WorldWideScience

Sample records for oklahoma community networks

  1. Oklahoma seismic network

    International Nuclear Information System (INIS)

    Luza, K.V.; Lawson, J.E. Jr.; Univ. of Oklahoma, Norman, OK

    1993-07-01

    The US Nuclear Regulatory Commission has established rigorous guidelines that must be adhered to before a permit to construct a nuclear-power plant is granted to an applicant. Local as well as regional seismicity and structural relationships play an integral role in the final design criteria for nuclear power plants. The existing historical record of seismicity is inadequate in a number of areas of the Midcontinent region because of the lack of instrumentation and (or) the sensitivity of the instruments deployed to monitor earthquake events. The Nemaha Uplift/Midcontinent Geophysical Anomaly is one of five principal areas east of the Rocky Mountain front that has a moderately high seismic-risk classification. The Nemaha uplift, which is common to the states of Oklahoma, Kansas, and Nebraska, is approximately 415 miles long and 12-14 miles wide. The Midcontinent Geophysical Anomaly extends southward from Minnesota across Iowa and the southeastern corner of Nebraska and probably terminates in central Kansas. A number of moderate-sized earthquakes--magnitude 5 or greater--have occurred along or west of the Nemaha uplift. The Oklahoma Geological Survey, in cooperation with the geological surveys of Kansas, Nebraska, and Iowa, conducted a 5-year investigation of the seismicity and tectonic relationships of the Nemaha uplift and associated geologic features in the Midcontinent. This investigation was intended to provide data to be used to design nuclear-power plants. However, the information is also being used to design better large-scale structures, such as dams and high-use buildings, and to provide the necessary data to evaluate earthquake-insurance rates in the Midcontinent

  2. Oklahoma City's Emerging Hispanic Community: New Partnerships, New Successes

    Science.gov (United States)

    Kinders, Mark A.; Pope, Myron L.

    2016-01-01

    The University of Central Oklahoma's new strategic plan sought to increase its connection to the emerging Hispanic community in Oklahoma City. Simultaneously, the Greater Oklahoma City Hispanic Chamber of Commerce was seeking a higher education partner. This case study describes resulting new programs for Hispanic students and businesses. The…

  3. Urban and community forests of the South Central West region: Arkansas, Louisiana, Oklahoma, Texas

    Science.gov (United States)

    David J. Nowak; Eric J. Greenfield

    2010-01-01

    This report details how land cover and urbanization vary within the states of Arkansas, Louisiana, Oklahoma, and Texas by community (incorporated and census designated places), county subdivision, and county. Specifically this report provides critical urban and community forestry information for each state including human population characteristics and trends, changes...

  4. The Perceptions of Administrators in the Implementation of Professional Learning Communities: A Case Study in an Oklahoma School District

    Science.gov (United States)

    Jaques, Shelley

    2010-01-01

    In January of 2002, President George Bush implemented the No Child left behind act that required all students to be proficient on state standards by the year 2014. One way a school district in Oklahoma met these new requirements was through the implementation of the principles of a Professional Learning Community. This case study was designed…

  5. Community Structure in Online Collegiate Social Networks

    Science.gov (United States)

    Traud, Amanda; Kelsic, Eric; Mucha, Peter; Porter, Mason

    2009-03-01

    Online social networking sites have become increasingly popular with college students. The networks we studied are defined through ``friendships'' indicated by Facebook users from UNC, Oklahoma, Caltech, Georgetown, and Princeton. We apply the tools of network science to study the Facebook networks from these five different universities at a single point in time. We investigate each single-institution network's community structure, which we obtain through partitioning the graph using an eigenvector method. We use both graphical and quantitative tools, including pair-counting methods, which we interpret through statistical analysis and permutation tests to measure the correlations between the network communities and a set of characteristics given by each user (residence, class year, major, and high school). We also analyze the single gender subsets of these networks, and the impact of missing demographical data. Our study allows us to compare the online social networks for the five schools as well as infer differences in offline social interactions. At the schools studied, we were able to define which characteristics of the Facebook users correlate best with friendships.

  6. Estimating receiver functions on dense arrays: application to the IRIS Community Wavefield Experiment in Oklahoma

    Science.gov (United States)

    Zhong, M.; Zhan, Z.

    2017-12-01

    Receiver functions (RF) estimated on dense arrays have been widely used for studies of Earth structures at different scales. However, there are still challenges in estimating and interpreting RF images due to non-uniqueness of deconvolution, noise in data, and lack of uncertainty. Here, we develop a dense-array-based RF method towards robust and high-resolution RF images. We cast RF images as the models in a sparsity-promoted inverse problem, in which waveforms from multiple events recorded by neighboring stations are jointly inverted. We use the Neighborhood Algorithm to find the optimal model (i.e., RF image) as well as an ensemble of models for further uncertainty quantification. Synthetic tests and application to the IRIS Community Wavefield Experiment in Oklahoma demonstrate that the new method is able to deal with challenging dataset, retrieve reliable high-resolution RF images, and provide realistic uncertainty estimates.

  7. Stakeholder engagement: a model for tobacco policy planning in Oklahoma Tribal communities.

    Science.gov (United States)

    Blanchard, Jessica W; Petherick, J T; Basara, Heather

    2015-01-01

    Oklahoma law pre-empts local governments from enacting smoking restrictions inside public places that are stricter than state law, but the sovereign status of Oklahoma's 38 Tribal nations means they are uniquely positioned to stand apart as leaders in the area of tobacco policy. To provide recommendations for employing university-Tribal partnerships as an effective strategy for tobacco policy planning in tribal communities. Using a community-based participatory research approach, researchers facilitated a series of meetings with key Tribal stakeholders in order to develop a comprehensive tobacco policy plan. Ongoing engagement activities held between January 2011 and May 2012, including interdepartmental visits, facility site tours, interviews, and attendance at tribal activities, were critical for fostering constructive and trusting relationships between all partners involved in the policy planning process. The 17-month collaborative engagement produced a plan designed to regulate the use of commercial tobacco in all Tribally owned properties. The extended period of collaboration between the researchers and Tribal stakeholders facilitated: (1) levels of trust between partners; and (2) a steadfast commitment to the planning process, ensuring completion of the plan amid uncertain political climates and economic concerns about tobacco bans. Extended engagement produced an effective foundation for policy planning that promoted collaboration between otherwise dispersed Tribal departments, and facilitated communication of diverse stakeholder interests related to the goal of tobacco policies. The findings of this study provide useful strategies and best practices for those looking to employ Tribal-university partnerships as strategies for tobacco control planning and policy-based research. Copyright © 2015 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

  8. Investigating microearthquake finite source attributes with IRIS Community Wavefield Demonstration Experiment in Oklahoma

    Science.gov (United States)

    Fan, Wenyuan; McGuire, Jeffrey J.

    2018-05-01

    An earthquake rupture process can be kinematically described by rupture velocity, duration and spatial extent. These key kinematic source parameters provide important constraints on earthquake physics and rupture dynamics. In particular, core questions in earthquake science can be addressed once these properties of small earthquakes are well resolved. However, these parameters of small earthquakes are poorly understood, often limited by available datasets and methodologies. The IRIS Community Wavefield Experiment in Oklahoma deployed ˜350 three component nodal stations within 40 km2 for a month, offering an unprecedented opportunity to test new methodologies for resolving small earthquake finite source properties in high resolution. In this study, we demonstrate the power of the nodal dataset to resolve the variations in the seismic wavefield over the focal sphere due to the finite source attributes of a M2 earthquake within the array. The dense coverage allows us to tightly constrain rupture area using the second moment method even for such a small earthquake. The M2 earthquake was a strike-slip event and unilaterally propagated towards the surface at 90 per cent local S- wave speed (2.93 km s-1). The earthquake lasted ˜0.019 s and ruptured Lc ˜70 m by Wc ˜45 m. With the resolved rupture area, the stress-drop of the earthquake is estimated as 7.3 MPa for Mw 2.3. We demonstrate that the maximum and minimum bounds on rupture area are within a factor of two, much lower than typical stress drop uncertainty, despite a suboptimal station distribution. The rupture properties suggest that there is little difference between the M2 Oklahoma earthquake and typical large earthquakes. The new three component nodal systems have great potential for improving the resolution of studies of earthquake source properties.

  9. Decoding communities in networks

    Science.gov (United States)

    Radicchi, Filippo

    2018-02-01

    According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

  10. Decoding communities in networks.

    Science.gov (United States)

    Radicchi, Filippo

    2018-02-01

    According to a recent information-theoretical proposal, the problem of defining and identifying communities in networks can be interpreted as a classical communication task over a noisy channel: memberships of nodes are information bits erased by the channel, edges and nonedges in the network are parity bits introduced by the encoder but degraded through the channel, and a community identification algorithm is a decoder. The interpretation is perfectly equivalent to the one at the basis of well-known statistical inference algorithms for community detection. The only difference in the interpretation is that a noisy channel replaces a stochastic network model. However, the different perspective gives the opportunity to take advantage of the rich set of tools of coding theory to generate novel insights on the problem of community detection. In this paper, we illustrate two main applications of standard coding-theoretical methods to community detection. First, we leverage a state-of-the-art decoding technique to generate a family of quasioptimal community detection algorithms. Second and more important, we show that the Shannon's noisy-channel coding theorem can be invoked to establish a lower bound, here named as decodability bound, for the maximum amount of noise tolerable by an ideal decoder to achieve perfect detection of communities. When computed for well-established synthetic benchmarks, the decodability bound explains accurately the performance achieved by the best community detection algorithms existing on the market, telling us that only little room for their improvement is still potentially left.

  11. Community Seismic Network (CSN)

    Science.gov (United States)

    Clayton, R. W.; Heaton, T. H.; Kohler, M. D.; Cheng, M.; Guy, R.; Chandy, M.; Krause, A.; Bunn, J.; Olson, M.; Faulkner, M.; Liu, A.; Strand, L.

    2012-12-01

    We report on developments in sensor connectivity, architecture, and data fusion algorithms executed in Cloud computing systems in the Community Seismic Network (CSN), a network of low-cost sensors housed in homes and offices by volunteers in the Pasadena, CA area. The network has over 200 sensors continuously reporting anomalies in local acceleration through the Internet to a Cloud computing service (the Google App Engine) that continually fuses sensor data to rapidly detect shaking from earthquakes. The Cloud computing system consists of data centers geographically distributed across the continent and is likely to be resilient even during earthquakes and other local disasters. The region of Southern California is partitioned in a multi-grid style into sets of telescoping cells called geocells. Data streams from sensors within a geocell are fused to detect anomalous shaking across the geocell. Temporal spatial patterns across geocells are used to detect anomalies across regions. The challenge is to detect earthquakes rapidly with an extremely low false positive rate. We report on two data fusion algorithms, one that tessellates the surface so as to fuse data from a large region around Pasadena and the other, which uses a standard tessellation of equal-sized cells. Since September 2011, the network has successfully detected earthquakes of magnitude 2.5 or higher within 40 Km of Pasadena. In addition to the standard USB device, which connects to the host's computer, we have developed a stand-alone sensor that directly connects to the internet via Ethernet or wifi. This bypasses security concerns that some companies have with the USB-connected devices, and allows for 24/7 monitoring at sites that would otherwise shut down their computers after working hours. In buildings we use the sensors to model the behavior of the structures during weak events in order to understand how they will perform during strong events. Visualization models of instrumented buildings ranging

  12. Community detection using preference networks

    Science.gov (United States)

    Tasgin, Mursel; Bingol, Haluk O.

    2018-04-01

    Community detection is the task of identifying clusters or groups of nodes in a network where nodes within the same group are more connected with each other than with nodes in different groups. It has practical uses in identifying similar functions or roles of nodes in many biological, social and computer networks. With the availability of very large networks in recent years, performance and scalability of community detection algorithms become crucial, i.e. if time complexity of an algorithm is high, it cannot run on large networks. In this paper, we propose a new community detection algorithm, which has a local approach and is able to run on large networks. It has a simple and effective method; given a network, algorithm constructs a preference network of nodes where each node has a single outgoing edge showing its preferred node to be in the same community with. In such a preference network, each connected component is a community. Selection of the preferred node is performed using similarity based metrics of nodes. We use two alternatives for this purpose which can be calculated in 1-neighborhood of nodes, i.e. number of common neighbors of selector node and its neighbors and, the spread capability of neighbors around the selector node which is calculated by the gossip algorithm of Lind et.al. Our algorithm is tested on both computer generated LFR networks and real-life networks with ground-truth community structure. It can identify communities accurately in a fast way. It is local, scalable and suitable for distributed execution on large networks.

  13. Nuclear Community in network

    International Nuclear Information System (INIS)

    Tejedor, E.

    2014-01-01

    The internet has revolutionized the ways of communication and many companies/ organizations have adapted to the change but others have not, or have done it halfway. This presentation is a review of the main characteristics of virtual communities and their typology. The status of the Nuclear Online Community, both pro nuclear and antinuclear is analysed , and their main similarities and differences are discussed. The Pro nuclear Online Community is formed gradually. This presentation attempts to define some ways to increase the scope of the Community and encourage greater dissemination of the characteristics of nuclear energy. (Author)

  14. Networking the rural community.

    Science.gov (United States)

    Tiongson, K H; Arneson, S I

    1993-04-01

    A branch network of affiliate hospitals has been providing home care services to rural North Dakota residents successfully for a decade. Here's how this effective system meets the special challenges that a rural environment poses for hiring, training, scheduling, and supporting home care aides.

  15. An evolving network model with community structure

    International Nuclear Information System (INIS)

    Li Chunguang; Maini, Philip K

    2005-01-01

    Many social and biological networks consist of communities-groups of nodes within which connections are dense, but between which connections are sparser. Recently, there has been considerable interest in designing algorithms for detecting community structures in real-world complex networks. In this paper, we propose an evolving network model which exhibits community structure. The network model is based on the inner-community preferential attachment and inter-community preferential attachment mechanisms. The degree distributions of this network model are analysed based on a mean-field method. Theoretical results and numerical simulations indicate that this network model has community structure and scale-free properties

  16. Searching for Communities in Bipartite Networks

    OpenAIRE

    Barber, Michael J.; Faria, Margarida; Streit, Ludwig; Strogan, Oleg

    2008-01-01

    Bipartite networks are a useful tool for representing and investigating interaction networks. We consider methods for identifying communities in bipartite networks. Intuitive notions of network community groups are made explicit using Newman's modularity measure. A specialized version of the modularity, adapted to be appropriate for bipartite networks, is presented; a corresponding algorithm is described for identifying community groups through maximizing this measure. The algorithm is applie...

  17. Epidemics in adaptive networks with community structure

    Science.gov (United States)

    Shaw, Leah; Tunc, Ilker

    2010-03-01

    Models for epidemic spread on static social networks do not account for changes in individuals' social interactions. Recent studies of adaptive networks have modeled avoidance behavior, as non-infected individuals try to avoid contact with infectives. Such models have not generally included realistic social structure. Here we study epidemic spread on an adaptive network with community structure. We model the effect of heterogeneous communities on infection levels and epidemic extinction. We also show how an epidemic can alter the community structure.

  18. Effects of multiple spreaders in community networks

    Science.gov (United States)

    Hu, Zhao-Long; Ren, Zhuo-Ming; Yang, Guang-Yong; Liu, Jian-Guo

    2014-12-01

    Human contact networks exhibit the community structure. Understanding how such community structure affects the epidemic spreading could provide insights for preventing the spreading of epidemics between communities. In this paper, we explore the spreading of multiple spreaders in community networks. A network based on the clustering preferential mechanism is evolved, whose communities are detected by the Girvan-Newman (GN) algorithm. We investigate the spreading effectiveness by selecting the nodes as spreaders in the following ways: nodes with the largest degree in each community (community hubs), the same number of nodes with the largest degree from the global network (global large-degree) and randomly selected one node within each community (community random). The experimental results on the SIR model show that the spreading effectiveness based on the global large-degree and community hubs methods is the same in the early stage of the infection and the method of community random is the worst. However, when the infection rate exceeds the critical value, the global large-degree method embodies the worst spreading effectiveness. Furthermore, the discrepancy of effectiveness for the three methods will decrease as the infection rate increases. Therefore, we should immunize the hubs in each community rather than those hubs in the global network to prevent the outbreak of epidemics.

  19. Sociospatial Knowledge Networks: Appraising Community as Place.

    Science.gov (United States)

    Skelly, Anne H.; Arcury, Thomas A.; Gesler, Wilbert M.; Cravey, Altha J.; Dougherty, Molly C.; Washburn, Sarah A.; Nash, Sally

    2002-01-01

    A new theory of geographical analysis--sociospatial knowledge networks--provides a framework for understanding the social and spatial locations of a community's health knowledge and beliefs. This theory is guiding an ethnographic study of health beliefs, knowledge, and knowledge networks in a diverse rural community at high risk for type-2…

  20. Fast unfolding of communities in large networks

    International Nuclear Information System (INIS)

    Blondel, Vincent D; Guillaume, Jean-Loup; Lambiotte, Renaud; Lefebvre, Etienne

    2008-01-01

    We propose a simple method to extract the community structure of large networks. Our method is a heuristic method that is based on modularity optimization. It is shown to outperform all other known community detection methods in terms of computation time. Moreover, the quality of the communities detected is very good, as measured by the so-called modularity. This is shown first by identifying language communities in a Belgian mobile phone network of 2 million customers and by analysing a web graph of 118 million nodes and more than one billion links. The accuracy of our algorithm is also verified on ad hoc modular networks

  1. Assessing Community Informatics: A Review of Methodological Approaches for Evaluating Community Networks and Community Technology Centers.

    Science.gov (United States)

    O'Neil, Dara

    2002-01-01

    Analyzes the emerging community informatics evaluation literature to develop an understanding of the indicators used to gauge project impacts in community networks and community technology centers. The study finds that community networks and community technology center assessments fall into five key areas: strong democracy; social capital;…

  2. Consumer engagement in social networks brand community

    OpenAIRE

    Rybakovas, Paulius

    2016-01-01

    Consumers increasingly integrate social media into their day-to-day lives. For companies consumer engagement in a brand community on social network is becoming increasingly important for developing relations with consumers. Consumer engagement in a brand community on social network creates a dynamic relationship between the community members and the brand which contributes to an increase in consumer loyalty to the brand. The literature is abundant of studies, which examines the consumer engag...

  3. Networks : Empowering Communities through Telecentre Networking

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

    ... Egypt, Syria, Lebanon and Morocco - have pulled together in loose networks for peer support. ... IDRC “unpacks women's empowerment” at McGill University Conference ... New funding opportunity for gender equality and climate change.

  4. Finding overlapping communities in multilayer networks.

    Science.gov (United States)

    Liu, Weiyi; Suzumura, Toyotaro; Ji, Hongyu; Hu, Guangmin

    2018-01-01

    Finding communities in multilayer networks is a vital step in understanding the structure and dynamics of these layers, where each layer represents a particular type of relationship between nodes in the natural world. However, most community discovery methods for multilayer networks may ignore the interplay between layers or the unique topological structure in a layer. Moreover, most of them can only detect non-overlapping communities. In this paper, we propose a new community discovery method for multilayer networks, which leverages the interplay between layers and the unique topology in a layer to reveal overlapping communities. Through a comprehensive analysis of edge behaviors within and across layers, we first calculate the similarities for edges from the same layer and the cross layers. Then, by leveraging these similarities, we can construct a dendrogram for the multilayer networks that takes both the unique topological structure and the important interplay into consideration. Finally, by introducing a new community density metric for multilayer networks, we can cut the dendrogram to get the overlapping communities for these layers. By applying our method on both synthetic and real-world datasets, we demonstrate that our method has an accurate performance in discovering overlapping communities in multilayer networks.

