for

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

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/.

Concept mapping and network analysis: an analytic approach to measure ties among constructs.

Science.gov (United States)

Goldman, Alyssa W; Kane, Mary

2014-12-01

Group concept mapping is a mixed-methods approach that helps a group visually represent its ideas on a topic of interest through a series of related maps. The maps and additional graphics are useful for planning, evaluation and theory development. Group concept maps are typically described, interpreted and utilized through points, clusters and distances, and the implications of these features in understanding how constructs relate to one another. This paper focuses on the application of network analysis to group concept mapping to quantify the strength and directionality of relationships among clusters. The authors outline the steps of this analysis, and illustrate its practical use through an organizational strategic planning example. Additional benefits of this analysis to evaluation projects are also discussed, supporting the overall utility of this supplemental technique to the standard concept mapping methodology. Copyright © 2014 Elsevier Ltd. All rights reserved.

Report of Break Out Group 1

DEFF Research Database (Denmark)

Alward, Randy; Carley, Kathleen M.; Madsen, Fredrik Huitfeldt

2006-01-01

, action" (OODA) loop. The break out group discussed vulnerability presentation needs common across various application domains, particularly in support of network discovery and network analysis tasks in those domains. Finally, the break out group wished to determine whether there is a means...... of characterizing a vulnerability. This would take into account the potential for the vulnerability to be exploited as well as the potential impact on the operations supported by the network, and on the network structure itself, of a successful exploit of that vulnerability.......To help understand a network and its ability to continue operating when under attack, the break out group discussed issues that need to be considered when presenting network vulnerability information to an analyst, manager or commander in effective support of that person's "observe, orient, decide...

Network Approach to Autistic Traits: Group and Subgroup Analyses of ADOS Item Scores

Science.gov (United States)

Anderson, George M.; Montazeri, Farhad; de Bildt, Annelies

2015-01-01

A network conceptualization might contribute to understanding the occurrence and interacting nature of behavioral traits in the autism realm. Networks were constructed based on correlations of item scores of the Autism Diagnostic Observation Schedule for Modules 1, 2 and 3 obtained for a group of 477 Dutch individuals with developmental disorders.…

Performance assessment of air quality monitoring networks using principal component analysis and cluster analysis

International Nuclear Information System (INIS)

Lu, Wei-Zhen; He, Hong-Di; Dong, Li-yun

2011-01-01

This study aims to evaluate the performance of two statistical methods, principal component analysis and cluster analysis, for the management of air quality monitoring network of Hong Kong and the reduction of associated expenses. The specific objectives include: (i) to identify city areas with similar air pollution behavior; and (ii) to locate emission sources. The statistical methods were applied to the mass concentrations of sulphur dioxide (SO 2 ), respirable suspended particulates (RSP) and nitrogen dioxide (NO 2 ), collected in monitoring network of Hong Kong from January 2001 to December 2007. The results demonstrate that, for each pollutant, the monitoring stations are grouped into different classes based on their air pollution behaviors. The monitoring stations located in nearby area are characterized by the same specific air pollution characteristics and suggested with an effective management of air quality monitoring system. The redundant equipments should be transferred to other monitoring stations for allowing further enlargement of the monitored area. Additionally, the existence of different air pollution behaviors in the monitoring network is explained by the variability of wind directions across the region. The results imply that the air quality problem in Hong Kong is not only a local problem mainly from street-level pollutions, but also a region problem from the Pearl River Delta region. (author)

A reliability analysis tool for SpaceWire network

Science.gov (United States)

Zhou, Qiang; Zhu, Longjiang; Fei, Haidong; Wang, Xingyou

2017-04-01

A SpaceWire is a standard for on-board satellite networks as the basis for future data-handling architectures. It is becoming more and more popular in space applications due to its technical advantages, including reliability, low power and fault protection, etc. High reliability is the vital issue for spacecraft. Therefore, it is very important to analyze and improve the reliability performance of the SpaceWire network. This paper deals with the problem of reliability modeling and analysis with SpaceWire network. According to the function division of distributed network, a reliability analysis method based on a task is proposed, the reliability analysis of every task can lead to the system reliability matrix, the reliability result of the network system can be deduced by integrating these entire reliability indexes in the matrix. With the method, we develop a reliability analysis tool for SpaceWire Network based on VC, where the computation schemes for reliability matrix and the multi-path-task reliability are also implemented. By using this tool, we analyze several cases on typical architectures. And the analytic results indicate that redundancy architecture has better reliability performance than basic one. In practical, the dual redundancy scheme has been adopted for some key unit, to improve the reliability index of the system or task. Finally, this reliability analysis tool will has a directive influence on both task division and topology selection in the phase of SpaceWire network system design.

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

Social management of laboratory rhesus macaques housed in large groups using a network approach: A review.

Science.gov (United States)

McCowan, Brenda; Beisner, Brianne; Hannibal, Darcy

2017-12-07

Biomedical facilities across the nation and worldwide aim to develop cost-effective methods for the reproductive management of macaque breeding groups, typically by housing macaques in large, multi-male multi-female social groups that provide monkey subjects for research as well as appropriate socialization for their psychological well-being. One of the most difficult problems in managing socially housed macaques is their propensity for deleterious aggression. From a management perspective, deleterious aggression (as opposed to less intense aggression that serves to regulate social relationships) is undoubtedly the most problematic behavior observed in group-housed macaques, which can readily escalate to the degree that it causes social instability, increases serious physical trauma leading to group dissolution, and reduces psychological well-being. Thus for both welfare and other management reasons, aggression among rhesus macaques at primate centers and facilities needs to be addressed with a more proactive approach.Management strategies need to be instituted that maximize social housing while also reducing problematic social aggression due to instability using efficacious methods for detection and prevention in the most cost effective manner. Herein we review a new proactive approach using social network analysis to assess and predict deleterious aggression in macaque groups. We discovered three major pathways leading to instability, such as unusually high rates and severity of trauma and social relocations.These pathways are linked either directly or indirectly to network structure in rhesus macaque societies. We define these pathways according to the key intrinsic and extrinsic variables (e.g., demographic, genetic or social factors) that influence network and behavioral measures of stability (see Fig. 1). They are: (1) presence of natal males, (2) matrilineal genetic fragmentation, and (3) the power structure and conflict policing behavior supported by this

Integrating neural network technology and noise analysis

International Nuclear Information System (INIS)

Uhrig, R.E.; Oak Ridge National Lab., TN

1995-01-01

The integrated use of neural network and noise analysis technologies offers advantages not available by the use of either technology alone. The application of neural network technology to noise analysis offers an opportunity to expand the scope of problems where noise analysis is useful and unique ways in which the integration of these technologies can be used productively. The two-sensor technique, in which the responses of two sensors to an unknown driving source are related, is used to demonstration such integration. The relationship between power spectral densities (PSDs) of accelerometer signals is derived theoretically using noise analysis to demonstrate its uniqueness. This relationship is modeled from experimental data using a neural network when the system is working properly, and the actual PSD of one sensor is compared with the PSD of that sensor predicted by the neural network using the PSD of the other sensor as an input. A significant deviation between the actual and predicted PSDs indicate that system is changing (i.e., failing). Experiments carried out on check values and bearings illustrate the usefulness of the methodology developed. (Author)

Economic assessment group on power transmission and distribution networks tariffs; Groupe d'expertise economique sur la tarification des reseaux de transport et de distribution de l'electricite

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-06-01

Facing the new law on the electric power market liberalization, the french government created an experts group to analyze solutions and assessment methods of the electrical networks costs and tariffs and to control their efficiency. This report presents the analysis and the conclusions of the group. It concerns the three main subjects: the regulation context, the tariffing of the electric power transmission and distribution (the cost and efficiency of the various options) and the tariffing of the electric power supply to the eligible consumers. The authors provide a guideline for a tariffing policy. (A.L.B.)

Analysis and Testing of Mobile Wireless Networks

Science.gov (United States)

Alena, Richard; Evenson, Darin; Rundquist, Victor; Clancy, Daniel (Technical Monitor)

2002-01-01

Wireless networks are being used to connect mobile computing elements in more applications as the technology matures. There are now many products (such as 802.11 and 802.11b) which ran in the ISM frequency band and comply with wireless network standards. They are being used increasingly to link mobile Intranet into Wired networks. Standard methods of analyzing and testing their performance and compatibility are needed to determine the limits of the technology. This paper presents analytical and experimental methods of determining network throughput, range and coverage, and interference sources. Both radio frequency (BE) domain and network domain analysis have been applied to determine wireless network throughput and range in the outdoor environment- Comparison of field test data taken under optimal conditions, with performance predicted from RF analysis, yielded quantitative results applicable to future designs. Layering multiple wireless network- sooners can increase performance. Wireless network components can be set to different radio frequency-hopping sequences or spreading functions, allowing more than one sooner to coexist. Therefore, we ran multiple 802.11-compliant systems concurrently in the same geographical area to determine interference effects and scalability, The results can be used to design of more robust networks which have multiple layers of wireless data communication paths and provide increased throughput overall.

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

African Journals Online (AJOL)

... number of nodes (n) in a linear topology. The degradation is found to be higher in a fully mesh network as a result of increase in interference and MAC layer contention in the network. Key words: Wireless mesh network (WMN), Adhoc network, Network capacity analysis, Bottleneck collision domain, Medium access control ...

Modular analysis of biological networks.

Science.gov (United States)

Kaltenbach, Hans-Michael; Stelling, Jörg

2012-01-01

The analysis of complex biological networks has traditionally relied on decomposition into smaller, semi-autonomous units such as individual signaling pathways. With the increased scope of systems biology (models), rational approaches to modularization have become an important topic. With increasing acceptance of de facto modularity in biology, widely different definitions of what constitutes a module have sparked controversies. Here, we therefore review prominent classes of modular approaches based on formal network representations. Despite some promising research directions, several important theoretical challenges remain open on the way to formal, function-centered modular decompositions for dynamic biological networks.

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

A statistical framework for differential network analysis from microarray data

Directory of Open Access Journals (Sweden)

Datta Somnath

2010-02-01

Full Text Available Abstract Background It has been long well known that genes do not act alone; rather groups of genes act in consort during a biological process. Consequently, the expression levels of genes are dependent on each other. Experimental techniques to detect such interacting pairs of genes have been in place for quite some time. With the advent of microarray technology, newer computational techniques to detect such interaction or association between gene expressions are being proposed which lead to an association network. While most microarray analyses look for genes that are differentially expressed, it is of potentially greater significance to identify how entire association network structures change between two or more biological settings, say normal versus diseased cell types. Results We provide a recipe for conducting a differential analysis of networks constructed from microarray data under two experimental settings. At the core of our approach lies a connectivity score that represents the strength of genetic association or interaction between two genes. We use this score to propose formal statistical tests for each of following queries: (i whether the overall modular structures of the two networks are different, (ii whether the connectivity of a particular set of "interesting genes" has changed between the two networks, and (iii whether the connectivity of a given single gene has changed between the two networks. A number of examples of this score is provided. We carried out our method on two types of simulated data: Gaussian networks and networks based on differential equations. We show that, for appropriate choices of the connectivity scores and tuning parameters, our method works well on simulated data. We also analyze a real data set involving normal versus heavy mice and identify an interesting set of genes that may play key roles in obesity. Conclusions Examining changes in network structure can provide valuable information about the

Antenna analysis using neural networks

Science.gov (United States)

Smith, William T.

1992-01-01

Conventional computing schemes have long been used to analyze problems in electromagnetics (EM). The vast majority of EM applications require computationally intensive algorithms involving numerical integration and solutions to large systems of equations. The feasibility of using neural network computing algorithms for antenna analysis is investigated. The ultimate goal is to use a trained neural network algorithm to reduce the computational demands of existing reflector surface error compensation techniques. Neural networks are computational algorithms based on neurobiological systems. Neural nets consist of massively parallel interconnected nonlinear computational elements. They are often employed in pattern recognition and image processing problems. Recently, neural network analysis has been applied in the electromagnetics area for the design of frequency selective surfaces and beam forming networks. The backpropagation training algorithm was employed to simulate classical antenna array synthesis techniques. The Woodward-Lawson (W-L) and Dolph-Chebyshev (D-C) array pattern synthesis techniques were used to train the neural network. The inputs to the network were samples of the desired synthesis pattern. The outputs are the array element excitations required to synthesize the desired pattern. Once trained, the network is used to simulate the W-L or D-C techniques. Various sector patterns and cosecant-type patterns (27 total) generated using W-L synthesis were used to train the network. Desired pattern samples were then fed to the neural network. The outputs of the network were the simulated W-L excitations. A 20 element linear array was used. There were 41 input pattern samples with 40 output excitations (20 real parts, 20 imaginary). A comparison between the simulated and actual W-L techniques is shown for a triangular-shaped pattern. Dolph-Chebyshev is a different class of synthesis technique in that D-C is used for side lobe control as opposed to pattern

Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis

Directory of Open Access Journals (Sweden)

Jieqiong Wang

2017-09-01

Full Text Available Previous studies on congenital amusia mainly focused on the impaired fronto-temporal pathway. It is possible that neural pathways of amusia patients on a larger scale are affected. In this study, we investigated changes in structural connections by applying both tract-based and network-based analysis to DTI data of 12 subjects with congenital amusia and 20 demographic-matched normal controls. TBSS (tract-based spatial statistics was used to detect microstructural changes. The results showed that amusics had higher diffusivity indices in the corpus callosum, the right inferior/superior longitudinal fasciculus, and the right inferior frontal-occipital fasciculus (IFOF. The axial diffusivity values of the right IFOF were negatively correlated with musical scores in the amusia group. Network-based analysis showed that the efficiency of the brain network was reduced in amusics. The impairments of WM tracts were also found to be correlated with reduced network efficiency in amusics. This suggests that impaired WM tracts may lead to the reduced network efficiency seen in amusics. Our findings suggest that congenital amusia is a disconnection syndrome.

Is Congenital Amusia a Disconnection Syndrome? A Study Combining Tract- and Network-Based Analysis.

Science.gov (United States)

Wang, Jieqiong; Zhang, Caicai; Wan, Shibiao; Peng, Gang

2017-01-01

Previous studies on congenital amusia mainly focused on the impaired fronto-temporal pathway. It is possible that neural pathways of amusia patients on a larger scale are affected. In this study, we investigated changes in structural connections by applying both tract-based and network-based analysis to DTI data of 12 subjects with congenital amusia and 20 demographic-matched normal controls. TBSS (tract-based spatial statistics) was used to detect microstructural changes. The results showed that amusics had higher diffusivity indices in the corpus callosum, the right inferior/superior longitudinal fasciculus, and the right inferior frontal-occipital fasciculus (IFOF). The axial diffusivity values of the right IFOF were negatively correlated with musical scores in the amusia group. Network-based analysis showed that the efficiency of the brain network was reduced in amusics. The impairments of WM tracts were also found to be correlated with reduced network efficiency in amusics. This suggests that impaired WM tracts may lead to the reduced network efficiency seen in amusics. Our findings suggest that congenital amusia is a disconnection syndrome.

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.

Functional Interaction Network Construction and Analysis for Disease Discovery.

Science.gov (United States)

Wu, Guanming; Haw, Robin

2017-01-01

Network-based approaches project seemingly unrelated genes or proteins onto a large-scale network context, therefore providing a holistic visualization and analysis platform for genomic data generated from high-throughput experiments, reducing the dimensionality of data via using network modules and increasing the statistic analysis power. Based on the Reactome database, the most popular and comprehensive open-source biological pathway knowledgebase, we have developed a highly reliable protein functional interaction network covering around 60 % of total human genes and an app called ReactomeFIViz for Cytoscape, the most popular biological network visualization and analysis platform. In this chapter, we describe the detailed procedures on how this functional interaction network is constructed by integrating multiple external data sources, extracting functional interactions from human curated pathway databases, building a machine learning classifier called a Naïve Bayesian Classifier, predicting interactions based on the trained Naïve Bayesian Classifier, and finally constructing the functional interaction database. We also provide an example on how to use ReactomeFIViz for performing network-based data analysis for a list of genes.