  5. Creative Network Communities in the Translocal Space of Digital Networks

    Directory of Open Access Journals (Sweden)

    Rasa Smite

    2013-01-01

    Full Text Available What should sociological research be in the age of Web 2.0? Considering that the task of “network sociology” is not only empirical research but also the interpretation of tendencies of the network culture, this research explores the rise of network communities within Eastern and Western Europe in the early Internet era. I coined the term creative networks to distinguish these early creative and social activities from today’s popular social networking. Thus I aimed to interpret the meaning of social action; the motivation of creative community actors, their main fields of activities and social organization forms; and the potential that these early developments contain for the future sustainability of networks. Data comprise interviews with networking experts and founders and members of various networks. Investigating respondents’ motivations for creating online networks and communities, and interpreting those terms, allows for comparing the creative networks of the 1990s with today’s social networks and for drawing conclusions.

  6. Community Based Networks and 5G

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2016-01-01

    The deployment of previous wireless standards has provided more benefits for urban dwellers than rural dwellers. 5G deployment may not be different. This paper identifies that Community Based Networks as carriers that deserve recognition as potential 5G providers may change this. The argument....... The findings indicate that 5G connectivity can be extended to rural areas by these networks, via heterogenous networks. Hence the delivery of 5G data rates delivery via Wireless WAN in rural areas can be achieved by utilizing the causal factors of the identified models for Community Based Networks....

  7. Using Social Network Analysis to Evaluate Community Capacity Building of a Regional Community Cancer Network

    Science.gov (United States)

    Luque, John; Tyson, Dinorah Martinez; Lee, Ji-Hyun; Gwede, Clement; Vadaparampil, Susan; Noel-Thomas, Shalewa; Meade, Cathy

    2010-01-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of 25 Community Network Programs funded by the National Cancer Institute's (NCI's) Center to Reduce Cancer Health Disparities with the objectives to create a collaborative infrastructure of academic and community based organizations and to develop effective and sustainable interventions to…

  8. Immunization of networks with community structure

    International Nuclear Information System (INIS)

    Masuda, Naoki

    2009-01-01

    In this study, an efficient method to immunize modular networks (i.e. networks with community structure) is proposed. The immunization of networks aims at fragmenting networks into small parts with a small number of removed nodes. Its applications include prevention of epidemic spreading, protection against intentional attacks on networks, and conservation of ecosystems. Although preferential immunization of hubs is efficient, good immunization strategies for modular networks have not been established. On the basis of an immunization strategy based on eigenvector centrality, we develop an analytical framework for immunizing modular networks. To this end, we quantify the contribution of each node to the connectivity in a coarse-grained network among modules. We verify the effectiveness of the proposed method by applying it to model and real networks with modular structure.

  9. Brand communities embedded in social networks.

    Science.gov (United States)

    Zaglia, Melanie E

    2013-02-01

    Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social networks. The netnographic approach yields strong evidence of this existence; leading to a better understanding of such embedded brand communities, their peculiarities, and motivational drivers for participation; therefore the findings contribute to theory by combining two separate research streams. Due to the advantages of social networks, brand management is now able to implement brand communities with less time and financial effort; however, choosing the appropriate brand community type, cultivating consumers' interaction, and staying tuned to this social engagement are critical factors to gain anticipated brand outcomes.

  10. Social network analysis community detection and evolution

    CERN Document Server

    Missaoui, Rokia

    2015-01-01

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

  11. Network communities within and across borders

    OpenAIRE

    Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

    2014-01-01

    We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by t...

  12. Brand communities embedded in social networks ?

    OpenAIRE

    Zaglia, Melanie E.

    2013-01-01

    Brand communities represent highly valuable marketing, innovation management, and customer relationship management tools. However, applying successful marketing strategies today, and in the future, also means exploring and seizing the unprecedented opportunities of social network environments. This study combines these two social phenomena which have largely been researched separately, and aims to investigate the existence, functionality and different types of brand communities within social ...

  13. Community Core Evolution in Mobile Social Networks

    Directory of Open Access Journals (Sweden)

    Hao Xu

    2013-01-01

    Full Text Available Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

  14. Community core evolution in mobile social networks.

    Science.gov (United States)

    Xu, Hao; Xiao, Weidong; Tang, Daquan; Tang, Jiuyang; Wang, Zhenwen

    2013-01-01

    Community detection in social networks attracts a lot of attention in the recent years. Existing methods always depict the relationship of two nodes using the temporary connection. However, these temporary connections cannot be fully recognized as the real relationships when the history connections among nodes are considered. For example, a casual visit in Facebook cannot be seen as an establishment of friendship. Hence, our question is the following: how to cluster the real friends in mobile social networks? In this paper, we study the problem of detecting the stable community core in mobile social networks. The cumulative stable contact is proposed to depict the relationship among nodes. The whole process is divided into timestamps. Nodes and their connections can be added or removed at each timestamp, and historical contacts are considered when detecting the community core. Also, community cores can be tracked through the incremental computing, which can help to recognize the evolving of community structure. Empirical studies on real-world social networks demonstrate that our proposed method can effectively detect stable community cores in mobile social networks.

  15. 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.

  16. Epidemic spreading on complex networks with community structures

    NARCIS (Netherlands)

    Stegehuis, C.; van der Hofstad, R.W.; van Leeuwaarden, J.S.H.

    2016-01-01

    Many real-world networks display a community structure. We study two random graph models that create a network with similar community structure as a given network. One model preserves the exact community structure of the original network, while the other model only preserves the set of communities

  17. Information transfer in community structured multiplex networks

    Directory of Open Access Journals (Sweden)

    Albert eSolé Ribalta

    2015-08-01

    Full Text Available The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.. The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  18. Information transfer in community structured multiplex networks

    Science.gov (United States)

    Solé Ribalta, Albert; Granell, Clara; Gómez, Sergio; Arenas, Alex

    2015-08-01

    The study of complex networks that account for different types of interactions has become a subject of interest in the last few years, specially because its representational power in the description of users interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.). The mathematical description of these interacting networks has been coined under the name of multilayer networks, where each layer accounts for a type of interaction. It has been shown that diffusive processes on top of these networks present a phenomenology that cannot be explained by the naive superposition of single layer diffusive phenomena but require the whole structure of interconnected layers. Nevertheless, the description of diffusive phenomena on multilayer networks has obviated the fact that social networks have strong mesoscopic structure represented by different communities of individuals driven by common interests, or any other social aspect. In this work, we study the transfer of information in multilayer networks with community structure. The final goal is to understand and quantify, if the existence of well-defined community structure at the level of individual layers, together with the multilayer structure of the whole network, enhances or deteriorates the diffusion of packets of information.

  19. Oklahoma Tribes: A History

    Science.gov (United States)

    Gover, Kevin

    1977-01-01

    Oklahoma is a microcosm of American Indian country. Water rights, tribal government impotence, jurisdiction, tribal membership, treaty rights, taxation, sovereignty, racism, and poor housing, education, and health are all vital issues facing the Indian tribes of Oklahoma. In order to understand the complexity of these issues, a review of the…

  20. Network communities within and across borders.

    Science.gov (United States)

    Cerina, Federica; Chessa, Alessandro; Pammolli, Fabio; Riccaboni, Massimo

    2014-04-01

    We investigate the impact of borders on the topology of spatially embedded networks. Indeed territorial subdivisions and geographical borders significantly hamper the geographical span of networks thus playing a key role in the formation of network communities. This is especially important in scientific and technological policy-making, highlighting the interplay between pressure for the internationalization to lead towards a global innovation system and the administrative borders imposed by the national and regional institutions. In this study we introduce an outreach index to quantify the impact of borders on the community structure and apply it to the case of the European and US patent co-inventors networks. We find that (a) the US connectivity decays as a power of distance, whereas we observe a faster exponential decay for Europe; (b) European network communities essentially correspond to nations and contiguous regions while US communities span multiple states across the whole country without any characteristic geographic scale. We confirm our findings by means of a set of simulations aimed at exploring the relationship between different patterns of cross-border community structures and the outreach index.

  1. Exploratory community sensing in social networks

    Science.gov (United States)

    Khrabrov, Alexy; Stocco, Gabriel; Cybenko, George

    2010-04-01

    Social networks generally provide an implementation of some kind of groups or communities which users can voluntarily join. Twitter does not have this functionality, and there is no notion of a formal group or community. We propose a method for identification of communities and assignment of semantic meaning to the discussion topics of the resulting communities. Using this analysis method and a sample of roughly a month's worth of Tweets from Twitter's "gardenhose" feed, we demonstrate the discovery of meaningful user communities on Twitter. We examine Twitter data streaming in real time and treat it as a sensor. Twitter is a social network which pioneered microblogging with the messages fitting an SMS, and a variety of clients, browsers, smart phones and PDAs are used for status updates by individuals, businesses, media outlets and even devices all over the world. Often an aggregate trend of such statuses may represent an important development in the world, which has been demonstrated with the Iran and Moldova elections and the anniversary of the Tiananmen in China. We propose using Twitter as a sensor, tracking individuals and communities of interest, and characterizing individual roles and dynamics of their communications. We developed a novel algorithm of community identification in social networks based on direct communication, as opposed to linking. We show ways to find communities of interest and then browse their neighborhoods by either similarity or diversity of individuals and groups adjacent to the one of interest. We use frequent collocations and statistically improbable phrases to summarize the focus of the community, giving a quick overview of its main topics. Our methods provide insight into the largest social sensor network in the world and constitute a platform for social sensing.

  2. 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.

  3. Finding local communities in protein networks.

    Science.gov (United States)

    Voevodski, Konstantin; Teng, Shang-Hua; Xia, Yu

    2009-09-18

    Protein-protein interactions (PPIs) play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent, making our application useful for biologists who wish to

  4. Finding local communities in protein networks

    Directory of Open Access Journals (Sweden)

    Teng Shang-Hua

    2009-09-01

    Full Text Available Abstract Background Protein-protein interactions (PPIs play fundamental roles in nearly all biological processes, and provide major insights into the inner workings of cells. A vast amount of PPI data for various organisms is available from BioGRID and other sources. The identification of communities in PPI networks is of great interest because they often reveal previously unknown functional ties between proteins. A large number of global clustering algorithms have been applied to protein networks, where the entire network is partitioned into clusters. Here we take a different approach by looking for local communities in PPI networks. Results We develop a tool, named Local Protein Community Finder, which quickly finds a community close to a queried protein in any network available from BioGRID or specified by the user. Our tool uses two new local clustering algorithms Nibble and PageRank-Nibble, which look for a good cluster among the most popular destinations of a short random walk from the queried vertex. The quality of a cluster is determined by proportion of outgoing edges, known as conductance, which is a relative measure particularly useful in undersampled networks. We show that the two local clustering algorithms find communities that not only form excellent clusters, but are also likely to be biologically relevant functional components. We compare the performance of Nibble and PageRank-Nibble to other popular and effective graph partitioning algorithms, and show that they find better clusters in the graph. Moreover, Nibble and PageRank-Nibble find communities that are more functionally coherent. Conclusion The Local Protein Community Finder, accessible at http://xialab.bu.edu/resources/lpcf, allows the user to quickly find a high-quality community close to a queried protein in any network available from BioGRID or specified by the user. We show that the communities found by our tool form good clusters and are functionally coherent

  5. Network Community Detection on Metric Space

    Directory of Open Access Journals (Sweden)

    Suman Saha

    2015-08-01

    Full Text Available Community detection in a complex network is an important problem of much interest in recent years. In general, a community detection algorithm chooses an objective function and captures the communities of the network by optimizing the objective function, and then, one uses various heuristics to solve the optimization problem to extract the interesting communities for the user. In this article, we demonstrate the procedure to transform a graph into points of a metric space and develop the methods of community detection with the help of a metric defined for a pair of points. We have also studied and analyzed the community structure of the network therein. The results obtained with our approach are very competitive with most of the well-known algorithms in the literature, and this is justified over the large collection of datasets. On the other hand, it can be observed that time taken by our algorithm is quite less compared to other methods and justifies the theoretical findings.

  6. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    We study the problem of identifying and ranking the members of a community in a very large network with link analysis only, given a set of representatives of the community. We define the concept of a community justified by a formal analysis of a simple model of the evolution of a directed graph. ...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  7. Governance Mechanisms in Food Community Networks

    NARCIS (Netherlands)

    Pascucci, S.; Lombardi, A.; Cembalo, L.; Dentoni, D.

    2013-01-01

    This paper discusses the concept of the food community network (FCN) and how consumers and farmers organize credence food transactions. The FCN is based on pooling specific resources and using membership-based contracts to assign decision and property rights. It implies an organization based on a

  8. Insights into chemotaxonomic composition and carbon cycling of phototrophic communities in an artesian sulfur-rich spring (Zodletone, Oklahoma, USA), a possible analog for ancient microbial mat systems.

    Science.gov (United States)

    Bühring, S I; Sievert, S M; Jonkers, H M; Ertefai, T; Elshahed, M S; Krumholz, L R; Hinrichs, K-U

    2011-03-01

    Zodletone spring in Oklahoma is a unique environment with high concentrations of dissolved-sulfide (10 mm) and short-chain gaseous alkanes, exhibiting characteristics that are reminiscent of conditions that are thought to have existed in Earth's history, in particular the late Archean and early-to-mid Proterozoic. Here, we present a process-oriented investigation of the microbial community in two distinct mat formations at the spring source, (1) the top of the sediment in the source pool and (2) the purple streamers attached to the side walls. We applied a combination of pigment and lipid biomarker analyses, while functional activities were investigated in terms of oxygen production (microsensor analysis) and carbon utilization ((13)C incorporation experiments). Pigment analysis showed cyanobacterial pigments, in addition to pigments from purple sulfur bacteria (PSB), green sulfur bacteria (GSB) and Chloroflexus-like bacteria (CLB). Analysis of intact polar lipids (IPLs) in the source sediment confirmed the presence of phototrophic organisms via diacylglycerol phospholipids and betaine lipids, whereas glyceroldialkylglyceroltetraether additionally indicated the presence of archaea. No archaeal IPLs were found in the purple streamers, which were strongly dominated by betaine lipids. (13)C-bicarbonate- and -acetate-labeling experiments indicated cyanobacteria as predominant phototrophs in the source sediment, carbon was actively fixed by PSB/CLB/GSB in purple streamers by using near infrared light. Despite the presence of cyanobacteria, no oxygen could be detected in the presence of light, suggesting anoxygenic photosynthesis as the major metabolic process at this site. Our investigations furthermore indicated photoheterotrophy as an important process in both habitats. We obtained insights into a syntrophically operating phototrophic community in an ecosystem that bears resemblance to early Earth conditions, where cyanobacteria constitute an important contributor to

  9. Social network fragmentation and community health.

    Science.gov (United States)

    Chami, Goylette F; Ahnert, Sebastian E; Kabatereine, Narcis B; Tukahebwa, Edridah M

    2017-09-05

    Community health interventions often seek to intentionally destroy paths between individuals to prevent the spread of infectious diseases. Immunizing individuals through direct vaccination or the provision of health education prevents pathogen transmission and the propagation of misinformation concerning medical treatments. However, it remains an open question whether network-based strategies should be used in place of conventional field approaches to target individuals for medical treatment in low-income countries. We collected complete friendship and health advice networks in 17 rural villages of Mayuge District, Uganda. Here we show that acquaintance algorithms, i.e., selecting neighbors of randomly selected nodes, were systematically more efficient in fragmenting all networks than targeting well-established community roles, i.e., health workers, village government members, and schoolteachers. Additionally, community roles were not good proxy indicators of physical proximity to other households or connections to many sick people. We also show that acquaintance algorithms were effective in offsetting potential noncompliance with deworming treatments for 16,357 individuals during mass drug administration (MDA). Health advice networks were destroyed more easily than friendship networks. Only an average of 32% of nodes were removed from health advice networks to reduce the percentage of nodes at risk for refusing treatment in MDA to below 25%. Treatment compliance of at least 75% is needed in MDA to control human morbidity attributable to parasitic worms and progress toward elimination. Our findings point toward the potential use of network-based approaches as an alternative to role-based strategies for targeting individuals in rural health interventions.

  10. Cooperation in the prisoner's dilemma game on tunable community networks

    Science.gov (United States)

    Liu, Penghui; Liu, Jing

    2017-04-01

    Community networks have attracted lots of attention as they widely exist in the real world and are essential to study properties of networks. As the game theory illustrates the competitive relationship among individuals, studying the iterated prisoner's dilemma games (PDG) on community networks is meaningful. In this paper, we focus on investigating the relationship between the cooperation level of community networks and that of their communities in the prisoner's dilemma games. With this purpose in mind, a type of tunable community networks whose communities inherit not only the scale-free property, but also the characteristic of adjustable cooperation level of Holme and Kim (HK) networks is designed. Both uniform and non-uniform community networks are investigated. We find out that cooperation enhancement of communities can improve the cooperation level of the whole networks. Moreover, simulation results indicate that a large community is a better choice than a small community to improve the cooperation level of the whole networks. Thus, improving the cooperation level of community networks can be divided into a number of sub-problems targeting at improving the cooperation level of individual communities, which can save the computation cost and deal with the problem of improving the cooperation level of huge community networks. Moreover, as the larger community is a better choice, it is reasonable to start with large communities, according to the greedy strategy when the number of nodes can participate in the enhancement is limited.

  11. Overlapping community detection in weighted networks via a Bayesian approach

    Science.gov (United States)

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

    2017-02-01

    Complex networks as a powerful way to represent complex systems have been widely studied during the past several years. One of the most important tasks of complex network analysis is to detect communities embedded in networks. In the real world, weighted networks are very common and may contain overlapping communities where a node is allowed to belong to multiple communities. In this paper, we propose a novel Bayesian approach, called the Bayesian mixture network (BMN) model, to detect overlapping communities in weighted networks. The advantages of our method are (i) providing soft-partition solutions in weighted networks; (ii) providing soft memberships, which quantify 'how strongly' a node belongs to a community. Experiments on a large number of real and synthetic networks show that our model has the ability in detecting overlapping communities in weighted networks and is competitive with other state-of-the-art models at shedding light on community partition.

  12. Program Spotlight: National Outreach Network's Community Health Educators

    Science.gov (United States)

    National Outreach Network of Community Health Educators located at Community Network Program Centers, Partnerships to Advance Cancer Health Equity, and NCI-designated cancer centers help patients and their families receive survivorship support.

  13. Mapping the ecological networks of microbial communities.

    Science.gov (United States)

    Xiao, Yandong; Angulo, Marco Tulio; Friedman, Jonathan; Waldor, Matthew K; Weiss, Scott T; Liu, Yang-Yu

    2017-12-11

    Mapping the ecological networks of microbial communities is a necessary step toward understanding their assembly rules and predicting their temporal behavior. However, existing methods require assuming a particular population dynamics model, which is not known a priori. Moreover, those methods require fitting longitudinal abundance data, which are often not informative enough for reliable inference. To overcome these limitations, here we develop a new method based on steady-state abundance data. Our method can infer the network topology and inter-taxa interaction types without assuming any particular population dynamics model. Additionally, when the population dynamics is assumed to follow the classic Generalized Lotka-Volterra model, our method can infer the inter-taxa interaction strengths and intrinsic growth rates. We systematically validate our method using simulated data, and then apply it to four experimental data sets. Our method represents a key step towards reliable modeling of complex, real-world microbial communities, such as the human gut microbiota.