Analysis of world terror networks from the reduced Google matrix of Wikipedia

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El Zant, Samer; Frahm, Klaus M.; Jaffrès-Runser, Katia; Shepelyansky, Dima L.

2018-01-01

We apply the reduced Google matrix method to analyze interactions between 95 terrorist groups and determine their relationships and influence on 64 world countries. This is done on the basis of the Google matrix of the English Wikipedia (2017) composed of 5 416 537 articles which accumulate a great part of global human knowledge. The reduced Google matrix takes into account the direct and hidden links between a selection of 159 nodes (articles) appearing due to all paths of a random surfer moving over the whole network. As a result we obtain the network structure of terrorist groups and their relations with selected countries including hidden indirect links. Using the sensitivity of PageRank to a weight variation of specific links we determine the geopolitical sensitivity and influence of specific terrorist groups on world countries. The world maps of the sensitivity of various countries to influence of specific terrorist groups are obtained. We argue that this approach can find useful application for more extensive and detailed data bases analysis.

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...

Associations of Bcl-2 rs956572 genotype groups in the structural covariance network in early-stage Alzheimer's disease.

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Chang, Chiung-Chih; Chang, Ya-Ting; Huang, Chi-Wei; Tsai, Shih-Jen; Hsu, Shih-Wei; Huang, Shu-Hua; Lee, Chen-Chang; Chang, Wen-Neng; Lui, Chun-Chung; Lien, Chia-Yi

2018-02-08

Alzheimer's disease (AD) is a complex neurodegenerative disease, and genetic differences may mediate neuronal degeneration. In humans, a single-nucleotide polymorphism in the B-cell chronic lymphocytic leukemia/lymphoma-2 (Bcl-2) gene, rs956572, has been found to significantly modulate Bcl-2 protein expression in the brain. The Bcl-2 AA genotype has been associated with reduced Bcl-2 levels and lower gray matter volume in healthy populations. We hypothesized that different Bcl-2 genotype groups may modulate large-scale brain networks that determine neurobehavioral test scores. Gray matter structural covariance networks (SCNs) were constructed in 104 patients with AD using T1-weighted magnetic resonance imaging with seed-based correlation analysis. The patients were stratified into two genotype groups on the basis of Bcl-2 expression (G carriers, n = 76; A homozygotes, n = 28). Four SCNs characteristic of AD were constructed from seeds in the default mode network, salience network, and executive control network, and cognitive test scores served as the major outcome factor. For the G carriers, influences of the SCNs were observed mostly in the default mode network, of which the peak clusters anchored by the posterior cingulate cortex seed determined the cognitive test scores. In contrast, genetic influences in the A homozygotes were found mainly in the executive control network, and both the dorsolateral prefrontal cortex seed and the interconnected peak clusters were correlated with the clinical scores. Despite a small number of cases, the A homozygotes showed greater covariance strength than the G carriers among all four SCNs. Our results suggest that the Bcl-2 rs956572 polymorphism is associated with different strengths of structural covariance in AD that determine clinical outcomes. The greater covariance strength in the four SCNs shown in the A homozygotes suggests that different Bcl-2 polymorphisms play different modulatory roles.

Complex Network Analysis of Guangzhou Metro

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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.

Spectral Analysis Methods of Social Networks

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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

Using Social Network Analysis to Investigate Positive EOL Communication.

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Xu, Jiayun; Yang, Rumei; Wilson, Andrew; Reblin, Maija; Clayton, Margaret F; Ellington, Lee

2018-04-30

End of life (EOL) communication is a complex process involving the whole family and multiple care providers. Applications of analysis techniques that account for communication beyond the patient and patient/provider, will improve clinical understanding of EOL communication. To introduce the use of social network analysis to EOL communication data, and to provide an example of applying social network analysis to home hospice interactions. We provide a description of social network analysis using social network analysis to model communication patterns during home hospice nursing visits. We describe three social network attributes (i.e. magnitude, directionality, and reciprocity) in the expression of positive emotion among hospice nurses, family caregivers, and hospice cancer patients. Differences in communication structure by primary family caregiver gender and across time were also examined. Magnitude (frequency) in the expression of positive emotion occurred most often between nurses and caregivers or nurses and patients. Female caregivers directed more positive emotion to nurses, and nurses directed more positive emotion to other family caregivers when the primary family caregiver was male. Reciprocity (mutuality) in positive emotion declined towards day of death, but increased on day of actual patient death. There was variation in reciprocity by the type of positive emotion expressed. Our example demonstrates that social network analysis can be used to better understand the process of EOL communication. Social network analysis can be expanded to other areas of EOL research, such as EOL decision-making and health care teamwork. Copyright © 2018. Published by Elsevier Inc.

Spectrum-Based and Collaborative Network Topology Analysis and Visualization

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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…

Noise Analysis studies with neural networks

International Nuclear Information System (INIS)

Seker, S.; Ciftcioglu, O.

1996-01-01

Noise analysis studies with neural network are aimed. Stochastic signals at the input of the network are used to obtain an algorithmic multivariate stochastic signal modeling. To this end, lattice modeling of a stochastic signal is performed to obtain backward residual noise sources which are uncorrelated among themselves. There are applied together with an additional input to the network to obtain an algorithmic model which is used for signal detection for early failure in plant monitoring. The additional input provides the information to the network to minimize the difference between the signal and the network's one-step-ahead prediction. A stochastic algorithm is used for training where the errors reflecting the measurement error during the training are also modelled so that fast and consistent convergence of network's weights is obtained. The lattice structure coupled to neural network investigated with measured signals from an actual power plant. (authors)

Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?

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Béliveau, Audrey; Goring, Sarah; Platt, Robert W; Gustafson, Paul

2017-12-01

In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated. Copyright © 2017 John Wiley & Sons, Ltd.

Detecting Distributed Network Traffic Anomaly with Network-Wide Correlation Analysis

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Yang Dan

2008-12-01

Full Text Available Distributed network traffic anomaly refers to a traffic abnormal behavior involving many links of a network and caused by the same source (e.g., DDoS attack, worm propagation. The anomaly transiting in a single link might be unnoticeable and hard to detect, while the anomalous aggregation from many links can be prevailing, and does more harm to the networks. Aiming at the similar features of distributed traffic anomaly on many links, this paper proposes a network-wide detection method by performing anomalous correlation analysis of traffic signals' instantaneous parameters. In our method, traffic signals' instantaneous parameters are firstly computed, and their network-wide anomalous space is then extracted via traffic prediction. Finally, an anomaly is detected by a global correlation coefficient of anomalous space. Our evaluation using Abilene traffic traces demonstrates the excellent performance of this approach for distributed traffic anomaly detection.

Network analysis of oyster transcriptome revealed a cascade of cellular responses during recovery after heat shock.

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Lingling Zhang

Full Text Available Oysters, as a major group of marine bivalves, can tolerate a wide range of natural and anthropogenic stressors including heat stress. Recent studies have shown that oysters pretreated with heat shock can result in induced heat tolerance. A systematic study of cellular recovery from heat shock may provide insights into the mechanism of acquired thermal tolerance. In this study, we performed the first network analysis of oyster transcriptome by reanalyzing microarray data from a previous study. Network analysis revealed a cascade of cellular responses during oyster recovery after heat shock and identified responsive gene modules and key genes. Our study demonstrates the power of network analysis in a non-model organism with poor gene annotations, which can lead to new discoveries that go beyond the focus on individual genes.

Content analysis of neurodegenerative and mental diseases social groups.

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Martínez-Pérez, Borja; de la Torre-Díez, Isabel; Bargiela-Flórez, Beatriz; López-Coronado, Miguel; Rodrigues, Joel J P C

2015-12-01

This article aims to characterize the different types of Facebook and Twitter groups for different mental diseases, their purposes, and their functions. We focused the search on depressive disorders, dementia, and Alzheimer's and Parkinson's diseases and examined the Facebook (www.facebook.com) and Twitter (www.twitter.com) groups. We used four assessment criteria: (1) purpose, (2) type of creator, (3) telehealth content, and (4) free-text responses in surveys and interviews. We observed a total of 357 Parkinson groups, 325 dementia groups, 853 Alzheimer groups, and 1127 depression groups on Facebook and Twitter. Moreover, we analyze the responses provided by different users. The survey and interview responses showed that many people were interested in using social networks to support and help in the fight against these diseases. The results indicate that social networks are acceptable by users in terms of simplicity and utility. People use them for finding support, information, self-help, advocacy and awareness, and for collecting funds. © The Author(s) 2014.

Network-Based Visual Analysis of Tabular Data

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Liu, Zhicheng

2012-01-01

Tabular data is pervasive in the form of spreadsheets and relational databases. Although tables often describe multivariate data without explicit network semantics, it may be advantageous to explore the data modeled as a graph or network for analysis. Even when a given table design conveys some static network semantics, analysts may want to look…

Abnormal brain white matter network in young smokers: a graph theory analysis study.

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Zhang, Yajuan; Li, Min; Wang, Ruonan; Bi, Yanzhi; Li, Yangding; Yi, Zhang; Liu, Jixin; Yu, Dahua; Yuan, Kai

2018-04-01

Previous diffusion tensor imaging (DTI) studies had investigated the white matter (WM) integrity abnormalities in some specific fiber bundles in smokers. However, little is known about the changes in topological organization of WM structural network in young smokers. In current study, we acquired DTI datasets from 58 male young smokers and 51 matched nonsmokers and constructed the WM networks by the deterministic fiber tracking approach. Graph theoretical analysis was used to compare the topological parameters of WM network (global and nodal) and the inter-regional fractional anisotropy (FA) weighted WM connections between groups. The results demonstrated that both young smokers and nonsmokers had small-world topology in WM network. Further analysis revealed that the young smokers exhibited the abnormal topological organization, i.e., increased network strength, global efficiency, and decreased shortest path length. In addition, the increased nodal efficiency predominately was located in frontal cortex, striatum and anterior cingulate gyrus (ACG) in smokers. Moreover, based on network-based statistic (NBS) approach, the significant increased FA-weighted WM connections were mainly found in the PFC, ACG and supplementary motor area (SMA) regions. Meanwhile, the network parameters were correlated with the nicotine dependence severity (FTND) scores, and the nodal efficiency of orbitofrontal cortex was positive correlation with the cigarette per day (CPD) in young smokers. We revealed the abnormal topological organization of WM network in young smokers, which may improve our understanding of the neural mechanism of young smokers form WM topological organization level.

Identification and analysis of signaling networks potentially involved in breast carcinoma metastasis to the brain.

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Feng Li

Full Text Available Brain is a common site of breast cancer metastasis associated with significant neurologic morbidity, decreased quality of life, and greatly shortened survival. However, the molecular and cellular mechanisms underpinning brain colonization by breast carcinoma cells are poorly understood. Here, we used 2D-DIGE (Difference in Gel Electrophoresis proteomic analysis followed by LC-tandem mass spectrometry to identify the proteins differentially expressed in brain-targeting breast carcinoma cells (MB231-Br compared with parental MDA-MB-231 cell line. Between the two cell lines, we identified 12 proteins consistently exhibiting greater than 2-fold (p<0.05 difference in expression, which were associated by the Ingenuity Pathway Analysis (IPA with two major signaling networks involving TNFα/TGFβ-, NFκB-, HSP-70-, TP53-, and IFNγ-associated pathways. Remarkably, highly related networks were revealed by the IPA analysis of a list of 19 brain-metastasis-associated proteins identified recently by the group of Dr. A. Sierra using MDA-MB-435-based experimental system (Martin et al., J Proteome Res 2008 7:908-20, or a 17-gene classifier associated with breast cancer brain relapse reported by the group of Dr. J. Massague based on a microarray analysis of clinically annotated breast tumors from 368 patients (Bos et al., Nature 2009 459: 1005-9. These findings, showing that different experimental systems and approaches (2D-DIGE proteomics used on brain targeting cell lines or gene expression analysis of patient samples with documented brain relapse yield highly related signaling networks, suggest strongly that these signaling networks could be essential for a successful colonization of the brain by metastatic breast carcinoma cells.

Let's Face(book) It: Analyzing Interactions in Social Network Groups for Chemistry Learning

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Rap, Shelley; Blonder, Ron

2016-01-01

We examined how social network (SN) groups contribute to the learning of chemistry. The main goal was to determine whether chemistry learning could occur in the group discourse. The emphasis was on groups of students in the 11th and 12th grades who learn chemistry in preparation for their final external examination. A total of 1118 discourse…

Memory Transmission in Small Groups and Large Networks: An Agent-Based Model.

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Luhmann, Christian C; Rajaram, Suparna

2015-12-01

The spread of social influence in large social networks has long been an interest of social scientists. In the domain of memory, collaborative memory experiments have illuminated cognitive mechanisms that allow information to be transmitted between interacting individuals, but these experiments have focused on small-scale social contexts. In the current study, we took a computational approach, circumventing the practical constraints of laboratory paradigms and providing novel results at scales unreachable by laboratory methodologies. Our model embodied theoretical knowledge derived from small-group experiments and replicated foundational results regarding collaborative inhibition and memory convergence in small groups. Ultimately, we investigated large-scale, realistic social networks and found that agents are influenced by the agents with which they interact, but we also found that agents are influenced by nonneighbors (i.e., the neighbors of their neighbors). The similarity between these results and the reports of behavioral transmission in large networks offers a major theoretical insight by linking behavioral transmission to the spread of information. © The Author(s) 2015.

The facilitation of groups and networks: capabilities to shape creative cooperation

DEFF Research Database (Denmark)

Rasmussen, Lauge Baungaard

2003-01-01

The facilitator, defined as a process guide of creative cooperation, is becoming more and more in focus to assist groups,teams and networks to meet these challenges. The author defines and exemplifies different levels of creative coorperation. Core capabilities of facilitation are defined...

Network Analysis on Attitudes: A Brief Tutorial.

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Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van der Maas, Han L J

2017-07-01

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.

Beyond dark and bright: towards a more holistic understanding of inter-group networks.

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Hejnova, Petra

2010-01-01

Networks are becoming a popular organizational form for structuring human activities. To date, scholars have addressed networks in a variety of fields, including sociology, economics, public administration, criminology, political science, and international security. However, little has been done so far to systematically examine the similarities, differences, and connections between network forms of organization across different academic disciplines. This has important implications for both theory and practice. The lack of attention paid to organizational similarities and differences prevents the exchange of knowledge developed across fields. In turn, policy-makers cannot take full advantage of existing research, and may miss opportunities to improve the work of some networks and combat that of others. To address this gap in the literature, this paper uses the combination of organizational environments and organizational goals to develop a new typology of inter-group networks, and thus improve our understanding of how human behaviour is coordinated through networks.

A Network Text Analysis of David Ayer’s Fury

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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

Multiscale Embedded Gene Co-expression Network Analysis.

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Won-Min Song

2015-11-01

Full Text Available Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3, the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA by: i introducing quality control of co-expression similarities, ii parallelizing embedded network construction, and iii developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs. We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA. MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Multiscale Embedded Gene Co-expression Network Analysis.