  14. Facebook: Networking the Community of Society

    DEFF Research Database (Denmark)

    Tække, Jesper

    The article examines the significance of new "social media" like Facebook for the way we socialize, develop social identity, and shape society. Based on the work of Luhmann, the article proposes that community communication is fundamental to the selfregulation of our society and that this type...... but that also may pose certain risks for modern society and for the development and maintenance of social identity. The article argues that communication through and about status updates on Facebook may be categorized as network communication, and finally it discusses whether and to what extent this kind...... of communication also provides the basis for the formation and maintenance of people’s social identity, so that they and society are in harmony. In contrast to community communication, the article explores the notion of network communication, which is classified as communication that may have some positive effects...

  15. Vulnerability of R-MAT networks with communities

    Directory of Open Access Journals (Sweden)

    Nikolay Alexandrovich Kinash

    2016-06-01

    Full Text Available A generator R-MAT for modeling networks with different laws of link constructions within and between communities has been developed. Network attack simulations have been performed and pertinent robustness of diverse network combinations has been concluded.

  16. Phase synchronization on small-world networks with community structure

    International Nuclear Information System (INIS)

    Xiao-Hua, Wang; Li-Cheng, Jiao; Jian-She, Wu

    2010-01-01

    In this paper, we propose a simple model that can generate small-world network with community structure. The network is introduced as a tunable community organization with parameter r, which is directly measured by the ratio of inter- to intra-community connectivity, and a smaller r corresponds to a stronger community structure. The structure properties, including the degree distribution, clustering, the communication efficiency and modularity are also analysed for the network. In addition, by using the Kuramoto model, we investigated the phase synchronization on this network, and found that increasing the fuzziness of community structure will markedly enhance the network synchronizability; however, in an abnormal region (r ≤ 0.001), the network has even worse synchronizability than the case of isolated communities (r = 0). Furthermore, this network exhibits a remarkable synchronization behaviour in topological scales: the oscillators of high densely interconnected communities synchronize more easily, and more rapidly than the whole network. (general)

  17. Identification of hybrid node and link communities in complex networks.

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-02

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  18. Identification of hybrid node and link communities in complex networks

    Science.gov (United States)

    He, Dongxiao; Jin, Di; Chen, Zheng; Zhang, Weixiong

    2015-03-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately.

  19. Network-Based Community Brings forth Sustainable Society

    Science.gov (United States)

    Kikuchi, Toshiko

    It has already been shown that an artificial society based on the three relations of social configuration (market, communal, and obligatory relations) functioning in balance with each other formed a sustainable society which the social reproduction is possible. In this artificial society model, communal relations exist in a network-based community with alternating members rather than a conventional community with cooperative mutual assistance practiced in some agricultural communities. In this paper, using the comparison between network-based communities with alternating members and conventional communities with fixed members, the significance of a network-based community is considered. In concrete terms, the difference in appearance rate for sustainable society, economic activity and asset inequality between network-based communities and conventional communities is analyzed. The appearance rate for a sustainable society of network-based community is higher than that of conventional community. Moreover, most of network-based communities had a larger total number of trade volume than conventional communities. But, the value of Gini coefficient in conventional community is smaller than that of network-based community. These results show that communal relations based on a network-based community is significant for the social reproduction and economic efficiency. However, in such an artificial society, the inequality is sacrificed.

  20. A divisive spectral method for network community detection

    International Nuclear Information System (INIS)

    Cheng, Jianjun; Li, Longjie; Yao, Yukai; Chen, Xiaoyun; Leng, Mingwei; Lu, Weiguo

    2016-01-01

    Community detection is a fundamental problem in the domain of complex network analysis. It has received great attention, and many community detection methods have been proposed in the last decade. In this paper, we propose a divisive spectral method for identifying community structures from networks which utilizes a sparsification operation to pre-process the networks first, and then uses a repeated bisection spectral algorithm to partition the networks into communities. The sparsification operation makes the community boundaries clearer and sharper, so that the repeated spectral bisection algorithm extract high-quality community structures accurately from the sparsified networks. Experiments show that the combination of network sparsification and a spectral bisection algorithm is highly successful, the proposed method is more effective in detecting community structures from networks than the others. (paper: interdisciplinary statistical mechanics)

  1. A new hierarchical method to find community structure in networks

    Science.gov (United States)

    Saoud, Bilal; Moussaoui, Abdelouahab

    2018-04-01

    Community structure is very important to understand a network which represents a context. Many community detection methods have been proposed like hierarchical methods. In our study, we propose a new hierarchical method for community detection in networks based on genetic algorithm. In this method we use genetic algorithm to split a network into two networks which maximize the modularity. Each new network represents a cluster (community). Then we repeat the splitting process until we get one node at each cluster. We use the modularity function to measure the strength of the community structure found by our method, which gives us an objective metric for choosing the number of communities into which a network should be divided. We demonstrate that our method are highly effective at discovering community structure in both computer-generated and real-world network data.

  2. Hydrogeology and simulation of groundwater flow in the Central Oklahoma (Garber-Wellington) Aquifer, Oklahoma, 1987 to 2009, and simulation of available water in storage, 2010–2059

    Science.gov (United States)

    Mashburn, Shana L.; Ryter, Derek W.; Neel, Christopher R.; Smith, S. Jerrod; Magers, Jessica S.

    2014-02-10

    The Central Oklahoma (Garber-Wellington) aquifer underlies about 3,000 square miles of central Oklahoma. The study area for this investigation was the extent of the Central Oklahoma aquifer. Water from the Central Oklahoma aquifer is used for public, industrial, commercial, agricultural, and domestic supply. With the exception of Oklahoma City, all of the major communities in central Oklahoma rely either solely or partly on groundwater from this aquifer. The Oklahoma City metropolitan area, incorporating parts of Canadian, Cleveland, Grady, Lincoln, Logan, McClain, and Oklahoma Counties, has a population of approximately 1.2 million people. As areas are developed for groundwater supply, increased groundwater withdrawals may result in decreases in long-term aquifer storage. The U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, investigated the hydrogeology and simulated groundwater flow in the aquifer using a numerical groundwater-flow model. The purpose of this report is to describe an investigation of the Central Oklahoma aquifer that included analyses of the hydrogeology, hydrogeologic framework of the aquifer, and construction of a numerical groundwater-flow model. The groundwater-flow model was used to simulate groundwater levels and for water-budget analysis. A calibrated transient model was used to evaluate changes in groundwater storage associated with increased future water demands.

  3. Community Health Global Network and Sustainable Development

    Directory of Open Access Journals (Sweden)

    Rebekah Young

    2016-01-01

    Full Text Available With the achievements, failures and passing of the Millennium Development Goals (MDG, the world has turned its eyes to the Sustainable Development Goals (SDG, designed to foster sustainable social, economic and environmental development over the next 15 years.(1 Community-led initiatives are increasingly being recognised as playing a key role in realising sustainable community development and in the aspirations of universal healthcare.(2 In many parts of the world, faith-based organisations are some of the main players in community-led development and health care.(3 Community Health Global Network (CHGN creates links between organisations, with the purpose being to encourage communities to recognise their assets and abilities, identify shared concerns and discover solutions together, in order to define and lead their futures in sustainable ways.(4 CHGN has facilitated the development of collaborative groups of health and development initiatives called ‘Clusters’ in several countries including India, Bangladesh, Kenya, Tanzania, Zambia and Myanmar. In March 2016 these Clusters met together in an International Forum, to share learnings, experiences, challenges, achievements and to encourage one another. Discussions held throughout the forum suggest that the CHGN model is helping to promote effective, sustainable development and health care provision on both a local and a global scale.

  4. Community energy systems and the law of public utilities. Volume thirty-eight. Oklahoma. Final report of a study of the impacts of regulations affecting the acceptance of integrated community energy systems

    Energy Technology Data Exchange (ETDEWEB)

    Feurer, D.A.; Weaver, C.L.

    1981-01-01

    A detailed description is given of the laws and programs of the State of Oklahoma governing the regulation of public energy utilities, the siting of energy generating and transmission facilities, the municipal franchising of public energy utilities, and the prescription of rates to be charged by utilities including attendant problems of cost allocations, rate base and operating expense determinations, and rate of return allowances. These laws and programs are analyzed to identify impediments which they may present to the implementation of Integrated Community Energy Systems (ICES). This report is one of fifty-one separate volumes which describe such regulatory programs at the Federal level and in each state as background to the report entitled Community Energy Systems and the Law of Public Utilities, Volume One: An Overview. This report also contains a summary of a strategy described in Volume One: An Overview for overcoming these impediments by working within the existing regulatory framework and by making changes in the regulatory programs to enhance the likelihood of ICES implementation.

  5. Research on Community Structure in Bus Transport Networks

    International Nuclear Information System (INIS)

    Yang Xuhua; Wang Bo; Sun Youxian

    2009-01-01

    We abstract the bus transport networks (BTNs) to two kinds of complex networks with space L and space P methods respectively. Using improved community detecting algorithm (PKM agglomerative algorithm), we analyze the community property of two kinds of BTNs graphs. The results show that the BTNs graph described with space L method have obvious community property, but the other kind of BTNs graph described with space P method have not. The reason is that the BTNs graph described with space P method have the intense overlapping community property and general community division algorithms can not identify this kind of community structure. To overcome this problem, we propose a novel community structure called N-depth community and present a corresponding community detecting algorithm, which can detect overlapping community. Applying the novel community structure and detecting algorithm to a BTN evolution model described with space P, whose network property agrees well with real BTNs', we get obvious community property. (general)

  6. Sharing cost in social community networks

    DEFF Research Database (Denmark)

    Pal, Ranjan; Elango, Divya; Wardana, Satya Ardhy

    2012-01-01

    their deployment in a residential locality. Our proposed mechanism accounts for heterogeneous user preferences towards different router features and comes up with the optimal (feature-set, user costs) router blueprint that satisfies each user in a locality, in turn motivating them to buy routers and thereby improve......Wireless social community networks (WSCNs) is an emerging technology that operate in the unlicensed spectrum and have been created as an alternative to cellular wireless networks for providing low-cost, high speed wireless data access in urban areas. WSCNs is an upcoming idea that is starting...... reflect their slow progress in capturing the WiFi router market. In this paper, we look at a router design and cost sharing problem in WSCNs to improve deployment. We devise a simple to implement, successful, budget-balanced, ex-post efficient, and individually rational auction-based mechanism...

  7. Towards a Community Environmental Observation Network

    Science.gov (United States)

    Mertl, Stefan; Lettenbichler, Anton

    2014-05-01

    The Community Environmental Observation Network (CEON) is dedicated to the development of a free sensor network to collect and distribute environmental data (e.g. ground shaking, climate parameters). The data collection will be done with contributions from citizens, research institutions and public authorities like communities or schools. This will lead to a large freely available data base which can be used for public information, research, the arts,..... To start a free sensor network, the most important step is to provide easy access to free data collection and -distribution tools. The initial aims of the project CEON are dedicated to the development of these tools. A high quality data logger based on open hardware and free software is developed and a software suite of already existing free software for near-real time data communication and data distribution over the Internet will be assembled. Foremost, the development focuses on the collection of data related to the deformation of the earth (such as ground shaking, surface displacement of mass movements and glaciers) and the collection of climate data. The extent to other measurements will be considered in the design. The data logger is built using open hardware prototyping platforms like BeagleBone Black and Arduino. Main features of the data logger are: a 24Bit analog-to-digital converter; a GPS module for time reference and positioning; wireless mesh networking using Optimized Link State Routing; near real-time data transmission and communication; and near real-time differential GNSS positioning using the RTKLIB software. The project CEON is supported by the Internet Foundation Austria (IPA) within the NetIdee 2013 call.

  8. A Weighted Evolving Network with Community Size Preferential Attachment

    International Nuclear Information System (INIS)

    Zhuo Zhiwei; Shan Erfang

    2010-01-01

    Community structure is an important characteristic in real complex network. It is a network consists of groups of nodes within which links are dense but among which links are sparse. In this paper, the evolving network include node, link and community growth and we apply the community size preferential attachment and strength preferential attachment to a growing weighted network model and utilize weight assigning mechanism from BBV model. The resulting network reflects the intrinsic community structure with generalized power-law distributions of nodes' degrees and strengths.

  9. Opinion Dynamics on Complex Networks with Communities

    International Nuclear Information System (INIS)

    Ru, Wang; Li-Ping, Chi

    2008-01-01

    The Ising or Potts models of ferromagnetism have been widely used to describe locally interacting social or economic systems. We consider a related model, introduced by Sznajd to describe the evolution of consensus in the scale-free networks with the tunable strength (noted by Q) of community structure. In the Sznajd model, the opinion or state of any spins can only be changed by the influence of neighbouring pairs of similar connection spins. Such pairs can polarize their neighbours. Using asynchronous updating, it is found that the smaller the community strength Q, the larger the slope of the exponential relaxation time distribution. Then the effect of the initial up- spin concentration p as a function of the final all up probability E is investigated by taking different initialization strategies, the random node-chosen initialization strategy has no difference under different community strengths, while the strategies of community node-chosen initialization and hub node-chosen initialization are different in final probability under different Q, and the latter one is more effective in reaching final state

  10. Oklahoma forest industries, 1978

    Science.gov (United States)

    Victor A. Rudis; J. Greg Jones

    1978-01-01

    Oklahoma supplied 73 million cu ft of roundwood to forest industries in 1978, an increase of 13 percent since 1972, and 35 percent since 1975 (fig. 1). Pine made up four-fifths of the total. Sawlogs and pulpwood were the major products, accounting for 81 percent of the roundwood produced. Veneer logs accounted for 8 percent and the remainder was mostly posts.

  11. Emergence of communities and diversity in social networks

    OpenAIRE

    Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H. Eugene

    2017-01-01

    Understanding how communities emerge is a fundamental problem in social and economic systems. Here, we experimentally explore the emergence of communities in social networks, using the ultimatum game as a paradigm for capturing individual interactions. We find the emergence of diverse communities in static networks is the result of the local interaction between responders with inherent heterogeneity and rational proposers in which the former act as community leaders. In contrast, communities ...

  12. Community detection for networks with unipartite and bipartite structure

    Science.gov (United States)

    Chang, Chang; Tang, Chao

    2014-09-01

    Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

  13. Complex brain networks: From topological communities to clustered

    Indian Academy of Sciences (India)

    Complex brain networks: From topological communities to clustered dynamics ... Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. ... Pramana – Journal of Physics | News.

  14. Hydraulic Network Modelling of Small Community Water Distribution ...

    African Journals Online (AJOL)

    Prof Anyata

    ... design of a small community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using ..... self cleansing drinking water distribution system is set at 0.4m/s, .... distribution network offers advantages over manual ...

  15. Efficient inference of overlapping communities in complex networks

    DEFF Research Database (Denmark)

    Fruergaard, Bjarne Ørum; Herlau, Tue

    2014-01-01

    We discuss two views on extending existing methods for complex network modeling which we dub the communities first and the networks first view, respectively. Inspired by the networks first view that we attribute to White, Boorman, and Breiger (1976)[1], we formulate the multiple-networks stochastic...

  16. Joint community and anomaly tracking in dynamic networks

    OpenAIRE

    Baingana, Brian; Giannakis, Georgios B.

    2015-01-01

    Most real-world networks exhibit community structure, a phenomenon characterized by existence of node clusters whose intra-edge connectivity is stronger than edge connectivities between nodes belonging to different clusters. In addition to facilitating a better understanding of network behavior, community detection finds many practical applications in diverse settings. Communities in online social networks are indicative of shared functional roles, or affiliation to a common socio-economic st...

  17. Networked Community Change: Understanding Community Systems Change through the Lens of Social Network Analysis.

    Science.gov (United States)

    Lawlor, Jennifer A; Neal, Zachary P

    2016-06-01

    Addressing complex problems in communities has become a key area of focus in recent years (Kania & Kramer, 2013, Stanford Social Innovation Review). Building on existing approaches to understanding and addressing problems, such as action research, several new approaches have emerged that shift the way communities solve problems (e.g., Burns, 2007, Systemic Action Research; Foth, 2006, Action Research, 4, 205; Kania & Kramer, 2011, Stanford Social Innovation Review, 1, 36). Seeking to bring clarity to the emerging literature on community change strategies, this article identifies the common features of the most widespread community change strategies and explores the conditions under which such strategies have the potential to be effective. We identify and describe five common features among the approaches to change. Then, using an agent-based model, we simulate network-building behavior among stakeholders participating in community change efforts using these approaches. We find that the emergent stakeholder networks are efficient when the processes are implemented under ideal conditions. © Society for Community Research and Action 2016.

  18. Effects of multi-state links in network community detection

    International Nuclear Information System (INIS)

    Rocco, Claudio M.; Moronta, José; Ramirez-Marquez, José E.; Barker, Kash

    2017-01-01

    A community is defined as a group of nodes of a network that are densely interconnected with each other but only sparsely connected with the rest of the network. The set of communities (i.e., the network partition) and their inter-community links could be derived using special algorithms account for the topology of the network and, in certain cases, the possible weights associated to the links. In general, the set of weights represents some characteristic as capacity, flow and reliability, among others. The effects of considering weights could be translated to obtain a different partition. In many real situations, particularly when modeling infrastructure systems, networks must be modeled as multi-state networks (e.g., electric power networks). In such networks, each link is characterized by a vector of known random capacities (i.e., the weight on each link could vary according to a known probability distribution). In this paper a simple Monte Carlo approach is proposed to evaluate the effects of multi-state links on community detection as well as on the performance of the network. The approach is illustrated with the topology of an electric power system. - Highlights: • Identify network communities when considering multi-state links. • Identified how effects of considering weights translate to different partition. • Identified importance of Inter-Community Links and changes with respect to community. • Preamble to performing a resilience assessment able to mimic the evolution of the state of each community.

  19. Community Based Networks and 5G Wi-Fi

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2018-01-01

    This paper argues on why Community Based Networks should be recognized as potential 5G providers using 5G Wi-Fi. The argument is hinged on findings in a research to understand why Community Based Networks deploy telecom and Broadband infrastructure. The study was a qualitative study carried out...... inductively using Grounded Theory. Six cases were investigated. Two Community Based Network Mobilization Models were identified. The findings indicate that 5G Wi-Fi deployment by Community Based Networks is possible if policy initiatives and the 5G Wi-Fi standards are developed to facilitate the causal...

  20. Overlapping community detection in networks with positive and negative links

    International Nuclear Information System (INIS)

    Chen, Y; Wang, X L; Yuan, B; Tang, B Z

    2014-01-01

    Complex networks considering both positive and negative links have gained considerable attention during the past several years. Community detection is one of the main challenges for complex network analysis. Most of the existing algorithms for community detection in a signed network aim at providing a hard-partition of the network where any node should belong to a community or not. However, they cannot detect overlapping communities where a node is allowed to belong to multiple communities. The overlapping communities widely exist in many real-world networks. In this paper, we propose a signed probabilistic mixture (SPM) model for overlapping community detection in signed networks. Compared with the existing models, the advantages of our methodology are (i) providing soft-partition solutions for signed networks; (ii) providing soft memberships of nodes. Experiments on a number of signed networks show that our SPM model: (i) can identify assortative structures or disassortative structures as the same as other state-of-the-art models; (ii) can detect overlapping communities; (iii) outperforms other state-of-the-art models at shedding light on the community detection in synthetic signed networks. (paper)

  1. A novel community detection method in bipartite networks

    Science.gov (United States)

    Zhou, Cangqi; Feng, Liang; Zhao, Qianchuan

    2018-02-01

    Community structure is a common and important feature in many complex networks, including bipartite networks, which are used as a standard model for many empirical networks comprised of two types of nodes. In this paper, we propose a two-stage method for detecting community structure in bipartite networks. Firstly, we extend the widely-used Louvain algorithm to bipartite networks. The effectiveness and efficiency of the Louvain algorithm have been proved by many applications. However, there lacks a Louvain-like algorithm specially modified for bipartite networks. Based on bipartite modularity, a measure that extends unipartite modularity and that quantifies the strength of partitions in bipartite networks, we fill the gap by developing the Bi-Louvain algorithm that iteratively groups the nodes in each part by turns. This algorithm in bipartite networks often produces a balanced network structure with equal numbers of two types of nodes. Secondly, for the balanced network yielded by the first algorithm, we use an agglomerative clustering method to further cluster the network. We demonstrate that the calculation of the gain of modularity of each aggregation, and the operation of joining two communities can be compactly calculated by matrix operations for all pairs of communities simultaneously. At last, a complete hierarchical community structure is unfolded. We apply our method to two benchmark data sets and a large-scale data set from an e-commerce company, showing that it effectively identifies community structure in bipartite networks.