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Song, Won-Min; Zhang, Bin

2015-11-01

Gene co-expression network analysis has been shown effective in identifying functional co-expressed gene modules associated with complex human diseases. However, existing techniques to construct co-expression networks require some critical prior information such as predefined number of clusters, numerical thresholds for defining co-expression/interaction, or do not naturally reproduce the hallmarks of complex systems such as the scale-free degree distribution of small-worldness. Previously, a graph filtering technique called Planar Maximally Filtered Graph (PMFG) has been applied to many real-world data sets such as financial stock prices and gene expression to extract meaningful and relevant interactions. However, PMFG is not suitable for large-scale genomic data due to several drawbacks, such as the high computation complexity O(|V|3), the presence of false-positives due to the maximal planarity constraint, and the inadequacy of the clustering framework. Here, we developed a new co-expression network analysis framework called Multiscale Embedded Gene Co-expression Network Analysis (MEGENA) by: i) introducing quality control of co-expression similarities, ii) parallelizing embedded network construction, and iii) developing a novel clustering technique to identify multi-scale clustering structures in Planar Filtered Networks (PFNs). We applied MEGENA to a series of simulated data and the gene expression data in breast carcinoma and lung adenocarcinoma from The Cancer Genome Atlas (TCGA). MEGENA showed improved performance over well-established clustering methods and co-expression network construction approaches. MEGENA revealed not only meaningful multi-scale organizations of co-expressed gene clusters but also novel targets in breast carcinoma and lung adenocarcinoma.

Evaluating the Network: A Workflow for Tracking Twitter Interactions Using Social Networking Analysis

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Goodier, Sarah

2018-01-01

Networking plays an important role in research projects to build a community and audience around a research area. Using social media is popular in project communication as it provides the ability to engage with a group of followers daily. Such online networking tools provide the advantage of providing nearrealtime data, which can be used to…

Social network analysis as a method for analyzing interaction in collaborative online learning environments

Directory of Open Access Journals (Sweden)

Patricia Rice Doran

2011-12-01

Full Text Available Social network analysis software such as NodeXL has been used to describe participation and interaction in numerous social networks, but it has not yet been widely used to examine dynamics in online classes, where participation is frequently required rather than optional and participation patterns may be impacted by the requirements of the class, the instructor’s activities, or participants’ intrinsic engagement with the subject matter. Such social network analysis, which examines the dynamics and interactions among groups of participants in a social network or learning group, can be valuable in programs focused on teaching collaborative and communicative skills, including teacher preparation programs. Applied to these programs, social network analysis can provide information about instructional practices likely to facilitate student interaction and collaboration across diverse student populations. This exploratory study used NodeXL to visualize students’ participation in an online course, with the goal of identifying (1 ways in which NodeXL could be used to describe patterns in participant interaction within an instructional setting and (2 identifying specific patterns in participant interaction among students in this particular course. In this sample, general education teachers demonstrated higher measures of connection and interaction with other participants than did those from specialist (ESOL or special education backgrounds, and tended to interact more frequently with all participants than the majority of participants from specialist backgrounds. We recommend further research to delineate specific applications of NodeXL within an instructional context, particularly to identify potential patterns in student participation based on variables such as gender, background, cultural and linguistic heritage, prior training and education, and prior experience so that instructors can ensure their practice helps to facilitate student interaction

Reliability Analysis Techniques for Communication Networks in Nuclear Power Plant

International Nuclear Information System (INIS)

Lim, T. J.; Jang, S. C.; Kang, H. G.; Kim, M. C.; Eom, H. S.; Lee, H. J.

2006-09-01

The objectives of this project is to investigate and study existing reliability analysis techniques for communication networks in order to develop reliability analysis models for nuclear power plant's safety-critical networks. It is necessary to make a comprehensive survey of current methodologies for communication network reliability. Major outputs of this study are design characteristics of safety-critical communication networks, efficient algorithms for quantifying reliability of communication networks, and preliminary models for assessing reliability of safety-critical communication networks

Living network meta-analysis compared with pairwise meta-analysis in comparative effectiveness research: empirical study

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Nikolakopoulou, Adriani; Mavridis, Dimitris; Furukawa, Toshi A; Cipriani, Andrea; Tricco, Andrea C; Straus, Sharon E; Siontis, George C M; Egger, Matthias

2018-01-01

Abstract Objective To examine whether the continuous updating of networks of prospectively planned randomised controlled trials (RCTs) (“living” network meta-analysis) provides strong evidence against the null hypothesis in comparative effectiveness of medical interventions earlier than the updating of conventional, pairwise meta-analysis. Design Empirical study of the accumulating evidence about the comparative effectiveness of clinical interventions. Data sources Database of network meta-analyses of RCTs identified through searches of Medline, Embase, and the Cochrane Database of Systematic Reviews until 14 April 2015. Eligibility criteria for study selection Network meta-analyses published after January 2012 that compared at least five treatments and included at least 20 RCTs. Clinical experts were asked to identify in each network the treatment comparison of greatest clinical interest. Comparisons were excluded for which direct and indirect evidence disagreed, based on side, or node, splitting test (Pmeta-analysis. The frequency and time to strong evidence was compared against the null hypothesis between pairwise and network meta-analyses. Results 49 comparisons of interest from 44 networks were included; most (n=39, 80%) were between active drugs, mainly from the specialties of cardiology, endocrinology, psychiatry, and rheumatology. 29 comparisons were informed by both direct and indirect evidence (59%), 13 by indirect evidence (27%), and 7 by direct evidence (14%). Both network and pairwise meta-analysis provided strong evidence against the null hypothesis for seven comparisons, but for an additional 10 comparisons only network meta-analysis provided strong evidence against the null hypothesis (P=0.002). The median time to strong evidence against the null hypothesis was 19 years with living network meta-analysis and 23 years with living pairwise meta-analysis (hazard ratio 2.78, 95% confidence interval 1.00 to 7.72, P=0.05). Studies directly comparing

A content analysis of chronic diseases social groups on Facebook and Twitter.

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De la Torre-Díez, Isabel; Díaz-Pernas, Francisco Javier; Antón-Rodríguez, Míriam

2012-01-01

Research on the use of social networks for health-related purposes is limited. This study aims to characterize the purpose and use of Facebook and Twitter groups concerning colorectal cancer, breast cancer, and diabetes. We searched in Facebook ( www.facebook.com ) and Twitter ( www.twitter.com ) using the terms "colorectal cancer," "breast cancer," and "diabetes." Each important group has been analyzed by extracting its network name, number of members, interests, and Web site URL. We found 216 breast cancer groups, 171 colorectal cancer groups, and 527 diabetes groups on Facebook and Twitter. The largest percentage of the colorectal cancer groups (25.58%) addresses prevention, similarly to breast cancer, whereas diabetes groups are mainly focused on research issues (25.09%). There are more social groups about breast cancer and diabetes on Facebook (around 82%) than on Twitter (around 18%). Regarding colorectal cancer, the difference is less: Facebook had 62.23%, and Twitter 31.76%. Social networks are a useful tool for supporting patients suffering from these three diseases. Regarding the use of these social networks for disease support purposes, Facebook shows a higher usage rate than Twitter, perhaps because Twitter is newer than Facebook, and its use is not so generalized.

Social networking in online support groups for health: how online social networking benefits patients.

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Chung, Jae Eun

2014-01-01

An increasing number of online support groups (OSGs) have embraced the features of social networking. So far, little is known about how patients use and benefit from these features. By implementing the uses-and-gratifications framework, the author conducted an online survey with current users of OSGs to examine associations among motivation, use of specific features of OSG, and support outcomes. Findings suggest that OSG users make selective use of varied features depending on their needs, and that perceptions of receiving emotional and informational support are associated more with the use of some features than others. For example, those with strong motivation for social interaction use diverse features of OSG and make one-to-one connections with other users by friending. In contrast, those with strong motivation for information seeking limit their use primarily to discussion boards. Results also show that online social networking features, such as friending and sharing of personal stories on blogs, are helpful in satisfying the need for emotional support. The present study sheds light on online social networking features in the context of health-related OSGs and provides practical lessons on how to improve the capacity of OSGs to serve the needs of their users.

Network systems security analysis

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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.

Network Vulnerability and Risk Assessment

National Research Council Canada - National Science Library

Alward, Randy G; Carley, Kathleen M; Madsen, Fredrik; Taylor, Vincent K; Vandenberghe, Grant

2006-01-01

.... The break out group discussed vulnerability presentation needs common across various application domains, particularly in support of network discovery and network analysis tasks in those domains...

Salience Network and Depressive Severities in Parkinson's Disease with Mild Cognitive Impairment: A Structural Covariance Network Analysis.

Science.gov (United States)

Chang, Ya-Ting; Lu, Cheng-Hsien; Wu, Ming-Kung; Hsu, Shih-Wei; Huang, Chi-Wei; Chang, Wen-Neng; Lien, Chia-Yi; Lee, Jun-Jun; Chang, Chiung-Chih

2017-01-01

Purpose: In Parkinson's disease with mild cognitive impairment (PD-MCI), we investigated the clinical significance of salience network (SN) in depression and cognitive performance. Methods: Seventy seven PD-MCI patients that fulfilled multi-domain and non-amnestic subtype were included. Gray matter structural covariance networks were constructed by 3D T1-magnetic resonance imaging and seed based analysis. The patients were divided into two groups by psychiatric interviews and screening of Geriatric Depression Scale (GDS): PD-MCI with depression (PD-MCI-D) or without depression (PD-MCI-ND). The seed or peak cluster volume, or the significant differences in the regression slopes in each seed-peak cluster correlation, were used to evaluate the significance with the neurobehavioral scores. Results: This study is the first to demonstrate that the PD-MCI-ND group presented a larger number of voxels of structural covariance in SN than the PD-MCI-D group. The right fronto-insular seed volumes and the peak cluster of left lingual gyrus showed significant inverse correlation with the Geriatric Depression Scale (GDS; r = -0.231, P = 0.046). Conclusions: This study is the first to validate the clinical significance of the SN in PD-MCI-D. The right insular seed value and the SN correlated with the severity of depression in PD-MCI.

Application of neural networks to quantitative spectrometry analysis

International Nuclear Information System (INIS)

Pilato, V.; Tola, F.; Martinez, J.M.; Huver, M.

1999-01-01

Accurate quantitative analysis of complex spectra (fission and activation products), relies upon experts' knowledge. In some cases several hours, even days of tedious calculations are needed. This is because current software is unable to solve deconvolution problems when several rays overlap. We have shown that such analysis can be correctly handled by a neural network, and the procedure can be automated with minimum laboratory measurements for networks training, as long as all the elements of the analysed solution figure in the training set and provided that adequate scaling of input data is performed. Once the network has been trained, analysis is carried out in a few seconds. On submitting to a test between several well-known laboratories, where unknown quantities of 57 Co, 58 Co, 85 Sr, 88 Y, 131 I, 139 Ce, 141 Ce present in a sample had to be determined, the results yielded by our network classed it amongst the best. The method is described, including experimental device and measures, training set designing, relevant input parameters definition, input data scaling and networks training. Main results are presented together with a statistical model allowing networks error prediction

LOGISTIC NETWORK REGRESSION FOR SCALABLE ANALYSIS OF NETWORKS WITH JOINT EDGE/VERTEX DYNAMICS.

Science.gov (United States)

Almquist, Zack W; Butts, Carter T

2014-08-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach.

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

Directory of Open Access Journals (Sweden)

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.

Boolean Factor Analysis by Attractor Neural Network

Czech Academy of Sciences Publication Activity Database

Frolov, A. A.; Húsek, Dušan; Muraviev, I. P.; Polyakov, P.Y.

2007-01-01

Roč. 18, č. 3 (2007), s. 698-707 ISSN 1045-9227 R&D Projects: GA AV ČR 1ET100300419; GA ČR GA201/05/0079 Institutional research plan: CEZ:AV0Z10300504 Keywords : recurrent neural network * Hopfield-like neural network * associative memory * unsupervised learning * neural network architecture * neural network application * statistics * Boolean factor analysis * dimensionality reduction * features clustering * concepts search * information retrieval Subject RIV: BB - Applied Statistics, Operational Research Impact factor: 2.769, year: 2007

Applying social network analysis to understand the knowledge sharing behaviour of practitioners in a clinical online discussion forum.

Science.gov (United States)

Stewart, Samuel Alan; Abidi, Syed Sibte Raza

2012-12-04

Knowledge Translation (KT) plays a vital role in the modern health care community, facilitating the incorporation of new evidence into practice. Web 2.0 tools provide a useful mechanism for establishing an online KT environment in which health practitioners share their practice-related knowledge and experiences with an online community of practice. We have implemented a Web 2.0 based KT environment--an online discussion forum--for pediatric pain practitioners across seven different hospitals in Thailand. The online discussion forum enabled the pediatric pain practitioners to share and translate their experiential knowledge to help improve the management of pediatric pain in hospitals. The goal of this research is to investigate the knowledge sharing dynamics of a community of practice through an online discussion forum. We evaluated the communication patterns of the community members using statistical and social network analysis methods in order to better understand how the online community engages to share experiential knowledge. Statistical analyses and visualizations provide a broad overview of the communication patterns within the discussion forum. Social network analysis provides the tools to delve deeper into the social network, identifying the most active members of the community, reporting the overall health of the social network, isolating the potential core members of the social network, and exploring the inter-group relationships that exist across institutions and professions. The statistical analyses revealed a network dominated by a single institution and a single profession, and found a varied relationship between reading and posting content to the discussion forum. The social network analysis discovered a healthy network with strong communication patterns, while identifying which users are at the center of the community in terms of facilitating communication. The group-level analysis suggests that there is strong interprofessional and interregional

Prediction of coal response to froth flotation based on coal analysis using regression and artificial neural network

Energy Technology Data Exchange (ETDEWEB)

Jorjani, E.; Poorali, H.A.; Sam, A.; Chelgani, S.C.; Mesroghli, S.; Shayestehfar, M.R. [Islam Azad University, Tehran (Iran). Dept. of Mining Engineering

2009-10-15

In this paper, the combustible value (i.e. 100-Ash) and combustible recovery of coal flotation concentrate were predicted by regression and artificial neural network based on proximate and group macerals analysis. The regression method shows that the relationships between (a) in (ash), volatile matter and moisture (b) in (ash), in (liptinite), fusinite and vitrinite with combustible value can achieve the correlation coefficients (R{sup 2}) of 0.8 and 0.79, respectively. In addition, the input sets of (c) ash, volatile matter and moisture (d) ash, liptinite and fusinite can predict the combustible recovery with the correlation coefficients of 0.84 and 0.63, respectively. Feed-forward artificial neural network with 6-8-12-11-2-1 arrangement for moisture, ash and volatile matter input set was capable to estimate both combustible value and combustible recovery with correlation of 0.95. It was shown that the proposed neural network model could accurately reproduce all the effects of proximate and group macerals analysis on coal flotation system.

75 FR 49528 - Freescale Semiconductor, Inc., Networking and Multimedia Group (“NMG”) Excluding the Multimedia...

Science.gov (United States)

2010-08-13

... services for chips used in networking and multimedia products. The company reports that workers leased from... Multimedia Applications Division, including on-site workers of Synergy Services, Craftcorp, Directions..., Inc., Networking and Multimedia Group (``NMG'') Excluding the Multimedia Applications Division...

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

Directory of Open Access Journals (Sweden)

Jianhua Ni

2016-08-01

Full Text Available 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.