  2. Partner network communities – a resource of universities’ activities

    Directory of Open Access Journals (Sweden)

    Romm Mark V.

    2016-01-01

    Full Text Available The network activity is not only part and parcel of the modern university, but it also demonstrates the level of its success. There appeared an urgent need for understanding the nature of universities’ network interactions and finding the most effective models of their network cooperation. The article analyzes partnership network communities with higher educational establishments (universities’ participation, which are being actively created nowadays. The conditions for successful network activities of a university in scientific, academic and professional network communities are presented.

  3. Incorporating profile information in community detection for online social networks

    Science.gov (United States)

    Fan, W.; Yeung, K. H.

    2014-07-01

    Community structure is an important feature in the study of complex networks. It is because nodes of the same community may have similar properties. In this paper we extend two popular community detection methods to partition online social networks. In our extended methods, the profile information of users is used for partitioning. We apply the extended methods in several sample networks of Facebook. Compared with the original methods, the community structures we obtain have higher modularity. Our results indicate that users' profile information is consistent with the community structure of their friendship network to some extent. To the best of our knowledge, this paper is the first to discuss how profile information can be used to improve community detection in online social networks.

  4. Similarity between community structures of different online social networks and its impact on underlying community detection

    Science.gov (United States)

    Fan, W.; Yeung, K. H.

    2015-03-01

    As social networking services are popular, many people may register in more than one online social network. In this paper we study a set of users who have accounts of three online social networks: namely Foursquare, Facebook and Twitter. Community structure of this set of users may be reflected in these three online social networks. Therefore, high correlation between these reflections and the underlying community structure may be observed. In this work, community structures are detected in all three online social networks. Also, we investigate the similarity level of community structures across different networks. It is found that they show strong correlation with each other. The similarity between different networks may be helpful to find a community structure close to the underlying one. To verify this, we propose a method to increase the weights of some connections in networks. With this method, new networks are generated to assist community detection. By doing this, value of modularity can be improved and the new community structure match network's natural structure better. In this paper we also show that the detected community structures of online social networks are correlated with users' locations which are identified on Foursquare. This information may also be useful for underlying community detection.

  5. Cluster synchronization in community network with hybrid coupling

    International Nuclear Information System (INIS)

    Yang, Lixin; Jiang, Jun; Liu, Xiaojun

    2016-01-01

    Highlights: • A community network model with hybrid coupling is proposed. • Control scheme is designed via combining adaptive external coupling strength and feedback control. • The influence of topology structure on synchronization of community network is discussed. - Abstract: A general model of community network with hybrid coupling is proposed in this paper. In the community network model with hybrid coupling, the inner connections are in the same type of coupling within the same community and in different types of coupling in different communities. The connections between different pair of communities are also nonidentical. Cluster synchronization of community network with hybrid coupling is investigated via adaptive couplings control scheme. Effective controllers are designed for constructing an effective control scheme and adjusting automatically the adaptive external coupling strength by taking external coupling strength as adaptive variables on a small fraction of network edges. Moreover, the impact of the topology on the synchronizability of community network is investigated. The numerical results reveal that the number of links between communities and the degree of the connector nodes have significant effects on the synchronization performance.

  6. Detecting the overlapping and hierarchical community structure in complex networks

    International Nuclear Information System (INIS)

    Lancichinetti, Andrea; Fortunato, Santo; Kertesz, Janos

    2009-01-01

    Many networks in nature, society and technology are characterized by a mesoscopic level of organization, with groups of nodes forming tightly connected units, called communities or modules, that are only weakly linked to each other. Uncovering this community structure is one of the most important problems in the field of complex networks. Networks often show a hierarchical organization, with communities embedded within other communities; moreover, nodes can be shared between different communities. Here, we present the first algorithm that finds both overlapping communities and the hierarchical structure. The method is based on the local optimization of a fitness function. Community structure is revealed by peaks in the fitness histogram. The resolution can be tuned by a parameter enabling different hierarchical levels of organization to be investigated. Tests on real and artificial networks give excellent results.

  7. Homophyly/kinship hypothesis: Natural communities, and predicting in networks

    Science.gov (United States)

    Li, Angsheng; Li, Jiankou; Pan, Yicheng

    2015-02-01

    It has been a longstanding challenge to understand natural communities in real world networks. We proposed a community finding algorithm based on fitness of networks, two algorithms for prediction, accurate prediction and confirmation of keywords for papers in the citation network Arxiv HEP-TH (high energy physics theory), and the measures of internal centrality, external de-centrality, internal and external slopes to characterize the structures of communities. We implemented our algorithms on 2 citation and 5 cooperation graphs. Our experiments explored and validated a homophyly/kinship principle of real world networks. The homophyly/kinship principle includes: (1) homophyly is the natural selection in real world networks, similar to Darwin's kinship selection in nature, (2) real world networks consist of natural communities generated by the natural selection of homophyly, (3) most individuals in a natural community share a short list of common attributes, (4) natural communities have an internal centrality (or internal heterogeneity) that a natural community has a few nodes dominating most of the individuals in the community, (5) natural communities have an external de-centrality (or external homogeneity) that external links of a natural community homogeneously distributed in different communities, and (6) natural communities of a given network have typical structures determined by the internal slopes, and have typical patterns of outgoing links determined by external slopes, etc. Our homophyly/kinship principle perfectly matches Darwin's observation that animals from ants to people form social groups in which most individuals work for the common good, and that kinship could encourage altruistic behavior. Our homophyly/kinship principle is the network version of Darwinian theory, and builds a bridge between Darwinian evolution and network science.

  8. Emergence of communities and diversity in social networks.

    Science.gov (United States)

    Han, Xiao; Cao, Shinan; Shen, Zhesi; Zhang, Boyu; Wang, Wen-Xu; Cressman, Ross; Stanley, H Eugene

    2017-03-14

    Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics.

  9. Mixture models with entropy regularization for community detection in networks

    Science.gov (United States)

    Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

    2018-04-01

    Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

  10. A framework for detecting communities of unbalanced sizes in networks

    Science.gov (United States)

    Žalik, Krista Rizman; Žalik, Borut

    2018-01-01

    Community detection in large networks has been a focus of recent research in many of fields, including biology, physics, social sciences, and computer science. Most community detection methods partition the entire network into communities, groups of nodes that have many connections within communities and few connections between them and do not identify different roles that nodes can have in communities. We propose a community detection model that integrates more different measures that can fast identify communities of different sizes and densities. We use node degree centrality, strong similarity with one node from community, maximal similarity of node to community, compactness of communities and separation between communities. Each measure has its own strength and weakness. Thus, combining different measures can benefit from the strengths of each one and eliminate encountered problems of using an individual measure. We present a fast local expansion algorithm for uncovering communities of different sizes and densities and reveals rich information on input networks. Experimental results show that the proposed algorithm is better or as effective as the other community detection algorithms for both real-world and synthetic networks while it requires less time.

  11. Allied health education in Oklahoma.

    Science.gov (United States)

    Holder, L; Nelson, S; Curcio, B

    1990-11-01

    This article is the first of several dealing with medical education and recruitment in Oklahoma and generated at the request of the OSMA-OUHSC Liaison Committee. The articles were sought out and submitted with the assistance of Edward N. Brandt, Jr., MD, PhD, executive dean at the University of Oklahoma College of Medicine.

  12. An exploration of fetish social networks and communities

    OpenAIRE

    Fay, Damien; Haddadi, Hamed; Seto, Michael C.; Wang, Han; Kling, Christoph Carl

    2015-01-01

    Online Social Networks (OSNs) provide a venue for virtual interactions and relationships between individuals. In some communities, OSNs also facilitate arranging online meetings and relationships. FetLife, the worlds largest anonymous social network for the BDSM, fetish and kink communities, provides a unique example of an OSN that serves as an interaction space, community organizing tool, and sexual market. In this paper, we present a first look at the characteristics of European members of ...

  13. 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.

  14. Community Detection for Multiplex Social Networks Based on Relational Bayesian Networks

    DEFF Research Database (Denmark)

    Jiang, Jiuchuan; Jaeger, Manfred

    2014-01-01

    Many techniques have been proposed for community detection in social networks. Most of these techniques are only designed for networks defined by a single relation. However, many real networks are multiplex networks that contain multiple types of relations and different attributes on the nodes...

  15. Effect of size heterogeneity on community identification in complex networks

    Energy Technology Data Exchange (ETDEWEB)

    Danon, L.; Diaz-Guilera, A.; Arenas, A.

    2008-01-01

    Identifying community structure can be a potent tool in the analysis and understanding of the structure of complex networks. Up to now, methods for evaluating the performance of identification algorithms use ad-hoc networks with communities of equal size. We show that inhomogeneities in community sizes can and do affect the performance of algorithms considerably, and propose an alternative method which takes these factors into account. Furthermore, we propose a simple modification of the algorithm proposed by Newman for community detection (Phys. Rev. E 69 066133) which treats communities of different sizes on an equal footing, and show that it outperforms the original algorithm while retaining its speed.

  16. Predicting Community Evolution in Social Networks

    Directory of Open Access Journals (Sweden)

    Stanisław Saganowski

    2015-05-01

    Full Text Available Nowadays, sustained development of different social media can be observed worldwide. One of the relevant research domains intensively explored recently is analysis of social communities existing in social media as well as prediction of their future evolution taking into account collected historical evolution chains. These evolution chains proposed in the paper contain group states in the previous time frames and its historical transitions that were identified using one out of two methods: Stable Group Changes Identification (SGCI and Group Evolution Discovery (GED. Based on the observed evolution chains of various length, structural network features are extracted, validated and selected as well as used to learn classification models. The experimental studies were performed on three real datasets with different profile: DBLP, Facebook and Polish blogosphere. The process of group prediction was analysed with respect to different classifiers as well as various descriptive feature sets extracted from evolution chains of different length. The results revealed that, in general, the longer evolution chains the better predictive abilities of the classification models. However, chains of length 3 to 7 enabled the GED-based method to almost reach its maximum possible prediction quality. For SGCI, this value was at the level of 3–5 last periods.

  17. Clustering coefficient and community structure of bipartite networks

    Science.gov (United States)

    Zhang, Peng; Wang, Jinliang; Li, Xiaojia; Li, Menghui; Di, Zengru; Fan, Ying

    2008-12-01

    Many real-world networks display natural bipartite structure, where the basic cycle is a square. In this paper, with the similar consideration of standard clustering coefficient in binary networks, a definition of the clustering coefficient for bipartite networks based on the fraction of squares is proposed. In order to detect community structures in bipartite networks, two different edge clustering coefficients LC4 and LC3 of bipartite networks are defined, which are based on squares and triples respectively. With the algorithm of cutting the edge with the least clustering coefficient, communities in artificial and real world networks are identified. The results reveal that investigating bipartite networks based on the original structure can show the detailed properties that is helpful to get deep understanding about the networks.

  18. Facebook faith - social networking in a faith based community

    OpenAIRE

    Lundqvist, K O; Lundqvist, Karsten Oster

    2009-01-01

    This paper views the increasing social networking as an efficient emerging ministry to the moveable generation. Through social network such as Facebook, ministry from a pastoral perspective can \\ud become more authentic and meaningful. Ministry is relational. Social Networking sites provide a strong platform to being part in other people’s life. Social networking and living online builds \\ud community beyond geographical boarders. Young adults and youths digital identity often reflects their ...

  19. Weighted Evolving Networks with Self-organized Communities

    International Nuclear Information System (INIS)

    Xie Zhou; Wang Xiaofan; Li Xiang

    2008-01-01

    In order to describe the self-organization of communities in the evolution of weighted networks, we propose a new evolving model for weighted community-structured networks with the preferential mechanisms functioned in different levels according to community sizes and node strengths, respectively. Theoretical analyses and numerical simulations show that our model captures power-law distributions of community sizes, node strengths, and link weights, with tunable exponents of ν ≥ 1, γ > 2, and α > 2, respectively, sharing large clustering coefficients and scaling clustering spectra, and covering the range from disassortative networks to assortative networks. Finally, we apply our new model to the scientific co-authorship networks with both their weighted and unweighted datasets to verify its effectiveness

  20. Community structures and role detection in music networks

    Science.gov (United States)

    Teitelbaum, T.; Balenzuela, P.; Cano, P.; Buldú, Javier M.

    2008-12-01

    We analyze the existence of community structures in two different social networks using data obtained from similarity and collaborative features between musical artists. Our analysis reveals some characteristic organizational patterns and provides information about the driving forces behind the growth of the networks. In the similarity network, we find a strong correlation between clusters of artists and musical genres. On the other hand, the collaboration network shows two different kinds of communities: rather small structures related to music bands and geographic zones, and much bigger communities built upon collaborative clusters with a high number of participants related through the period the artists were active. Finally, we detect the leading artists inside their corresponding communities and analyze their roles in the network by looking at a few topological properties of the nodes.

  1. Covariance, correlation matrix, and the multiscale community structure of networks.

    Science.gov (United States)

    Shen, Hua-Wei; Cheng, Xue-Qi; Fang, Bin-Xing

    2010-07-01

    Empirical studies show that real world networks often exhibit multiple scales of topological descriptions. However, it is still an open problem how to identify the intrinsic multiple scales of networks. In this paper, we consider detecting the multiscale community structure of network from the perspective of dimension reduction. According to this perspective, a covariance matrix of network is defined to uncover the multiscale community structure through the translation and rotation transformations. It is proved that the covariance matrix is the unbiased version of the well-known modularity matrix. We then point out that the translation and rotation transformations fail to deal with the heterogeneous network, which is very common in nature and society. To address this problem, a correlation matrix is proposed through introducing the rescaling transformation into the covariance matrix. Extensive tests on real world and artificial networks demonstrate that the correlation matrix significantly outperforms the covariance matrix, identically the modularity matrix, as regards identifying the multiscale community structure of network. This work provides a novel perspective to the identification of community structure and thus various dimension reduction methods might be used for the identification of community structure. Through introducing the correlation matrix, we further conclude that the rescaling transformation is crucial to identify the multiscale community structure of network, as well as the translation and rotation transformations.

  2. Cluster synchronization for directed community networks via pinning partial schemes

    International Nuclear Information System (INIS)

    Hu Cheng; Jiang Haijun

    2012-01-01

    Highlights: ► Cluster synchronization for directed community networks is proposed by pinning partial schemes. ► Each community is considered as a whole. ► Several novel pinning criteria are derived based on the information of communities. ► A numerical example with simulation is provided. - Abstract: In this paper, we focus on driving a class of directed networks to achieve cluster synchronization by pinning schemes. The desired cluster synchronization states are no longer decoupled orbits but a set of un-decoupled trajectories. Each community is considered as a whole and the synchronization criteria are derived based on the information of communities. Several pinning schemes including feedback control and adaptive strategy are proposed to select controlled communities by analyzing the information of each community such as indegrees and outdegrees. In all, this paper answers several challenging problems in pinning control of directed community networks: (1) What communities should be chosen as controlled candidates? (2) How many communities are needed to be controlled? (3) How large should the control gains be used in a given community network to achieve cluster synchronization? Finally, an example with numerical simulations is given to demonstrate the effectiveness of the theoretical results.

  3. Asymmetric intimacy and algorithm for detecting communities in bipartite networks

    Science.gov (United States)

    Wang, Xingyuan; Qin, Xiaomeng

    2016-11-01

    In this paper, an algorithm to choose a good partition in bipartite networks has been proposed. Bipartite networks have more theoretical significance and broader prospect of application. In view of distinctive structure of bipartite networks, in our method, two parameters are defined to show the relationships between the same type nodes and heterogeneous nodes respectively. Moreover, our algorithm employs a new method of finding and expanding the core communities in bipartite networks. Two kinds of nodes are handled separately and merged, and then the sub-communities are obtained. After that, objective communities will be found according to the merging rule. The proposed algorithm has been simulated in real-world networks and artificial networks, and the result verifies the accuracy and reliability of the parameters on intimacy for our algorithm. Eventually, comparisons with similar algorithms depict that the proposed algorithm has better performance.

  4. Information dynamics algorithm for detecting communities in networks

    Science.gov (United States)

    Massaro, Emanuele; Bagnoli, Franco; Guazzini, Andrea; Lió, Pietro

    2012-11-01

    The problem of community detection is relevant in many scientific disciplines, from social science to statistical physics. Given the impact of community detection in many areas, such as psychology and social sciences, we have addressed the issue of modifying existing well performing algorithms by incorporating elements of the domain application fields, i.e. domain-inspired. We have focused on a psychology and social network-inspired approach which may be useful for further strengthening the link between social network studies and mathematics of community detection. Here we introduce a community-detection algorithm derived from the van Dongen's Markov Cluster algorithm (MCL) method [4] by considering networks' nodes as agents capable to take decisions. In this framework we have introduced a memory factor to mimic a typical human behavior such as the oblivion effect. The method is based on information diffusion and it includes a non-linear processing phase. We test our method on two classical community benchmark and on computer generated networks with known community structure. Our approach has three important features: the capacity of detecting overlapping communities, the capability of identifying communities from an individual point of view and the fine tuning the community detectability with respect to prior knowledge of the data. Finally we discuss how to use a Shannon entropy measure for parameter estimation in complex networks.

  5. Global and local targeted immunization in networks with community structure

    International Nuclear Information System (INIS)

    Yan, Shu; Tang, Shaoting; Pei, Sen; Zheng, Zhiming; Fang, Wenyi

    2015-01-01

    Immunization plays an important role in the field of epidemic spreading in complex networks. In previous studies, targeted immunization has been proved to be an effective strategy. However, when extended to networks with community structure, it is unknown whether the superior strategy is to vaccinate the nodes who have the most connections in the entire network (global strategy), or those in the original community where epidemic starts to spread (local strategy). In this work, by using both analytic approaches and simulations, we observe that the answer depends on the closeness between communities. If communities are tied closely, the global strategy is superior to the local strategy. Otherwise, the local targeted immunization is advantageous. The existence of a transitional value of closeness implies that we should adopt different strategies. Furthermore, we extend our investigation from two-community networks to multi-community networks. We consider the mode of community connection and the location of community where epidemic starts to spread. Both simulation results and theoretical predictions show that local strategy is a better option for immunization in most cases. But if the epidemic begins from a core community, global strategy is superior in some cases. (paper)

  6. Sampling from complex networks with high community structures.

    Science.gov (United States)

    Salehi, Mostafa; Rabiee, Hamid R; Rajabi, Arezo

    2012-06-01

    In this paper, we propose a novel link-tracing sampling algorithm, based on the concepts from PageRank vectors, to sample from networks with high community structures. Our method has two phases; (1) Sampling the closest nodes to the initial nodes by approximating personalized PageRank vectors and (2) Jumping to a new community by using PageRank vectors and unknown neighbors. Empirical studies on several synthetic and real-world networks show that the proposed method improves the performance of network sampling compared to the popular link-based sampling methods in terms of accuracy and visited communities.

  7. Epidemic spreading in time-varying community networks.

    Science.gov (United States)

    Ren, Guangming; Wang, Xingyuan

    2014-06-01

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold qc. The epidemic will survive when q > qc and die when q epidemic spreading in complex networks with community structure.

  8. Impulsive Cluster Synchronization in Community Network with Nonidentical Nodes

    International Nuclear Information System (INIS)

    Deng Liping; Wu Zhaoyan

    2012-01-01

    In this paper, cluster synchronization in community network with nonidentical nodes and impulsive effects is investigated. Community networks with two kinds of topological structure are investigated. Positive weighted network is considered first and external pinning controllers are designed for achieving cluster synchronization. Cooperative and competitive network under some assumptions is investigated as well and can achieve cluster synchronization with only impulsive controllers. Based on the stability analysis of impulsive differential equation and the Lyapunov stability theory, several simple and useful synchronization criteria are derived. Finally, numerical simulations are provided to verify the effectiveness of the derived results.

  9. Community Size Effects on Epidemic Spreading in Multiplex Social Networks.

    Directory of Open Access Journals (Sweden)

    Ting Liu

    Full Text Available The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals' alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals' risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals' protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.

  10. Community Size Effects on Epidemic Spreading in Multiplex Social Networks.

    Science.gov (United States)

    Liu, Ting; Li, Ping; Chen, Yan; Zhang, Jie

    2016-01-01

    The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explore the interactions between disease spreading and population response in terms of information diffusion and individuals' alertness. We model the system by mapping multiplex networks into two-layer networks and incorporating individuals' risk awareness, on the assumption that their response to the disease spreading depends on the size of the community they belong to. By comparing the final incidence of diseases in multiplex networks, we find that there is considerable mitigation of diseases spreading for full phase of spreading speed when individuals' protection responses are introduced. Interestingly, the degree of community overlap between the two layers is found to be critical factor that affects the final incidence. We also analyze the consequences of the epidemic incidence in communities with different sizes and the impacts of community overlap between two layers. Specifically, as the diseases information makes individuals alert and take measures to prevent the diseases, the effective protection is more striking in small community. These phenomena can be explained by the multiplexity of the networked system and the competition between two spreading processes.

  11. A model for evolution of overlapping community networks

    Science.gov (United States)

    Karan, Rituraj; Biswal, Bibhu

    2017-05-01

    A model is proposed for the evolution of network topology in social networks with overlapping community structure. Starting from an initial community structure that is defined in terms of group affiliations, the model postulates that the subsequent growth and loss of connections is similar to the Hebbian learning and unlearning in the brain and is governed by two dominant factors: the strength and frequency of interaction between the members, and the degree of overlap between different communities. The temporal evolution from an initial community structure to the current network topology can be described based on these two parameters. It is possible to quantify the growth occurred so far and predict the final stationary state to which the network is likely to evolve. Applications in epidemiology or the spread of email virus in a computer network as well as finding specific target nodes to control it are envisaged. While facing the challenge of collecting and analyzing large-scale time-resolved data on social groups and communities one faces the most basic questions: how do communities evolve in time? This work aims to address this issue by developing a mathematical model for the evolution of community networks and studying it through computer simulation.

  12. 76 FR 70940 - Approval and Promulgation of Air Quality Implementation Plans; Oklahoma; Infrastructure...

    Science.gov (United States)

    2011-11-16

    ...). Oklahoma's monitoring network includes the State and Local Air Monitoring Stations (SLAMS), which measure... and PM 2.5 monitor locations and current and historical data, including ozone design values for...

  13. Extending a configuration model to find communities in complex networks

    International Nuclear Information System (INIS)

    Jin, Di; Hu, Qinghua; He, Dongxiao; Yang, Bo; Baquero, Carlos

    2013-01-01

    Discovery of communities in complex networks is a fundamental data analysis task in various domains. Generative models are a promising class of techniques for identifying modular properties from networks, which has been actively discussed recently. However, most of them cannot preserve the degree sequence of networks, which will distort the community detection results. Rather than using a blockmodel as most current works do, here we generalize a configuration model, namely, a null model of modularity, to solve this problem. Towards decomposing and combining sub-graphs according to the soft community memberships, our model incorporates the ability to describe community structures, something the original model does not have. Also, it has the property, as with the original model, that it fixes the expected degree sequence to be the same as that of the observed network. We combine both the community property and degree sequence preserving into a single unified model, which gives better community results compared with other models. Thereafter, we learn the model using a technique of nonnegative matrix factorization and determine the number of communities by applying consensus clustering. We test this approach both on synthetic benchmarks and on real-world networks, and compare it with two similar methods. The experimental results demonstrate the superior performance of our method over competing methods in detecting both disjoint and overlapping communities. (paper)

  14. Interaction Networks: Generating High Level Hints Based on Network Community Clustering

    Science.gov (United States)

    Eagle, Michael; Johnson, Matthew; Barnes, Tiffany

    2012-01-01

    We introduce a novel data structure, the Interaction Network, for representing interaction-data from open problem solving environment tutors. We show how using network community detecting techniques are used to identify sub-goals in problems in a logic tutor. We then use those community structures to generate high level hints between sub-goals.…

  15. Hydraulic Network Modelling of Small Community Water Distribution ...

    African Journals Online (AJOL)

    Prof Anyata

    community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using. WaterCAD ..... Table 1: Criteria Relating Population to Water Demand (NWSP, 2000) ..... timely manner ... Department, Middle East Technical.

  16. Community Clustering Algorithm in Complex Networks Based on Microcommunity Fusion

    Directory of Open Access Journals (Sweden)

    Jin Qi

    2015-01-01

    Full Text Available With the further research on physical meaning and digital features of the community structure in complex networks in recent years, the improvement of effectiveness and efficiency of the community mining algorithms in complex networks has become an important subject in this area. This paper puts forward a concept of the microcommunity and gets final mining results of communities through fusing different microcommunities. This paper starts with the basic definition of the network community and applies Expansion to the microcommunity clustering which provides prerequisites for the microcommunity fusion. The proposed algorithm is more efficient and has higher solution quality compared with other similar algorithms through the analysis of test results based on network data set.

  17. Evolution properties of the community members for dynamic networks

    Science.gov (United States)

    Yang, Kai; Guo, Qiang; Li, Sheng-Nan; Han, Jing-Ti; Liu, Jian-Guo

    2017-03-01

    The collective behaviors of community members for dynamic social networks are significant for understanding evolution features of communities. In this Letter, we empirically investigate the evolution properties of the new community members for dynamic networks. Firstly, we separate data sets into different slices, and analyze the statistical properties of new members as well as communities they joined in for these data sets. Then we introduce a parameter φ to describe community evolution between different slices and investigate the dynamic community properties of the new community members. The empirical analyses for the Facebook, APS, Enron and Wiki data sets indicate that both the number of new members and joint communities increase, the ratio declines rapidly and then becomes stable over time, and most of the new members will join in the small size communities that is s ≤ 10. Furthermore, the proportion of new members in existed communities decreases firstly and then becomes stable and relatively small for these data sets. Our work may be helpful for deeply understanding the evolution properties of community members for social networks.

  18. An Efficient Hierarchy Algorithm for Community Detection in Complex Networks

    Directory of Open Access Journals (Sweden)

    Lili Zhang

    2014-01-01

    Full Text Available Community structure is one of the most fundamental and important topology characteristics of complex networks. The research on community structure has wide applications and is very important for analyzing the topology structure, understanding the functions, finding the hidden properties, and forecasting the time-varying of the networks. This paper analyzes some related algorithms and proposes a new algorithm—CN agglomerative algorithm based on graph theory and the local connectedness of network to find communities in network. We show this algorithm is distributed and polynomial; meanwhile the simulations show it is accurate and fine-grained. Furthermore, we modify this algorithm to get one modified CN algorithm and apply it to dynamic complex networks, and the simulations also verify that the modified CN algorithm has high accuracy too.

  19. Exponential random graph models for networks with community structure.

    Science.gov (United States)

    Fronczak, Piotr; Fronczak, Agata; Bujok, Maksymilian

    2013-09-01

    Although the community structure organization is an important characteristic of real-world networks, most of the traditional network models fail to reproduce the feature. Therefore, the models are useless as benchmark graphs for testing community detection algorithms. They are also inadequate to predict various properties of real networks. With this paper we intend to fill the gap. We develop an exponential random graph approach to networks with community structure. To this end we mainly built upon the idea of blockmodels. We consider both the classical blockmodel and its degree-corrected counterpart and study many of their properties analytically. We show that in the degree-corrected blockmodel, node degrees display an interesting scaling property, which is reminiscent of what is observed in real-world fractal networks. A short description of Monte Carlo simulations of the models is also given in the hope of being useful to others working in the field.

  20. Dynamical community structure of populations evolving on genotype networks

    International Nuclear Information System (INIS)

    Capitán, José A.; Aguirre, Jacobo; Manrubia, Susanna

    2015-01-01

    Neutral evolutionary dynamics of replicators occurs on large and heterogeneous networks of genotypes. These networks, formed by all genotypes that yield the same phenotype, have a complex architecture that conditions the molecular composition of populations and their movements on genome spaces. Here we consider as an example the case of populations evolving on RNA secondary structure neutral networks and study the community structure of the network revealed through dynamical properties of the population at equilibrium and during adaptive transients. We unveil a rich hierarchical community structure that, eventually, can be traced back to the non-trivial relationship between RNA secondary structure and sequence composition. We demonstrate that usual measures of modularity that only take into account the static, topological structure of networks, cannot identify the community structure disclosed by population dynamics

  1. Digital atlas of Oklahoma

    Science.gov (United States)

    Rea, A.H.; Becker, C.J.

    1997-01-01

    This compact disc contains 25 digital map data sets covering the State of Oklahoma that may be of interest to the general public, private industry, schools, and government agencies. Fourteen data sets are statewide. These data sets include: administrative boundaries; 104th U.S. Congressional district boundaries; county boundaries; latitudinal lines; longitudinal lines; geographic names; indexes of U.S. Geological Survey 1:100,000, and 1:250,000-scale topographic quadrangles; a shaded-relief image; Oklahoma State House of Representatives district boundaries; Oklahoma State Senate district boundaries; locations of U.S. Geological Survey stream gages; watershed boundaries and hydrologic cataloging unit numbers; and locations of weather stations. Eleven data sets are divided by county and are located in 77 county subdirectories. These data sets include: census block group boundaries with selected demographic data; city and major highways text; geographic names; land surface elevation contours; elevation points; an index of U.S. Geological Survey 1:24,000-scale topographic quadrangles; roads, streets and address ranges; highway text; school district boundaries; streams, river and lakes; and the public land survey system. All data sets are provided in a readily accessible format. Most data sets are provided in Digital Line Graph (DLG) format. The attributes for many of the DLG files are stored in related dBASE(R)-format files and may be joined to the data set polygon attribute or arc attribute tables using dBASE(R)-compatible software. (Any use of trade names in this publication is for descriptive purposes only and does not imply endorsement by the U.S. Government.) Point attribute tables are provided in dBASE(R) format only, and include the X and Y map coordinates of each point. Annotation (text plotted in map coordinates) are provided in AutoCAD Drawing Exchange format (DXF) files. The shaded-relief image is provided in TIFF format. All data sets except the shaded

  2. Social networks and performance in distributed learning communities

    OpenAIRE

    Cadima, Rita; Ojeda Rodríguez, Jordi; Monguet Fierro, José María

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this study we analyse two distributed learning communities' social networks in order to understand how characteristics of the social structure can enhance s...

  3. Supporting Communities in Programmable Grid Networks: gTBN

    NARCIS (Netherlands)

    Christea, M.L; Strijkers, R.J.; Marchal, D.; Gommans, L.; Laat, C. de; Meijer, R.J.

    2009-01-01

    Abstract—This paper presents the generalised Token Based Networking (gTBN) architecture, which enables dynamic binding of communities and their applications to specialised network services. gTBN uses protocol independent tokens to provide decoupling of authorisation from time of usage as well as

  4. Social Networks: Gated Communities or Free Cantons?

    CERN Multimedia

    CERN. Geneva

    2017-01-01

    Online social networks and other cloud-based services have concentrated the control of the web in the hands of a few corporations. Our personal data has been commodified, often without our knowledge or consent. Is there a way to retain all the benefits of social networking without giving up control of our data?

  5. Community Size Effects on Epidemic Spreading in Multiplex Social Networks

    OpenAIRE

    Liu, Ting; Li, Ping; Chen, Yan; Zhang, Jie

    2016-01-01

    The dynamical process of epidemic spreading has drawn much attention of the complex network community. In the network paradigm, diseases spread from one person to another through the social ties amongst the population. There are a variety of factors that govern the processes of disease spreading on the networks. A common but not negligible factor is people's reaction to the outbreak of epidemics. Such reaction can be related information dissemination or self-protection. In this work, we explo...

  6. Adaptive multi-resolution Modularity for detecting communities in networks

    Science.gov (United States)

    Chen, Shi; Wang, Zhi-Zhong; Bao, Mei-Hua; Tang, Liang; Zhou, Ji; Xiang, Ju; Li, Jian-Ming; Yi, Chen-He

    2018-02-01

    Community structure is a common topological property of complex networks, which attracted much attention from various fields. Optimizing quality functions for community structures is a kind of popular strategy for community detection, such as Modularity optimization. Here, we introduce a general definition of Modularity, by which several classical (multi-resolution) Modularity can be derived, and then propose a kind of adaptive (multi-resolution) Modularity that can combine the advantages of different Modularity. By applying the Modularity to various synthetic and real-world networks, we study the behaviors of the methods, showing the validity and advantages of the multi-resolution Modularity in community detection. The adaptive Modularity, as a kind of multi-resolution method, can naturally solve the first-type limit of Modularity and detect communities at different scales; it can quicken the disconnecting of communities and delay the breakup of communities in heterogeneous networks; and thus it is expected to generate the stable community structures in networks more effectively and have stronger tolerance against the second-type limit of Modularity.

  7. Home-School Links: Networking the Learning Community.

    Science.gov (United States)

    1996

    The topic of networking the learning community with home-school links is addressed in four papers: "Internet Access via School: Expectations of Students and Parents" (Roy Crotty); "The School Library as Community Information Gateway" (Megan Perry); "Rural Access to the Internet" (Ken Eustace); and "NetDay '96:…

  8. New Ideas for Communities of Practice: Networks of Networks

    African Journals Online (AJOL)

    WimHugo

    mobile phones, and smart devices. We have used .... Google. ICSU WDS. GEO. Figure 2: Comparing Initiatives using Dimensions. Practical ... Practical implementation will require networks that are just complex enough to serve the purpose:.

  9. Detecting and evaluating communities in complex human and biological networks

    Science.gov (United States)

    Morrison, Greg; Mahadevan, L.

    2012-02-01

    We develop a simple method for detecting the community structure in a network can by utilizing a measure of closeness between nodes. This approach readily leads to a method of coarse graining the network, which allows the detection of the natural hierarchy (or hierarchies) of community structure without appealing to an unknown resolution parameter. The closeness measure can also be used to evaluate the robustness of an individual node's assignment to its community (rather than evaluating only the quality of the global structure). Each of these methods in community detection and evaluation are illustrated using a variety of real world networks of either biological or sociological importance and illustrate the power and flexibility of the approach.

  10. Communities in Large Networks: Identification and Ranking

    DEFF Research Database (Denmark)

    Olsen, Martin

    2008-01-01

    show that the problem of deciding whether a non trivial community exists is NP complete. Nevertheless, experiments show that a very simple greedy approach can identify members of a community in the Danish part of the web graph with time complexity only dependent on the size of the found community...... and its immediate surroundings. The members are ranked with a “local” variant of the PageRank algorithm. Results are reported from successful experiments on identifying and ranking Danish Computer Science sites and Danish Chess pages using only a few representatives....

  11. Multi-Relational Characterization of Dynamic Social Network Communities

    Science.gov (United States)

    Lin, Yu-Ru; Sundaram, Hari; Kelliher, Aisling

    The emergence of the mediated social web - a distributed network of participants creating rich media content and engaging in interactive conversations through Internet-based communication technologies - has contributed to the evolution of powerful social, economic and cultural change. Online social network sites and blogs, such as Facebook, Twitter, Flickr and LiveJournal, thrive due to their fundamental sense of "community". The growth of online communities offers both opportunities and challenges for researchers and practitioners. Participation in online communities has been observed to influence people's behavior in diverse ways ranging from financial decision-making to political choices, suggesting the rich potential for diverse applications. However, although studies on the social web have been extensive, discovering communities from online social media remains challenging, due to the interdisciplinary nature of this subject. In this article, we present our recent work on characterization of communities in online social media using computational approaches grounded on the observations from social science.

  12. Building research infrastructure in community health centers: a Community Health Applied Research Network (CHARN) report.

    Science.gov (United States)

    Likumahuwa, Sonja; Song, Hui; Singal, Robbie; Weir, Rosy Chang; Crane, Heidi; Muench, John; Sim, Shao-Chee; DeVoe, Jennifer E

    2013-01-01

    This article introduces the Community Health Applied Research Network (CHARN), a practice-based research network of community health centers (CHCs). Established by the Health Resources and Services Administration in 2010, CHARN is a network of 4 community research nodes, each with multiple affiliated CHCs and an academic center. The four nodes (18 individual CHCs and 4 academic partners in 9 states) are supported by a data coordinating center. Here we provide case studies detailing how CHARN is building research infrastructure and capacity in CHCs, with a particular focus on how community practice-academic partnerships were facilitated by the CHARN structure. The examples provided by the CHARN nodes include many of the building blocks of research capacity: communication capacity and "matchmaking" between providers and researchers; technology transfer; research methods tailored to community practice settings; and community institutional review board infrastructure to enable community oversight. We draw lessons learned from these case studies that we hope will serve as examples for other networks, with special relevance for community-based networks seeking to build research infrastructure in primary care settings.

  13. Malware Propagation and Prevention Model for Time-Varying Community Networks within Software Defined Networks

    Directory of Open Access Journals (Sweden)

    Lan Liu

    2017-01-01

    Full Text Available As the adoption of Software Defined Networks (SDNs grows, the security of SDN still has several unaddressed limitations. A key network security research area is in the study of malware propagation across the SDN-enabled networks. To analyze the spreading processes of network malware (e.g., viruses in SDN, we propose a dynamic model with a time-varying community network, inspired by research models on the spread of epidemics in complex networks across communities. We assume subnets of the network as communities and links that are dense in subnets but sparse between subnets. Using numerical simulation and theoretical analysis, we find that the efficiency of network malware propagation in this model depends on the mobility rate q of the nodes between subnets. We also find that there exists a mobility rate threshold qc. The network malware will spread in the SDN when the mobility rate q>qc. The malware will survive when q>qc and perish when qnetwork malware and provide a theoretical basis to reduce and prevent network security incidents.

  14. Stylized facts in social networks: Community-based static modeling

    Science.gov (United States)

    Jo, Hang-Hyun; Murase, Yohsuke; Török, János; Kertész, János; Kaski, Kimmo

    2018-06-01

    The past analyses of datasets of social networks have enabled us to make empirical findings of a number of aspects of human society, which are commonly featured as stylized facts of social networks, such as broad distributions of network quantities, existence of communities, assortative mixing, and intensity-topology correlations. Since the understanding of the structure of these complex social networks is far from complete, for deeper insight into human society more comprehensive datasets and modeling of the stylized facts are needed. Although the existing dynamical and static models can generate some stylized facts, here we take an alternative approach by devising a community-based static model with heterogeneous community sizes and larger communities having smaller link density and weight. With these few assumptions we are able to generate realistic social networks that show most stylized facts for a wide range of parameters, as demonstrated numerically and analytically. Since our community-based static model is simple to implement and easily scalable, it can be used as a reference system, benchmark, or testbed for further applications.