Google Correlations: New approaches to collecting data for statistical network analysis

Science.gov (United States)

Mahdavi, Paasha

This thesis introduces a new method for data collection on political elite networks using non-obtrusive web-based techniques. One possible indicator of elite connectivity is the frequency with which individuals appear at the same political events. Using a Google search scraping algorithm (Lee 2010) to capture how often pairs of individuals appear in the same news articles reporting on these events, I construct network matrices for a given list of individuals that I identify as elites using a variety of criteria. To assess cross-validity and conceptual accuracy, I compare data from this method to previously collected data on the network connectedness of three separate populations. I then supply an application of the Google method to collect network data on the Nigerian oil elite in 2012. Conducting a network analysis, I show that appointments to the Nigerian National Petroleum Corporation board of directors are made on the basis of political connectivity and not necessarily on technical experience or merit. These findings lend support to hypotheses that leaders use patronage appointments to lucrative bureaucratic positions in order to satisfy political elites. Given that many political theories on elite behavior aim to understand individual- and group-level interactions, the potential applicability of network data using the proposed technique is very large, especially in situations where collecting network data intrusively is costly or prohibitive.

Applying a social network analysis (SNA) approach to understanding radiologists' performance in reading mammograms

Science.gov (United States)

Tavakoli Taba, Seyedamir; Hossain, Liaquat; Heard, Robert; Brennan, Patrick; Lee, Warwick; Lewis, Sarah

2017-03-01

Rationale and objectives: Observer performance has been widely studied through examining the characteristics of individuals. Applying a systems perspective, while understanding of the system's output, requires a study of the interactions between observers. This research explains a mixed methods approach to applying a social network analysis (SNA), together with a more traditional approach of examining personal/ individual characteristics in understanding observer performance in mammography. Materials and Methods: Using social networks theories and measures in order to understand observer performance, we designed a social networks survey instrument for collecting personal and network data about observers involved in mammography performance studies. We present the results of a study by our group where 31 Australian breast radiologists originally reviewed 60 mammographic cases (comprising of 20 abnormal and 40 normal cases) and then completed an online questionnaire about their social networks and personal characteristics. A jackknife free response operating characteristic (JAFROC) method was used to measure performance of radiologists. JAFROC was tested against various personal and network measures to verify the theoretical model. Results: The results from this study suggest a strong association between social networks and observer performance for Australian radiologists. Network factors accounted for 48% of variance in observer performance, in comparison to 15.5% for the personal characteristics for this study group. Conclusion: This study suggest a strong new direction for research into improving observer performance. Future studies in observer performance should consider social networks' influence as part of their research paradigm, with equal or greater vigour than traditional constructs of personal characteristics.

Network Analysis of Commuting Flows: A Comparative Static Approach to German Data

NARCIS (Netherlands)

Patuelli, R.; Reggiani, A.; Nijkamp, P.; Bade, F-J

2007-01-01

The analysis of complex networks has recently received considerable attention. The work by Albert and Barabási presented a research challenge to network analysis, that is, growth of the network. The present paper offers a network analysis of the spatial commuting network in Germany. First, we study

Functional connectivity analysis of the brain network using resting-state fMRI

International Nuclear Information System (INIS)

Hayashi, Toshihiro

2011-01-01

Spatial patterns of spontaneous fluctuations in blood oxygenation level-dependent (BOLD) signals reflect the underlying neural architecture. The study of the brain network based on these self-organized patterns is termed resting-state functional MRI (fMRI). This review article aims at briefly reviewing a basic concept of this technology and discussing its implications for neuropsychological studies. First, the technical aspects of resting-state fMRI, including signal sources, physiological artifacts, image acquisition, and analytical methods such as seed-based correlation analysis and independent component analysis, are explained, followed by a discussion on the major resting-state networks, including the default mode network. In addition, the structure-function correlation studied using diffuse tensor imaging and resting-state fMRI is briefly discussed. Second, I have discussed the reservations and potential pitfalls of 2 major imaging methods: voxel-based lesion-symptom mapping and task fMRI. Problems encountered with voxel-based lesion-symptom mapping can be overcome by using resting-state fMRI and evaluating undamaged brain networks in patients. Regarding task fMRI in patients, I have also emphasized the importance of evaluating the baseline brain activity because the amplitude of activation in BOLD fMRI is hard to interpret as the same baseline cannot be assumed for both patient and normal groups. (author)

Economic assessment group on power transmission and distribution networks tariffs; Groupe d'expertise economique sur la tarification des reseaux de transport et de distribution de l'electricite

Energy Technology Data Exchange (ETDEWEB)

NONE

2000-06-01

Facing the new law on the electric power market liberalization, the french government created an experts group to analyze solutions and assessment methods of the electrical networks costs and tariffs and to control their efficiency. This report presents the analysis and the conclusions of the group. It concerns the three main subjects: the regulation context, the tariffing of the electric power transmission and distribution (the cost and efficiency of the various options) and the tariffing of the electric power supply to the eligible consumers. The authors provide a guideline for a tariffing policy. (A.L.B.)

Rural Health Networks: How Network Analysis Can Inform Patient Care and Organizational Collaboration in a Rural Breast Cancer Screening Network.

Science.gov (United States)

Prusaczyk, Beth; Maki, Julia; Luke, Douglas A; Lobb, Rebecca

2018-04-15

Rural health networks have the potential to improve health care quality and access. Despite this, the use of network analysis to study rural health networks is limited. The purpose of this study was to use network analysis to understand how a network of rural breast cancer care providers deliver services and to demonstrate the value of this methodology in this research area. Leaders at 47 Federally Qualified Health Centers and Rural Health Clinics across 10 adjacent rural counties were asked where they refer patients for mammograms or breast biopsies. These clinics and the 22 referral providers that respondents named comprised the network. The network was analyzed graphically and statistically with exponential random graph modeling. Most (96%, n = 45) of the clinics and referral sites (95%, n = 21) are connected to each other. Two clinics of the same type were 62% less likely to refer patients to the same providers as 2 clinics of different types (OR = 0.38, 95% CI = 0.29-0.50). Clinics in the same county have approximately 8 times higher odds of referring patients to the same providers compared to clinics in different counties (OR = 7.80, CI = 4.57-13.31). This study found that geographic location of resources is an important factor in rural health care providers' referral decisions and demonstrated the usefulness of network analysis for understanding rural health networks. These results can be used to guide delivery of patient care and strengthen the network by building resources that take location into account. © 2018 National Rural Health Association.

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.

Ignalina Safety Analysis Group

International Nuclear Information System (INIS)

Ushpuras, E.

1995-01-01

The article describes the fields of activities of Ignalina NPP Safety Analysis Group (ISAG) in the Lithuanian Energy Institute and overview the main achievements gained since the group establishment in 1992. The group is working under the following guidelines: in-depth analysis of the fundamental physical processes of RBMK-1500 reactors; collection, systematization and verification of the design and operational data; simulation and analysis of potential accident consequences; analysis of thermohydraulic and neutronic characteristics of the plant; provision of technical and scientific consultations to VATESI, Governmental authorities, and also international institutions, participating in various projects aiming at Ignalina NPP safety enhancement. The ISAG is performing broad scientific co-operation programs with both Eastern and Western scientific groups, supplying engineering assistance for Ignalina NPP. ISAG is also participating in the joint Lithuanian - Swedish - Russian project - Barselina, the first Probabilistic Safety Assessment (PSA) study of Ignalina NPP. The work is underway together with Maryland University (USA) for assessment of the accident confinement system for a range of breaks in the primary circuit. At present the ISAG personnel is also involved in the project under the grant from the Nuclear Safety Account, administered by the European Bank for reconstruction and development for the preparation and review of an in-depth safety assessment of the Ignalina plant

An Automatic User Grouping Model for a Group Recommender System in Location-Based Social Networks

Directory of Open Access Journals (Sweden)

Elahe Khazaei

2018-02-01

Full Text Available Spatial group recommendation refers to suggesting places to a given set of users. In a group recommender system, members of a group should have similar preferences in order to increase the level of satisfaction. Location-based social networks (LBSNs provide rich content, such as user interactions and location/event descriptions, which can be leveraged for group recommendations. In this paper, an automatic user grouping model is introduced that obtains information about users and their preferences through an LBSN. The preferences of the users, proximity of the places the users have visited in terms of spatial range, users’ free days, and the social relationships among users are extracted automatically from location histories and users’ profiles in the LBSN. These factors are combined to determine the similarities among users. The users are partitioned into groups based on these similarities. Group size is the key to coordinating group members and enhancing their satisfaction. Therefore, a modified k-medoids method is developed to cluster users into groups with specific sizes. To evaluate the efficiency of the proposed method, its mean intra-cluster distance and its distribution of cluster sizes are compared to those of general clustering algorithms. The results reveal that the proposed method compares favourably with general clustering approaches, such as k-medoids and spectral clustering, in separating users into groups of a specific size with a lower mean intra-cluster distance.