  15. Community-centred Networks and Networking among Companies, Educational and Cultural Institutions and Research

    DEFF Research Database (Denmark)

    Konnerup, Ulla; Dirckinck-Holmfeld, Lone

    2010-01-01

    This article presents visions for community-centred networks and networking among companies, educational and cultural institutions and research based on blended on- and off-line collaboration and communication. Our point of departure is the general vision of networking between government, industry...... and research as formulated in the Triple Helix Model (Etzkowitz 2008). The article draws on a case study of NoEL, a network on e-learning among business, educational and cultural institutions and research, all in all 21 partners from all around Denmark. Focus is how networks and networking change character......’ in Networked Learning, Wenger et al. 2009; The analysis concerns the participation structure and how the network activities connect local work practices and research, and how technology and online communication contribute to a change from participation in offline and physical network activities into online...

  16. Distributed detection of communities in complex networks using synthetic coordinates

    International Nuclear Information System (INIS)

    Papadakis, H; Fragopoulou, P; Panagiotakis, C

    2014-01-01

    Various applications like finding Web communities, detecting the structure of social networks, and even analyzing a graph’s structure to uncover Internet attacks are just some of the applications for which community detection is important. In this paper, we propose an algorithm that finds the entire community structure of a network, on the basis of local interactions between neighboring nodes and an unsupervised distributed hierarchical clustering algorithm. The novelty of the proposed approach, named SCCD (standing for synthetic coordinate community detection), lies in the fact that the algorithm is based on the use of Vivaldi synthetic network coordinates computed by a distributed algorithm. The current paper not only presents an efficient distributed community finding algorithm, but also demonstrates that synthetic network coordinates could be used to derive efficient solutions to a variety of problems. Experimental results and comparisons with other methods from the literature are presented for a variety of benchmark graphs with known community structure, derived from varying a number of graph parameters and real data set graphs. The experimental results and comparisons to existing methods with similar computation cost on real and synthetic data sets demonstrate the high performance and robustness of the proposed scheme. (paper)

  17. Social networking for web-based communities

    NARCIS (Netherlands)

    Issa, T.; Kommers, Petrus A.M.

    2013-01-01

    In the 21st century, a new technology was introduced to facilitate communication, collaboration, and interaction between individuals and businesses. This technology is called social networking; this technology is now part of Internet commodities like email, browsing and blogging. From the 20th

  18. Comparison and validation of community structures in complex networks

    Science.gov (United States)

    Gustafsson, Mika; Hörnquist, Michael; Lombardi, Anna

    2006-07-01

    The issue of partitioning a network into communities has attracted a great deal of attention recently. Most authors seem to equate this issue with the one of finding the maximum value of the modularity, as defined by Newman. Since the problem formulated this way is believed to be NP-hard, most effort has gone into the construction of search algorithms, and less to the question of other measures of community structures, similarities between various partitionings and the validation with respect to external information. Here we concentrate on a class of computer generated networks and on three well-studied real networks which constitute a bench-mark for network studies; the karate club, the US college football teams and a gene network of yeast. We utilize some standard ways of clustering data (originally not designed for finding community structures in networks) and show that these classical methods sometimes outperform the newer ones. We discuss various measures of the strength of the modular structure, and show by examples features and drawbacks. Further, we compare different partitions by applying some graph-theoretic concepts of distance, which indicate that one of the quality measures of the degree of modularity corresponds quite well with the distance from the true partition. Finally, we introduce a way to validate the partitionings with respect to external data when the nodes are classified but the network structure is unknown. This is here possible since we know everything of the computer generated networks, as well as the historical answer to how the karate club and the football teams are partitioned in reality. The partitioning of the gene network is validated by use of the Gene Ontology database, where we show that a community in general corresponds to a biological process.

  19. Epidemic spreading in time-varying community networks

    Energy Technology Data Exchange (ETDEWEB)

    Ren, Guangming, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [School of Electronic and Information, Guangdong Polytechnic Normal University, Guangzhou 510665 (China); Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China); Wang, Xingyuan, E-mail: wangxy@dlut.edu.cn, E-mail: ren-guang-ming@163.com [Faculty of Electronic Information and Electrical Engineering, Dalian University of Technology, Dalian 116024 (China)

    2014-06-15

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q{sub c}. The epidemic will survive when q > q{sub c} and die when q < q{sub c}. These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure.

  20. Epidemic spreading in time-varying community networks

    International Nuclear Information System (INIS)

    Ren, Guangming; Wang, Xingyuan

    2014-01-01

    The spreading processes of many infectious diseases have comparable time scale as the network evolution. Here, we present a simple networks model with time-varying community structure, and investigate susceptible-infected-susceptible epidemic spreading processes in this model. By both theoretic analysis and numerical simulations, we show that the efficiency of epidemic spreading in this model depends intensively on the mobility rate q of the individuals among communities. We also find that there exists a mobility rate threshold q c . The epidemic will survive when q > q c and die when q  c . These results can help understanding the impacts of human travel on the epidemic spreading in complex networks with community structure

  1. Network Exposure and Homicide Victimization in an African American Community

    Science.gov (United States)

    Wildeman, Christopher

    2014-01-01

    Objectives. We estimated the association of an individual’s exposure to homicide in a social network and the risk of individual homicide victimization across a high-crime African American community. Methods. Combining 5 years of homicide and police records, we analyzed a network of 3718 high-risk individuals that was created by instances of co-offending. We used logistic regression to model the odds of being a gunshot homicide victim by individual characteristics, network position, and indirect exposure to homicide. Results. Forty-one percent of all gun homicides occurred within a network component containing less than 4% of the neighborhood’s population. Network-level indicators reduced the association between individual risk factors and homicide victimization and improved the overall prediction of individual victimization. Network exposure to homicide was strongly associated with victimization: the closer one is to a homicide victim, the greater the risk of victimization. Regression models show that exposure diminished with social distance: each social tie removed from a homicide victim decreased one’s odds of being a homicide victim by 57%. Conclusions. Risk of homicide in urban areas is even more highly concentrated than previously thought. We found that most of the risk of gun violence was concentrated in networks of identifiable individuals. Understanding these networks may improve prediction of individual homicide victimization within disadvantaged communities. PMID:24228655

  2. Community (in) Colleges: The Relationship Between Online Network Involvement and Academic Outcomes at a Community College

    Science.gov (United States)

    Evans, Eliza D.; McFarland, Daniel A.; Rios-Aguilar, Cecilia; Deil-Amen, Regina

    2016-01-01

    Objective: This study explores the relationship between online social network involvement and academic outcomes among community college students. Prior theory hypothesizes that socio-academic moments are especially important for the integration of students into community colleges and that integration is related to academic outcomes. Online social…

  3. Collaboration: the Key to Establishing Community Networks in Regional Australia

    Directory of Open Access Journals (Sweden)

    Wal Taylor

    2002-01-01

    Full Text Available Despite the promise of community involvement, cohesion and empowerment offered by local community networks (CN using Internet Technologies, few communities in regional Australia have been able to demonstrate sustainable and vibrant CN which demonstrate increased social, cultural or self-reliance capital. The Faculty of Informatics and Communication at Central Queensland University (CQU and a local council have established a formal alliance to establish the COIN (Community Informatics projects to research issues around this topic. This paper presents the initial findings from this work and draws conclusions for possible comparison with other international experience. The research focuses attention on community understanding and cohesion, local government priorities in a community with relatively low diffusion of the Internet and the competing demands in a regional university between traditional service provision in an increasingly competitive market and the needs of establishing outreach research for altruistic, industry establishment and commercial rationale.

  4. Virality Prediction and Community Structure in Social Networks

    Science.gov (United States)

    Weng, Lilian; Menczer, Filippo; Ahn, Yong-Yeol

    2013-08-01

    How does network structure affect diffusion? Recent studies suggest that the answer depends on the type of contagion. Complex contagions, unlike infectious diseases (simple contagions), are affected by social reinforcement and homophily. Hence, the spread within highly clustered communities is enhanced, while diffusion across communities is hampered. A common hypothesis is that memes and behaviors are complex contagions. We show that, while most memes indeed spread like complex contagions, a few viral memes spread across many communities, like diseases. We demonstrate that the future popularity of a meme can be predicted by quantifying its early spreading pattern in terms of community concentration. The more communities a meme permeates, the more viral it is. We present a practical method to translate data about community structure into predictive knowledge about what information will spread widely. This connection contributes to our understanding in computational social science, social media analytics, and marketing applications.

  5. Identifying influential user communities on the social network

    Science.gov (United States)

    Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

    2015-10-01

    Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

  6. Community detection in complex networks using proximate support vector clustering

    Science.gov (United States)

    Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

    2018-03-01

    Community structure, one of the most attention attracting properties in complex networks, has been a cornerstone in advances of various scientific branches. A number of tools have been involved in recent studies concentrating on the community detection algorithms. In this paper, we propose a support vector clustering method based on a proximity graph, owing to which the introduced algorithm surpasses the traditional support vector approach both in accuracy and complexity. Results of extensive experiments undertaken on computer generated networks and real world data sets illustrate competent performances in comparison with the other counterparts.

  7. Optimal Pricing Strategy for Wireless Social Community Networks

    OpenAIRE

    Mazloumian, Amin; Manshaei, Mohammad Hossein; Felegyhazi, Mark; Hubaux, Jean-Pierre

    2008-01-01

    The increasing number of mobile applications fuels the demand for affordable and ubiquitous wireless access. The traditional wireless network technologies such as EV-DO or WiMAX provide this service but require a huge upfront investment in infrastructure and spectrum. On the contrary, as they do not have to face such an investment, social community operators rely on subscribers who constitute a community of users. The pricing strategy of the provided wireless access is an open problem for thi...

  8. Detecting Network Communities: An Application to Phylogenetic Analysis

    Science.gov (United States)

    Andrade, Roberto F. S.; Rocha-Neto, Ivan C.; Santos, Leonardo B. L.; de Santana, Charles N.; Diniz, Marcelo V. C.; Lobão, Thierry Petit; Goés-Neto, Aristóteles; Pinho, Suani T. R.; El-Hani, Charbel N.

    2011-01-01

    This paper proposes a new method to identify communities in generally weighted complex networks and apply it to phylogenetic analysis. In this case, weights correspond to the similarity indexes among protein sequences, which can be used for network construction so that the network structure can be analyzed to recover phylogenetically useful information from its properties. The analyses discussed here are mainly based on the modular character of protein similarity networks, explored through the Newman-Girvan algorithm, with the help of the neighborhood matrix . The most relevant networks are found when the network topology changes abruptly revealing distinct modules related to the sets of organisms to which the proteins belong. Sound biological information can be retrieved by the computational routines used in the network approach, without using biological assumptions other than those incorporated by BLAST. Usually, all the main bacterial phyla and, in some cases, also some bacterial classes corresponded totally (100%) or to a great extent (>70%) to the modules. We checked for internal consistency in the obtained results, and we scored close to 84% of matches for community pertinence when comparisons between the results were performed. To illustrate how to use the network-based method, we employed data for enzymes involved in the chitin metabolic pathway that are present in more than 100 organisms from an original data set containing 1,695 organisms, downloaded from GenBank on May 19, 2007. A preliminary comparison between the outcomes of the network-based method and the results of methods based on Bayesian, distance, likelihood, and parsimony criteria suggests that the former is as reliable as these commonly used methods. We conclude that the network-based method can be used as a powerful tool for retrieving modularity information from weighted networks, which is useful for phylogenetic analysis. PMID:21573202

  9. Modeling information diffusion in time-varying community networks

    Science.gov (United States)

    Cui, Xuelian; Zhao, Narisa

    2017-12-01

    Social networks are rarely static, and they typically have time-varying network topologies. A great number of studies have modeled temporal networks and explored social contagion processes within these models; however, few of these studies have considered community structure variations. In this paper, we present a study of how the time-varying property of a modular structure influences the information dissemination. First, we propose a continuous-time Markov model of information diffusion where two parameters, mobility rate and community attractiveness, are introduced to address the time-varying nature of the community structure. The basic reproduction number is derived, and the accuracy of this model is evaluated by comparing the simulation and theoretical results. Furthermore, numerical results illustrate that generally both the mobility rate and community attractiveness significantly promote the information diffusion process, especially in the initial outbreak stage. Moreover, the strength of this promotion effect is much stronger when the modularity is higher. Counterintuitively, it is found that when all communities have the same attractiveness, social mobility no longer accelerates the diffusion process. In addition, we show that the local spreading in the advantage group has been greatly enhanced due to the agglomeration effect caused by the social mobility and community attractiveness difference, which thus increases the global spreading.

  10. Connectivity and Nestedness in Bipartite Networks from Community Ecology

    International Nuclear Information System (INIS)

    Corso, Gilberto; De Araujo, A I Levartoski; De Almeida, Adriana M

    2011-01-01

    Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index ν. The index ν is found using the relation ν = 1 - τ where τ is the temperature of the adjacency matrix of the bipartite network. By its turn τ is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index ν is a function of the connectivities of the bipartite network. In addition we find a concise way to find ν which avoid cumbersome algorithm manupulation of the adjacency matrix.

  11. Connectivity and Nestedness in Bipartite Networks from Community Ecology

    Energy Technology Data Exchange (ETDEWEB)

    Corso, Gilberto [Departamento de Biofisica e Farmacologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, UFRN - Campus Universitario, Lagoa Nova, CEP 59078 972, Natal, RN (Brazil); De Araujo, A I Levartoski [Instituto Federal de Educacao, Ciencia e Tecnologia do Ceara Av. Treze de Maio, 2081 - Benfica CEP 60040-531 - Fortaleza, CE (Brazil); De Almeida, Adriana M, E-mail: corso@cb.ufrn.br [Departamento de Botanica, Ecologia e Zoologia, Centro de Biociencias, Universidade Federal do Rio Grande do Norte, UFRN - Campus Universitario, Lagoa Nova, CEP 59078 972, Natal, RN (Brazil)

    2011-03-01

    Bipartite networks and the nestedness concept appear in two different contexts in theoretical ecology: community ecology and islands biogeography. From a mathematical perspective nestedness is a pattern in a bipartite network. There are several nestedness indices in the market, we used the index {nu}. The index {nu} is found using the relation {nu} = 1 - {tau} where {tau} is the temperature of the adjacency matrix of the bipartite network. By its turn {tau} is defined with help of the Manhattan distance of the occupied elements of the adjacency matrix of the bipartite network. We prove that the nestedness index {nu} is a function of the connectivities of the bipartite network. In addition we find a concise way to find {nu} which avoid cumbersome algorithm manupulation of the adjacency matrix.

  12. 78 FR 78318 - Television Broadcasting Services; Oklahoma City, Oklahoma

    Science.gov (United States)

    2013-12-26

    ...: Proposed rule. SUMMARY: The Commission has before it a petition for rulemaking filed by Family Broadcasting Group, Inc. (``Family Broadcasting''), the licensee of station KSBI(TV), channel 51, Oklahoma City... instituted a freeze on the acceptance of full power television rulemaking petitions requesting channel...

  13. Fragmentation alters stream fish community structure in dendritic ecological networks.

    Science.gov (United States)

    Perkin, Joshuah S; Gido, Keith B

    2012-12-01

    Effects of fragmentation on the ecology of organisms occupying dendritic ecological networks (DENs) have recently been described through both conceptual and mathematical models, but few hypotheses have been tested in complex, real-world ecosystems. Stream fishes provide a model system for assessing effects of fragmentation on the structure of communities occurring within DENs, including how fragmentation alters metacommunity dynamics and biodiversity. A recently developed habitat-availability measure, the "dendritic connectivity index" (DCI), allows for assigning quantitative measures of connectivity in DENs regardless of network extent or complexity, and might be used to predict fish community response to fragmentation. We characterized stream fish community structure in 12 DENs in the Great Plains, USA, during periods of dynamic (summer) and muted (fall) discharge regimes to test the DCI as a predictive model of fish community response to fragmentation imposed by road crossings. Results indicated that fish communities in stream segments isolated by road crossings had reduced species richness (alpha diversity) relative to communities that maintained connectivity with the surrounding DEN during summer and fall. Furthermore, isolated communities had greater dissimilarity (beta diversity) to downstream sites notisolated by road crossings during summer and fall. Finally, dissimilarity among communities within DENs decreased as a function of increased habitat connectivity (measured using the DCI) for summer and fall, suggesting that communities within highly connected DENs tend to be more homogeneous. Our results indicate that the DCI is sensitive to community effects of fragmentation in riverscapes and might be used by managers to predict ecological responses to changes in habitat connectivity. Moreover, our findings illustrate that relating structural connectivity of riverscapes to functional connectivity among communities might aid in maintaining metacommunity

  14. Using Earthquake Analysis to Expand the Oklahoma Fault Database

    Science.gov (United States)

    Chang, J. C.; Evans, S. C.; Walter, J. I.

    2017-12-01

    The Oklahoma Geological Survey (OGS) is compiling a comprehensive Oklahoma Fault Database (OFD), which includes faults mapped in OGS publications, university thesis maps, and industry-contributed shapefiles. The OFD includes nearly 20,000 fault segments, but the work is far from complete. The OGS plans on incorporating other sources of data into the OFD, such as new faults from earthquake sequence analyses, geologic field mapping, active-source seismic surveys, and potential fields modeling. A comparison of Oklahoma seismicity and the OFD reveals that earthquakes in the state appear to nucleate on mostly unmapped or unknown faults. Here, we present faults derived from earthquake sequence analyses. From 2015 to present, there has been a five-fold increase in realtime seismic stations in Oklahoma, which has greatly expanded and densified the state's seismic network. The current seismic network not only improves our threshold for locating weaker earthquakes, but also allows us to better constrain focal plane solutions (FPS) from first motion analyses. Using nodal planes from the FPS, HypoDD relocation, and historic seismic data, we can elucidate these previously unmapped seismogenic faults. As the OFD is a primary resource for various scientific investigations, the inclusion of seismogenic faults improves further derivative studies, particularly with respect to seismic hazards. Our primal focus is on four areas of interest, which have had M5+ earthquakes in recent Oklahoma history: Pawnee (M5.8), Prague (M5.7), Fairview (M5.1), and Cushing (M5.0). Subsequent areas of interest will include seismically active data-rich areas, such as the central and northcentral parts of the state.

  15. Model of community emergence in weighted social networks

    Science.gov (United States)

    Kumpula, J. M.; Onnela, J.-P.; Saramäki, J.; Kertész, J.; Kaski, K.

    2009-04-01

    Over the years network theory has proven to be rapidly expanding methodology to investigate various complex systems and it has turned out to give quite unparalleled insight to their structure, function, and response through data analysis, modeling, and simulation. For social systems in particular the network approach has empirically revealed a modular structure due to interplay between the network topology and link weights between network nodes or individuals. This inspired us to develop a simple network model that could catch some salient features of mesoscopic community and macroscopic topology formation during network evolution. Our model is based on two fundamental mechanisms of network sociology for individuals to find new friends, namely cyclic closure and focal closure, which are mimicked by local search-link-reinforcement and random global attachment mechanisms, respectively. In addition we included to the model a node deletion mechanism by removing all its links simultaneously, which corresponds for an individual to depart from the network. Here we describe in detail the implementation of our model algorithm, which was found to be computationally efficient and produce many empirically observed features of large-scale social networks. Thus this model opens a new perspective for studying such collective social phenomena as spreading, structure formation, and evolutionary processes.