Graph analysis of cell clusters forming vascular networks

Science.gov (United States)

Alves, A. P.; Mesquita, O. N.; Gómez-Gardeñes, J.; Agero, U.

2018-03-01

This manuscript describes the experimental observation of vasculogenesis in chick embryos by means of network analysis. The formation of the vascular network was observed in the area opaca of embryos from 40 to 55 h of development. In the area opaca endothelial cell clusters self-organize as a primitive and approximately regular network of capillaries. The process was observed by bright-field microscopy in control embryos and in embryos treated with Bevacizumab (Avastin), an antibody that inhibits the signalling of the vascular endothelial growth factor (VEGF). The sequence of images of the vascular growth were thresholded, and used to quantify the forming network in control and Avastin-treated embryos. This characterization is made by measuring vessels density, number of cell clusters and the largest cluster density. From the original images, the topology of the vascular network was extracted and characterized by means of the usual network metrics such as: the degree distribution, average clustering coefficient, average short path length and assortativity, among others. This analysis allows to monitor how the largest connected cluster of the vascular network evolves in time and provides with quantitative evidence of the disruptive effects that Avastin has on the tree structure of vascular networks.

SOCIOLOGICAL UNDERSTANDING OF INTERNET: THEORETICAL APPROACHES TO THE NETWORK ANALYSIS

Directory of Open Access Journals (Sweden)

D. E. Dobrinskaya

2016-01-01

Full Text Available The network is an efficient way of social structure analysis for contemporary sociologists. It gives broad opportunities for detailed and fruitful research of different patterns of ties and social relations by quantitative analytical methods and visualization of network models. The network metaphor is used as the most representative tool for description of a new type of society. This new type is characterized by flexibility, decentralization and individualization. Network organizational form became the dominant form in modern societies. The network is also used as a mode of inquiry. Actually three theoretical network approaches in the Internet research case are the most relevant: social network analysis, “network society” theory and actor-network theory. Every theoretical approach has got its own notion of network. Their special methodological and theoretical features contribute to the Internet studies in different ways. The article represents a brief overview of these network approaches. This overview demonstrates the absence of a unified semantic space of the notion of “network” category. This fact, in turn, points out the need for detailed analysis of these approaches to reveal their theoretical and empirical possibilities in application to the Internet studies. 

The primary case is not enough: Variation among individuals, groups and social networks modify bacterial transmission dynamics.

Science.gov (United States)

Keiser, Carl N; Pinter-Wollman, Noa; Ziemba, Michael J; Kothamasu, Krishna S; Pruitt, Jonathan N

2018-03-01

The traits of the primary case of an infectious disease outbreak, and the circumstances for their aetiology, potentially influence the trajectory of transmission dynamics. However, these dynamics likely also depend on the traits of the individuals with whom the primary case interacts. We used the social spider Stegodyphus dumicola to test how the traits of the primary case, group phenotypic composition and group size interact to facilitate the transmission of a GFP-labelled cuticular bacterium. We also compared bacterial transmission across experimentally generated "daisy-chain" vs. "star" networks of social interactions. Finally, we compared social network structure across groups of different sizes. Groups of 10 spiders experienced more bacterial transmission events compared to groups of 30 spiders, regardless of groups' behavioural composition. Groups containing only one bold spider experienced the lowest levels of bacterial transmission regardless of group size. We found no evidence for the traits of the primary case influencing any transmission dynamics. In a second experiment, bacteria were transmitted to more individuals in experimentally induced star networks than in daisy-chains, on which transmission never exceeded three steps. In both experimental network types, transmission success depended jointly on the behavioural traits of the interacting individuals; however, the behavioural traits of the primary case were only important for transmission on star networks. Larger social groups exhibited lower interaction density (i.e. had a low ratio of observed to possible connections) and were more modular, i.e. they had more connections between nodes within a subgroup and fewer connections across subgroups. Thus, larger groups may restrict transmission by forming fewer interactions and by isolating subgroups that interacted with the primary case. These findings suggest that accounting for the traits of single exposed hosts has less power in predicting transmission

Break-collapse method for resistor networks-renormalization group applications

International Nuclear Information System (INIS)

Tsallis, C.; Coniglio, A.; Redner, S.

1982-01-01

The break-collapse method recently introduced for the q-state Potts model is adapted for resistor networks. This method greatly simplifies the calculation of the conductance of an arbitrary two-terminal d-dimensional array of conductances, obviating the use of either Kirchhoff's laws or the star-triangle or similiar transformations. Related properties are discussed as well. An illustrative real-space renormalization-group treatment of the random resistor problem on the square lattice is presented; satisfactory results are obtained. (Author) [pt

Bank-firm credit network in Japan: an analysis of a bipartite network.

Science.gov (United States)

Marotta, Luca; Miccichè, Salvatore; Fujiwara, Yoshi; Iyetomi, Hiroshi; Aoyama, Hideaki; Gallegati, Mauro; Mantegna, Rosario N

2015-01-01

We investigate the networked nature of the Japanese credit market. Our investigation is performed with tools of network science. In our investigation we perform community detection with an algorithm which is identifying communities composed of both banks and firms. We show that the communities obtained by directly working on the bipartite network carry information about the networked nature of the Japanese credit market. Our analysis is performed for each calendar year during the time period from 1980 to 2011. To investigate the time evolution of the networked structure of the credit market we introduce a new statistical method to track the time evolution of detected communities. We then characterize the time evolution of communities by detecting for each time evolving set of communities the over-expression of attributes of firms and banks. Specifically, we consider as attributes the economic sector and the geographical location of firms and the type of banks. In our 32-year-long analysis we detect a persistence of the over-expression of attributes of communities of banks and firms together with a slow dynamic of changes from some specific attributes to new ones. Our empirical observations show that the credit market in Japan is a networked market where the type of banks, geographical location of firms and banks, and economic sector of the firm play a role in shaping the credit relationships between banks and firms.

Network analysis of PTSD symptoms following mass violence.

Science.gov (United States)

Sullivan, Connor P; Smith, Andrew J; Lewis, Michael; Jones, Russell T

2018-01-01

Network analysis is a useful tool for understanding how symptoms interact with one another to influence psychopathology. However, this analytic strategy has not been fully utilized in the PTSD field. The current study utilized network analysis to examine connectedness and strength among PTSD symptoms (employing both partial correlation and regression network analyses) among a community sample of students exposed to the 2007 Virginia Tech shootings. Respondents (N = 4,639) completed online surveys 3-4 months postshootings, with PTSD symptom severity measured via the Trauma Symptom Questionnaire. Data were analyzed via adaptive least absolute shrinkage and selection operator (LASSO) and relative importance networks, as well as Dijkstra's algorithm to identify the shortest path from each symptom to all other symptoms. Relative importance network analysis revealed that intrusive thoughts had the strongest influence on other symptoms (i.e., had many strong connections [highest outdegree]) while computing Dijkstra's algorithm indicated that anger produced the shortest path to all other symptoms (i.e., the strongest connections to all other symptoms). Findings suggest that anger or intrusion likely play a crucial role in the development and maintenance of PTSD (i.e., are more influential within the network than are other symptoms). (PsycINFO Database Record (c) 2018 APA, all rights reserved).