  16. The Healthy Aging Research Network: Modeling Collaboration for Community Impact.

    Science.gov (United States)

    Belza, Basia; Altpeter, Mary; Smith, Matthew Lee; Ory, Marcia G

    2017-03-01

    As the first Centers for Disease Control and Prevention (CDC) Prevention Research Centers Program thematic network, the Healthy Aging Research Network was established to better understand the determinants of healthy aging within older adult populations, identify interventions that promote healthy aging, and assist in translating research into sustainable community-based programs throughout the nation. To achieve these goals requires concerted efforts of a collaborative network of academic, community, and public health organizational partnerships. For the 2001-2014 Prevention Research Center funding cycles, the Healthy Aging Research Network conducted prevention research and promoted the wide use of practices known to foster optimal health. Organized around components necessary for successful collaborations (i.e., governance and infrastructure, shaping focus, community involvement, and evaluation and improvement), this commentary highlights exemplars that demonstrate the Healthy Aging Research Network's unique contributions to the field. The Healthy Aging Research Network's collaboration provided a means to collectively build capacity for practice and policy, reduce fragmentation and duplication in health promotion and aging research efforts, maximize the efficient use of existing resources and generate additional resources, and ultimately, create synergies for advancing the healthy aging agenda. This collaborative model was built upon a backbone organization (coordinating center); setting of common agendas and mutually reinforcing activities; and continuous communications. Given its successes, the Healthy Aging Research Network model could be used to create new and evaluate existing thematic networks to guide the translation of research into policy and practice. Copyright © 2016 American Journal of Preventive Medicine. Published by Elsevier Inc. All rights reserved.

  17. Finding and testing network communities by lumped Markov chains.

    Science.gov (United States)

    Piccardi, Carlo

    2011-01-01

    Identifying communities (or clusters), namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster, which is then defined as an "α-community" if such a probability is not smaller than α. Consistently, a partition composed of α-communities is an "α-partition." These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired α-level allows one to immediately select the α-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.

  18. Finding and testing network communities by lumped Markov chains.

    Directory of Open Access Journals (Sweden)

    Carlo Piccardi

    Full Text Available Identifying communities (or clusters, namely groups of nodes with comparatively strong internal connectivity, is a fundamental task for deeply understanding the structure and function of a network. Yet, there is a lack of formal criteria for defining communities and for testing their significance. We propose a sharp definition that is based on a quality threshold. By means of a lumped Markov chain model of a random walker, a quality measure called "persistence probability" is associated to a cluster, which is then defined as an "α-community" if such a probability is not smaller than α. Consistently, a partition composed of α-communities is an "α-partition." These definitions turn out to be very effective for finding and testing communities. If a set of candidate partitions is available, setting the desired α-level allows one to immediately select the α-partition with the finest decomposition. Simultaneously, the persistence probabilities quantify the quality of each single community. Given its ability in individually assessing each single cluster, this approach can also disclose single well-defined communities even in networks that overall do not possess a definite clusterized structure.

  19. Temporal prediction of epidemic patterns in community networks

    International Nuclear Information System (INIS)

    Peng, Xiao-Long; Xu, Xin-Jian; Fu, Xinchu; Small, Michael

    2013-01-01

    Most previous studies of epidemic dynamics on complex networks suppose that the disease will eventually stabilize at either a disease-free state or an endemic one. In reality, however, some epidemics always exhibit sporadic and recurrent behaviour in one region because of the invasion from an endemic population elsewhere. In this paper we address this issue and study a susceptible–infected–susceptible epidemiological model on a network consisting of two communities, where the disease is endemic in one community but alternates between outbreaks and extinctions in the other. We provide a detailed characterization of the temporal dynamics of epidemic patterns in the latter community. In particular, we investigate the time duration of both outbreak and extinction, and the time interval between two consecutive inter-community infections, as well as their frequency distributions. Based on the mean-field theory, we theoretically analyse these three timescales and their dependence on the average node degree of each community, the transmission parameters and the number of inter-community links, which are in good agreement with simulations, except when the probability of overlaps between successive outbreaks is too large. These findings aid us in better understanding the bursty nature of disease spreading in a local community, and thereby suggesting effective time-dependent control strategies. (paper)

  20. Simpson-Arbuckle contact revisited in Northwest Oklahoma County, Oklahoma

    Energy Technology Data Exchange (ETDEWEB)

    Allison, M.D.; Allen, R.W. [Kabodi Inc., Ardmore, OK (United States)

    1995-09-01

    The Joins Formation, the lowermost formation of the Simpson Group, is traditionally the least studied or understood of the Simpson formations. The Joins, not known to produce hydrocarbons in central Oklahoma, is frequently overlooked by those more interested in the productive Simpson formations above and the Arbuckle carbonates below. In a study of the lower Simpson to upper Arbuckle interval in northwestern Oklahoma County, Oklahoma, the Joins Formation was found to be present. The central Oklahoma section consists of interbedded gray, olive gray and green splintery moderately waxy shale, cream to light gray homogeneous microcrystallin dolomite, and microcrystalline to fine crystalline fossiliferous slightly glauconitic well cemented sandstones are also noted. The entire Joins Formation is moderately to very fossiliferous; primarily consisting of crinoids, ostracods, brachiopods, and trilobites. The ostracod fauna closely resembles and correlates with the Arbuckle Mountain section, which has been extensively studied over the years by such authors as Taff, Ulrich and Harris. Beneath the Joins in this area is a normal section of Arbuckle dolomites. Due to the absence of a basal sand in the Joins the separation of the Joins and Arbuckle, utilizing electric logs only, is frequently tenuous. In comparison with the Arbuckle, the Joins tends to have higher gamma ray and S.P. values. Other tools, such as resistivity, bulk density and photoelectric (PE), are frequently inconclusive. For geologists studying the Simpson-Arbuckle contact in central Oklahoma, the presence or absence of the Joins Formation is best determined through conventional lithologic and palenontologic sample identification techniques. Once this has been done, correlation of electric logs with this type log is possible for the local area.

  1. Audit Trail Management System in Community Health Care Information Network.

    Science.gov (United States)

    Nakamura, Naoki; Nakayama, Masaharu; Nakaya, Jun; Tominaga, Teiji; Suganuma, Takuo; Shiratori, Norio

    2015-01-01

    After the Great East Japan Earthquake we constructed a community health care information network system. Focusing on the authentication server and portal server capable of SAML&ID-WSF, we proposed an audit trail management system to look over audit events in a comprehensive manner. Through implementation and experimentation, we verified the effectiveness of our proposed audit trail management system.

  2. Water Distribution Network Modelling of a Small Community using ...

    African Journals Online (AJOL)

    ... of a small community (Sakwa) water distribution network in North Eastern geopolitical region of Nigeria using WaterCAD simulator. The analysis included a review of pressures, velocities and head loss gradients under steady state average day demand, maximum day demand conditions, and fire flow under maximum day ...

  3. Social Networks and Performance in Distributed Learning Communities

    Science.gov (United States)

    Cadima, Rita; Ojeda, Jordi; Monguet, Josep M.

    2012-01-01

    Social networks play an essential role in learning environments as a key channel for knowledge sharing and students' support. In distributed learning communities, knowledge sharing does not occur as spontaneously as when a working group shares the same physical space; knowledge sharing depends even more on student informal connections. In this…

  4. Consensus-based methodology for detection communities in multilayered networks

    Science.gov (United States)

    Karimi-Majd, Amir-Mohsen; Fathian, Mohammad; Makrehchi, Masoud

    2018-03-01

    Finding groups of network users who are densely related with each other has emerged as an interesting problem in the area of social network analysis. These groups or so-called communities would be hidden behind the behavior of users. Most studies assume that such behavior could be understood by focusing on user interfaces, their behavioral attributes or a combination of these network layers (i.e., interfaces with their attributes). They also assume that all network layers refer to the same behavior. However, in real-life networks, users' behavior in one layer may differ from their behavior in another one. In order to cope with these issues, this article proposes a consensus-based community detection approach (CBC). CBC finds communities among nodes at each layer, in parallel. Then, the results of layers should be aggregated using a consensus clustering method. This means that different behavior could be detected and used in the analysis. As for other significant advantages, the methodology would be able to handle missing values. Three experiments on real-life and computer-generated datasets have been conducted in order to evaluate the performance of CBC. The results indicate superiority and stability of CBC in comparison to other approaches.

  5. Fighting for Scholarships in Oklahoma.

    Science.gov (United States)

    Roach, Ronald

    1999-01-01

    Fearing a federal court in Oklahoma might end a state-financed merit-scholarship program targeted by a discrimination lawsuit, black legislators passed a bill making the program race and gender neutral. State regents are criticized for failing to develop effective policy to remedy past discrimination. (MSE)

  6. Residential Energy Efficiency Potential: Oklahoma

    Energy Technology Data Exchange (ETDEWEB)

    Wilson, Eric J [National Renewable Energy Laboratory (NREL), Golden, CO (United States)

    2017-11-02

    Energy used by Oklahoma single-family homes that can be saved through cost-effective improvements. Prepared by Eric Wilson and Noel Merket, NREL, and Erin Boyd, U.S. Department of Energy Office of Energy Policy and Systems Analysis.

  7. Hyperbolic mapping of complex networks based on community information

    Science.gov (United States)

    Wang, Zuxi; Li, Qingguang; Jin, Fengdong; Xiong, Wei; Wu, Yao

    2016-08-01

    To improve the hyperbolic mapping methods both in terms of accuracy and running time, a novel mapping method called Community and Hyperbolic Mapping (CHM) is proposed based on community information in this paper. Firstly, an index called Community Intimacy (CI) is presented to measure the adjacency relationship between the communities, based on which a community ordering algorithm is introduced. According to the proposed Community-Sector hypothesis, which supposes that most nodes of one community gather in a same sector in hyperbolic space, CHM maps the ordered communities into hyperbolic space, and then the angular coordinates of nodes are randomly initialized within the sector that they belong to. Therefore, all the network nodes are so far mapped to hyperbolic space, and then the initialized angular coordinates can be optimized by employing the information of all nodes, which can greatly improve the algorithm precision. By applying the proposed dual-layer angle sampling method in the optimization procedure, CHM reduces the time complexity to O(n2) . The experiments show that our algorithm outperforms the state-of-the-art methods.

  8. Addressing cancer disparities via community network mobilization and intersectoral partnerships: a social network analysis.

    Directory of Open Access Journals (Sweden)

    Shoba Ramanadhan

    Full Text Available Community mobilization and collaboration among diverse partners are vital components of the effort to reduce and eliminate cancer disparities in the United States. We studied the development and impact of intersectoral connections among the members of the Massachusetts Community Network for Cancer Education, Research, and Training (MassCONECT. As one of the Community Network Program sites funded by the National Cancer Institute, this infrastructure-building initiative utilized principles of Community-based Participatory Research (CBPR to unite community coalitions, researchers, policymakers, and other important stakeholders to address cancer disparities in three Massachusetts communities: Boston, Lawrence, and Worcester. We conducted a cross-sectional, sociometric network analysis four years after the network was formed. A total of 38 of 55 members participated in the study (69% response rate. Over four years of collaboration, the number of intersectoral connections reported by members (intersectoral out-degree increased, as did the extent to which such connections were reported reciprocally (intersectoral reciprocity. We assessed relationships between these markers of intersectoral collaboration and three intermediate outcomes in the effort to reduce and eliminate cancer disparities: delivery of community activities, policy engagement, and grants/publications. We found a positive and statistically significant relationship between intersectoral out-degree and community activities and policy engagement (the relationship was borderline significant for grants/publications. We found a positive and statistically significant relationship between intersectoral reciprocity and community activities and grants/publications (the relationship was borderline significant for policy engagement. The study suggests that intersectoral connections may be important drivers of diverse intermediate outcomes in the effort to reduce and eliminate cancer disparities

  9. On a new concept of community: social networks, personal communities and collective intelligence

    Directory of Open Access Journals (Sweden)

    Rogério da Costa

    2006-01-01

    Full Text Available This text essentially deals with the transmutation of the concept of "community" into "social networks". This change is due largely to the boom of virtual communities in cyberspace, a fact that has generated a number of studies not only on this new way of weaving a society, but also on the dynamic structure of communication networks. At the core of this transformation, concepts such as social capital, trust and partial sympathy are called upon, to enable us to think about the new forms of association that regulate human activity in our time.

  10. A complex network based model for detecting isolated communities in water distribution networks

    Science.gov (United States)

    Sheng, Nan; Jia, Youwei; Xu, Zhao; Ho, Siu-Lau; Wai Kan, Chi

    2013-12-01

    Water distribution network (WDN) is a typical real-world complex network of major infrastructure that plays an important role in human's daily life. In this paper, we explore the formation of isolated communities in WDN based on complex network theory. A graph-algebraic model is proposed to effectively detect the potential communities due to pipeline failures. This model can properly illustrate the connectivity and evolution of WDN during different stages of contingency events, and identify the emerging isolated communities through spectral analysis on Laplacian matrix. A case study on a practical urban WDN in China is conducted, and the consistency between the simulation results and the historical data are reported to showcase the feasibility and effectiveness of the proposed model.

  11. An Overlapping Communities Detection Algorithm via Maxing Modularity in Opportunistic Networks

    Directory of Open Access Journals (Sweden)

    Gao Zhi-Peng

    2016-01-01

    Full Text Available Community detection in opportunistic networks has been a significant and hot issue, which is used to understand characteristics of networks through analyzing structure of it. Community is used to represent a group of nodes in a network where nodes inside the community have more internal connections than external connections. However, most of the existing community detection algorithms focus on binary networks or disjoint community detection. In this paper, we propose a novel algorithm via maxing modularity of communities (MMCto find overlapping community structure in opportunistic networks. It utilizes contact history of nodes to calculate the relation intensity between nodes. It finds nodes with high relation intensity as the initial community and extend the community with nodes of higher belong degree. The algorithm achieves a rapid and efficient overlapping community detection method by maxing the modularity of community continuously. The experiments prove that MMC is effective for uncovering overlapping communities and it achieves better performance than COPRA and Conductance.

  12. A Community Network of 100 Black Carbon Sensors

    Science.gov (United States)

    Preble, C.; Kirchstetter, T.; Caubel, J.; Cados, T.; Keeling, C.; Chang, S.

    2017-12-01

    We developed a low-cost black carbon sensor, field tested its performance, and then built and deployed a network of 100 sensors in West Oakland, California. We operated the network for 100 days beginning mid-May 2017 to measure spatially resolved black carbon concentrations throughout the community. West Oakland is a San Francisco Bay Area mixed residential and industrial community that is adjacent to regional port and rail yard facilities and surrounded by major freeways. As such, the community is affected by diesel particulate matter emissions from heavy-duty diesel trucks, locomotives, and ships associated with freight movement. In partnership with Environmental Defense Fund, the Bay Area Air Quality Management District, and the West Oakland Environmental Indicators Project, we deployed the black carbon monitoring network outside of residences and business, along truck routes and arterial streets, and at upwind locations. The sensor employs the filter-based light transmission method to measure black carbon and has good precision and correspondence with current commercial black carbon instruments. Throughout the 100-day period, each of the 100 sensors transmitted data via a cellular network. A MySQL database was built to receive and manage the data in real-time. The database included diagnostic features to monitor each sensor's operational status and facilitate the maintenance of the network. Spatial and temporal patterns in black carbon concentrations will be presented, including patterns around industrial facilities, freeways, and truck routes, as well as the relationship between neighborhood concentrations and the BAAQMD's monitoring site. Lessons learned during this first of its kind black carbon monitoring network will also be shared.

  13. - Oklahoma Water Resources Center

    Science.gov (United States)

    Development Ag Business Community & Rural Development Crops Family & Consumer Sciences Gardening Family & Consumer Sciences Food & Ag Products Center Horticulture & Landscape Architecture & Landscape Architecture Natural Resource Ecology & Management Plant & Soil Sciences

  14. Guifi.net: Security analysis of a heterogeneous community network

    OpenAIRE

    Ramos García, Patricia

    2018-01-01

    Guifi.net is a heterogeneous community network that brings Internet to rural areas or vulnerable groups. This opens the door to many advances, but encompasses some risks as well. The aim of this project is to assess the general security of Guifi.net from tests performed on a key network element: the router. In particular, MikroTik and Ubiquiti are the most used makes in Guifi.net and hence, the target of this project. Basic, yet important, security settings are tested. On the plus side, the ...

  15. Who Networks? The Social Psychology of Virtual Communities

    Science.gov (United States)

    2004-06-01

    Massively-Multiplayer Online Role-Playing Game ( MMORPG ). One of the strongest attractions of EverQuest is the opportunity to engage in fantasy identity...another but,you can catch me mostly in 30’s but if i am not there you can’t catch me anywhere. I am addicted to Pogo too :oP (sic) However, community...their arrest. 1. Network Capital In order to feed their addiction using the Internet, pedophiles display all of the elements of network capital

  16. Interest communities and flow roles in directed networks: the Twitter network of the UK riots.

    Science.gov (United States)

    Beguerisse-Díaz, Mariano; Garduño-Hernández, Guillermo; Vangelov, Borislav; Yaliraki, Sophia N; Barahona, Mauricio

    2014-12-06

    Directionality is a crucial ingredient in many complex networks in which information, energy or influence are transmitted. In such directed networks, analysing flows (and not only the strength of connections) is crucial to reveal important features of the network that might go undetected if the orientation of connections is ignored. We showcase here a flow-based approach for community detection through the study of the network of the most influential Twitter users during the 2011 riots in England. Firstly, we use directed Markov Stability to extract descriptions of the network at different levels of coarseness in terms of interest communities, i.e. groups of nodes within which flows of information are contained and reinforced. Such interest communities reveal user groupings according to location, profession, employer and topic. The study of flows also allows us to generate an interest distance, which affords a personalized view of the attention in the network as viewed from the vantage point of any given user. Secondly, we analyse the profiles of incoming and outgoing long-range flows with a combined approach of role-based similarity and the novel relaxed minimum spanning tree algorithm to reveal that the users in the network can be classified into five roles. These flow roles go beyond the standard leader/follower dichotomy and differ from classifications based on regular/structural equivalence. We then show that the interest communities fall into distinct informational organigrams characterized by a different mix of user roles reflecting the quality of dialogue within them. Our generic framework can be used to provide insight into how flows are generated, distributed, preserved and consumed in directed networks.

  17. A community of practice: librarians in a biomedical research network.

    Science.gov (United States)

    De Jager-Loftus, Danielle P; Midyette, J David; Harvey, Barbara

    2014-01-01

    Providing library and reference services within a biomedical research community presents special challenges for librarians, especially those in historically lower-funded states. These challenges can include understanding needs, defining and communicating the library's role, building relationships, and developing and maintaining general and subject specific knowledge. This article describes a biomedical research network and the work of health sciences librarians at the lead intensive research institution with librarians from primarily undergraduate institutions and tribal colleges. Applying the concept of a community of practice to a collaborative effort suggests how librarians can work together to provide effective reference services to researchers in biomedicine.

  18. Efficient community-based control strategies in adaptive networks

    International Nuclear Information System (INIS)

    Yang Hui; Tang Ming; Zhang Haifeng

    2012-01-01

    Most studies on adaptive networks concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structure in the transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing the modularity, we investigate the evolution of community structure during the transient process, and find that a strong community structure is induced by the rewiring mechanism in the early stage of epidemic dynamics, which, remarkably, delays the outbreak of disease. We then study the effects of control strategies started at different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not ‘the earlier, the better’ for the implementation of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of a strong community structure. For the immunization strategy, immunizing the susceptible nodes on susceptible–infected links and immunizing susceptible nodes randomly have similar control effects. However, for the quarantine strategy, quarantining the infected nodes on susceptible–infected links can yield a far better result than quarantining infected nodes randomly. More significantly, the community-based quarantine strategy performs better than the community-based immunization strategy. This study may shed new light on the forecast and the prevention of epidemics among humans. (paper)

  19. 75 FR 68398 - Texas, Oklahoma & Eastern Railroad, LLC-Acquisition and Operation Exemption-Texas, Oklahoma...

    Science.gov (United States)

    2010-11-05

    ... & Eastern Railroad, LLC--Acquisition and Operation Exemption--Texas, Oklahoma & Eastern Railroad Company Texas, Oklahoma & Eastern Railroad, LLC (TOE), a noncarrier, has filed a verified notice of exemption under 49 CFR 1150.31 to acquire from Texas, Oklahoma & Eastern Railroad Company and to operate...

  20. Community, Collective or Movement? Evaluating Theoretical Perspectives on Network Building

    Science.gov (United States)

    Spitzer, W.

    2015-12-01

    Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. What is the most useful theoretical model for conceptualizing the work of the NNOCCI community? This presentation will examine the pros and cons of three perspectives -- community of practice, collective impact, and social movements. The community of practice approach emphasizes use of common tools, support for practice, social learning, and organic development of leadership. A collective impact model focuses on defining common outcomes, aligning activities toward a common goal, structured collaboration. A social movement emphasizes building group identity and creating a sense of group efficacy. This presentation will address how these models compare in terms of their utility in program planning and evaluation, their fit with the unique characteristics of the NNOCCI community, and their relevance to our program goals.

  1. Community detection, link prediction, and layer interdependence in multilayer networks

    Science.gov (United States)

    De Bacco, Caterina; Power, Eleanor A.; Larremore, Daniel B.; Moore, Cristopher

    2017-04-01

    Complex systems are often characterized by distinct types of interactions between the same entities. These can be described as a multilayer network where each layer represents one type of interaction. These layers may be interdependent in complicated ways, revealing different kinds of structure in the network. In this work we present a generative model, and an efficient expectation-maximization algorithm, which allows us to perform inference tasks such as community detection and link prediction in this setting. Our model assumes overlapping communities that are common between the layers, while allowing these communities to affect each layer in a different way, including arbitrary mixtures of assortative, disassortative, or directed structure. It also gives us a mathematically principled way to define the interdependence between layers, by measuring how much information about one layer helps us predict links in another layer. In particular, this allows us to bundle layers together to compress redundant information and identify small groups of layers which suffice to predict the remaining layers accurately. We illustrate these findings by analyzing synthetic data and two real multilayer networks, one representing social support relationships among villagers in South India and the other representing shared genetic substring material between genes of the malaria parasite.

  2. Fault Lines: Seismicity and the Fracturing of Energy Narratives in Oklahoma

    Science.gov (United States)

    Grubert, E.; Drummond, V. A.; Brandt, A. R.

    2016-12-01

    Fault Lines: Seismicity and the Fracturing of Energy Narratives in Oklahoma Virginia Drummond1, Emily Grubert21Stanford University, Stanford Earth Summer Undergraduate Research Program2Stanford University, Emmett Interdisciplinary Program in Environment and ResourcesOklahoma is an oil state where residents have historically been supportive of the oil and gas industry. However, a dramatic increase in seismic activity between 2009 and 2015 widely attributed to wastewater injection associated with oil production is a new and highly salient consequence of oil development, affecting local communities' relationship to the environment and to the oil industry. Understanding how seismicity plays into Oklahoma's evolving dialogue about energy is integral to understanding both the current realities and the future of energy communities in Oklahoma.This research engages Oklahoma residents through open-ended interviews and mixed quantitative-qualitative survey research to characterize how energy narratives shape identity in response to conflict between environmental outcomes and economic interest. We perform approximately 20 interviews with residents of Oklahoma, with particular attention to recruiting residents from a wide range of age groups and who work either within or outside the oil and gas industry. General population surveys supplementing detailed interviews with information about community characteristics, social and environmental priorities, and experience with hazards are delivered to residents selected at random from zip codes known to have experienced significant seismicity. We identify narratives used by residents in response to tension between economic and environmental concerns, noting Oklahoma as an interesting case study for how a relatively pro-industry community reacts to and reframes its relationship with energy development, given conflict. In particular, seismicity has fractured the dominant narrative of oil development as positive into new narratives

  3. Mass media influence spreading in social networks with community structure

    Science.gov (United States)

    Candia, Julián; Mazzitello, Karina I.

    2008-07-01

    We study an extension of Axelrod's model for social influence, in which cultural drift is represented as random perturbations, while mass media are introduced by means of an external field. In this scenario, we investigate how the modular structure of social networks affects the propagation of mass media messages across a society. The community structure of social networks is represented by coupled random networks, in which two random graphs are connected by intercommunity links. Considering inhomogeneous mass media fields, we study the conditions for successful message spreading and find a novel phase diagram in the multidimensional parameter space. These findings show that social modularity effects are of paramount importance for designing successful, cost-effective advertising campaigns.

  4. Local community detection as pattern restoration by attractor dynamics of recurrent neural networks.

    Science.gov (United States)

    Okamoto, Hiroshi

    2016-08-01

    Densely connected parts in networks are referred to as "communities". Community structure is a hallmark of a variety of real-world networks. Individual communities in networks form functional modules of complex systems described by networks. Therefore, finding communities in networks is essential to approaching and understanding complex systems described by networks. In fact, network science has made a great deal of effort to develop effective and efficient methods for detecting communities in networks. Here we put forward a type of community detection, which has been little examined so far but will be practically useful. Suppose that we are given a set of source nodes that includes some (but not all) of "true" members of a particular community; suppose also that the set includes some nodes that are not the members of this community (i.e., "false" members of the community). We propose to detect the community from this "imperfect" and "inaccurate" set of source nodes using attractor dynamics of recurrent neural networks. Community detection by the proposed method can be viewed as restoration of the original pattern from a deteriorated pattern, which is analogous to cue-triggered recall of short-term memory in the brain. We demonstrate the effectiveness of the proposed method using synthetic networks and real social networks for which correct communities are known. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

  5. Structural and Geophysical Characterization of Oklahoma Basement

    Science.gov (United States)

    Morgan, C.; Johnston, C. S.; Carpenter, B. M.; Reches, Z.

    2017-12-01

    Oklahoma has experienced a large increase in seismicity since 2009 that has been attributed to wastewater injection. Most earthquakes, including four M5+ earthquakes, nucleated at depths > 4 km, well within the pre-Cambrian crystalline basement, even though wastewater injection occurred almost exclusively in the sedimentary sequence above. To better understand the structural characteristics of the rhyolite and granite that makeup the midcontinent basement, we analyzed a 150 m long core recovered from a basement borehole (Shads 4) in Rogers County, NE Oklahoma. The analysis of the fracture network in the rhyolite core included measurements of fracture inclination, aperture, and density, the examination fracture surface features and fill minerology, as well as x-ray diffraction analysis of secondary mineralization. We also analyzed the highly fractured and faulted segments of the core with a portable gamma-ray detector, magnetometer, and rebound hammer. The preliminary analysis of the fractures within the rhyolite core showed: (1) Fracture density increasing with depth by a factor of 10, from 4 fractures/10m in the upper core segment to 40 fracture/10m at 150 m deeper. (2) The fractures are primarily sub-vertical, inclined 10-20° from the axis of the vertical core. (3) The secondary mineralization is dominated by calcite and epidote. (4) Fracture aperture ranges from 0.35 to 2.35mm based on the thickness of secondary filling. (5) About 8% of the examined fractures display slickenside striations. (6) Increases of elasticity (by rebound hammer) and gamma-ray emissions are systematically correlated with a decrease in magnetic susceptibility in core segments of high fracture density and/or faulting; this observation suggests diagenetic fracture re-mineralization.

  6. Vortex network community based reduced-order force model

    Science.gov (United States)

    Gopalakrishnan Meena, Muralikrishnan; Nair, Aditya; Taira, Kunihiko

    2017-11-01

    We characterize the vortical wake interactions by utilizing network theory and cluster-based approaches, and develop a data-inspired unsteady force model. In the present work, the vortical interaction network is defined by nodes representing vortical elements and the edges quantified by induced velocity measures amongst the vortices. The full vorticity field is reduced to a finite number of vortical clusters based on network community detection algorithm, which serves as a basis for a skeleton network that captures the essence of the wake dynamics. We use this reduced representation of the wake to develop a data-inspired reduced-order force model that can predict unsteady fluid forces on the body. The overall formulation is demonstrated for laminar flows around canonical bluff body wake and stalled flow over an airfoil. We also show the robustness of the present network-based model against noisy data, which motivates applications towards turbulent flows and experimental measurements. Supported by the National Science Foundation (Grant 1632003).

  7. Stochastic fluctuations and the detectability limit of network communities.

    Science.gov (United States)

    Floretta, Lucio; Liechti, Jonas; Flammini, Alessandro; De Los Rios, Paolo

    2013-12-01

    We have analyzed the detectability limits of network communities in the framework of the popular Girvan and Newman benchmark. By carefully taking into account the inevitable stochastic fluctuations that affect the construction of each and every instance of the benchmark, we come to the conclusion that the native, putative partition of the network is completely lost even before the in-degree/out-degree ratio becomes equal to that of a structureless Erdös-Rényi network. We develop a simple iterative scheme, analytically well described by an infinite branching process, to provide an estimate of the true detectability limit. Using various algorithms based on modularity optimization, we show that all of them behave (semiquantitatively) in the same way, with the same functional form of the detectability threshold as a function of the network parameters. Because the same behavior has also been found by further modularity-optimization methods and for methods based on different heuristics implementations, we conclude that indeed a correct definition of the detectability limit must take into account the stochastic fluctuations of the network construction.

  8. Multilabel user classification using the community structure of online networks.

    Science.gov (United States)

    Rizos, Georgios; Papadopoulos, Symeon; Kompatsiaris, Yiannis

    2017-01-01

    We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE), an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  9. Multilabel user classification using the community structure of online networks.

    Directory of Open Access Journals (Sweden)

    Georgios Rizos

    Full Text Available We study the problem of semi-supervised, multi-label user classification of networked data in the online social platform setting. We propose a framework that combines unsupervised community extraction and supervised, community-based feature weighting before training a classifier. We introduce Approximate Regularized Commute-Time Embedding (ARCTE, an algorithm that projects the users of a social graph onto a latent space, but instead of packing the global structure into a matrix of predefined rank, as many spectral and neural representation learning methods do, it extracts local communities for all users in the graph in order to learn a sparse embedding. To this end, we employ an improvement of personalized PageRank algorithms for searching locally in each user's graph structure. Then, we perform supervised community feature weighting in order to boost the importance of highly predictive communities. We assess our method performance on the problem of user classification by performing an extensive comparative study among various recent methods based on graph embeddings. The comparison shows that ARCTE significantly outperforms the competition in almost all cases, achieving up to 35% relative improvement compared to the second best competing method in terms of F1-score.

  10. The Internet and Community Networks: Case Studies of Five U.S. Cities

    OpenAIRE

    Horrigan, John B.

    2001-01-01

    This paper looks at five U.S. cities (Austin, Cleveland, Nashville, Portland, and Washington, DC) and explores strategies being employed by community activists and local governments to create and sustain community networking projects. In some cities, community networking initiatives are relatively mature, while in others they are in early or intermediate stages. The paper looks at several factors that help explain the evolution of community networks in cities: 1) Local government support; 2) ...

  11. Epidemic spreading on complex networks with overlapping and non-overlapping community structure

    Science.gov (United States)

    Shang, Jiaxing; Liu, Lianchen; Li, Xin; Xie, Feng; Wu, Cheng

    2015-02-01

    Many real-world networks exhibit community structure where vertices belong to one or more communities. Recent studies show that community structure plays an import role in epidemic spreading. In this paper, we investigate how the extent of overlap among communities affects epidemics. In order to experiment on the characteristic of overlapping communities, we propose a rewiring algorithm that can change the community structure from overlapping to non-overlapping while maintaining the degree distribution of the network. We simulate the Susceptible-Infected-Susceptible (SIS) epidemic process on synthetic scale-free networks and real-world networks by applying our rewiring algorithm. Experiments show that epidemics spread faster on networks with higher level of overlapping communities. Furthermore, overlapping communities' effect interacts with the average degree's effect. Our work further illustrates the important role of overlapping communities in the process of epidemic spreading.

  12. Proceedings of the Neural Network Workshop for the Hanford Community

    Energy Technology Data Exchange (ETDEWEB)

    Keller, P.E.

    1994-01-01

    These proceedings were generated from a series of presentations made at the Neural Network Workshop for the Hanford Community. The abstracts and viewgraphs of each presentation are reproduced in these proceedings. This workshop was sponsored by the Computing and Information Sciences Department in the Molecular Science Research Center (MSRC) at the Pacific Northwest Laboratory (PNL). Artificial neural networks constitute a new information processing technology that is destined within the next few years, to provide the world with a vast array of new products. A major reason for this is that artificial neural networks are able to provide solutions to a wide variety of complex problems in a much simpler fashion than is possible using existing techniques. In recognition of these capabilities, many scientists and engineers are exploring the potential application of this new technology to their fields of study. An artificial neural network (ANN) can be a software simulation, an electronic circuit, optical system, or even an electro-chemical system designed to emulate some of the brain`s rudimentary structure as well as some of the learning processes that are believed to take place in the brain. For a very wide range of applications in science, engineering, and information technology, ANNs offer a complementary and potentially superior approach to that provided by conventional computing and conventional artificial intelligence. This is because, unlike conventional computers, which have to be programmed, ANNs essentially learn from experience and can be trained in a straightforward fashion to carry out tasks ranging from the simple to the highly complex.

  13. Community and Social Network Sites as Technology Enhanced Learning Environments

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Christiansen, Ellen

    2008-01-01

    This paper examines the affordance of the Danish social networking site Mingler.dk for peer-to-peer learning and development. With inspiration from different theoretical frameworks, the authors argue how learning and development in such social online systems can be conceptualised and analysed....... Theoretically the paper defines development in accordance with Vygotsky's concept of the zone of proximal development, and learning in accordance with Wenger's concept of communities of practice. The authors suggest analysing the learning and development taking place on Mingler.dk by using these concepts...... supplemented by the notion of horizontal learning adopted from Engestrm and Wenger. Their analysis shows how horizontal learning happens by crossing boundaries between several sites of engagement, and how the actors' multiple membership enables the community members to draw on a vast amount of resources from...

  14. Analysis of the communities of an urban mobile phone network.

    Science.gov (United States)

    Botta, Federico; Del Genio, Charo I

    2017-01-01

    Being able to characterise the patterns of communications between individuals across different time scales is of great importance in understanding people's social interactions. Here, we present a detailed analysis of the community structure of the network of mobile phone calls in the metropolitan area of Milan revealing temporal patterns of communications between people. We show that circadian and weekly patterns can be found in the evolution of communities, presenting evidence that these cycles arise not only at the individual level but also at that of social groups. Our findings suggest that these trends are present across a range of time scales, from hours to days and weeks, and can be used to detect socially relevant events.

  15. Error and attack tolerance of synchronization in Hindmarsh–Rose neural networks with community structure

    International Nuclear Information System (INIS)

    Li, Chun-Hsien; Yang, Suh-Yuh

    2014-01-01

    Synchronization is one of the most important features observed in large-scale complex networks of interacting dynamical systems. As is well known, there is a close relation between the network topology and the network synchronizability. Using the coupled Hindmarsh–Rose neurons with community structure as a model network, in this paper we explore how failures of the nodes due to random errors or intentional attacks affect the synchronizability of community networks. The intentional attacks are realized by removing a fraction of the nodes with high values in some centrality measure such as the centralities of degree, eigenvector, betweenness and closeness. According to the master stability function method, we employ the algebraic connectivity of the considered community network as an indicator to examine the network synchronizability. Numerical evidences show that the node failure strategy based on the betweenness centrality has the most influence on the synchronizability of community networks. With this node failure strategy for a given network with a fixed number of communities, we find that the larger the degree of communities, the worse the network synchronizability; however, for a given network with a fixed degree of communities, we observe that the more the number of communities, the better the network synchronizability.

  16. Gender perspective on the social networks of household heads and community leaders after involuntary resettlement

    NARCIS (Netherlands)

    Quetulio-Navarra, Melissa; Znidarsic, Anja; Niehof, Anke

    2017-01-01

    The study of social network analysis in Indonesia and the Philippines reveals that after a certain period in a new community and living among involuntarily resettled strangers, household heads and community leaders will eventually replace their disrupted previous networks with new network ties. The

  17. Road networks predict human influence on Amazonian bird communities

    Science.gov (United States)

    Ahmed, Sadia E.; Lees, Alexander C.; Moura, Nárgila G.; Gardner, Toby A.; Barlow, Jos; Ferreira, Joice; Ewers, Robert M.

    2014-01-01

    Road building can lead to significant deleterious impacts on biodiversity, varying from direct road-kill mortality and direct habitat loss associated with road construction, to more subtle indirect impacts from edge effects and fragmentation. However, little work has been done to evaluate the specific effects of road networks and biodiversity loss beyond the more generalized effects of habitat loss. Here, we compared forest bird species richness and composition in the municipalities of Santarém and Belterra in Pará state, eastern Brazilian Amazon, with a road network metric called ‘roadless volume (RV)’ at the scale of small hydrological catchments (averaging 3721 ha). We found a significant positive relationship between RV and both forest bird richness and the average number of unique species (species represented by a single record) recorded at each site. Forest bird community composition was also significantly affected by RV. Moreover, there was no significant correlation between RV and forest cover, suggesting that road networks may impact biodiversity independently of changes in forest cover. However, variance partitioning analysis indicated that RV has partially independent and therefore additive effects, suggesting that RV and forest cover are best used in a complementary manner to investigate changes in biodiversity. Road impacts on avian species richness and composition independent of habitat loss may result from road-dependent habitat disturbance and fragmentation effects that are not captured by total percentage habitat cover, such as selective logging, fire, hunting, traffic disturbance, edge effects and road-induced fragmentation. PMID:25274363

  18. Topic-oriented community detection of rating-based social networks

    Directory of Open Access Journals (Sweden)

    Ali Reihanian

    2016-07-01

    Full Text Available Nowadays, real world social networks contain a vast range of information including shared objects, comments, following information, etc. Finding meaningful communities in this kind of networks is an interesting research area and has attracted the attention of many researchers. The community structure of complex networks reveals both their organization and hidden relations among their constituents. Most of the researches in the field of community detection mainly focus on the topological structure of the network without performing any content analysis. In recent years, a number of researches have proposed approaches which consider both the contents that are interchanged in networks, and the topological structures of the networks in order to find more meaningful communities. In this research, the effect of topic analysis in finding more meaningful communities in social networking sites in which the users express their feelings toward different objects (like movies by means of rating is demonstrated by performing extensive experiments.

  19. Identifying and characterizing key nodes among communities based on electrical-circuit networks.

    Science.gov (United States)

    Zhu, Fenghui; Wang, Wenxu; Di, Zengru; Fan, Ying

    2014-01-01

    Complex networks with community structures are ubiquitous in the real world. Despite many approaches developed for detecting communities, we continue to lack tools for identifying overlapping and bridging nodes that play crucial roles in the interactions and communications among communities in complex networks. Here we develop an algorithm based on the local flow conservation to effectively and efficiently identify and distinguish the two types of nodes. Our method is applicable in both undirected and directed networks without a priori knowledge of the community structure. Our method bypasses the extremely challenging problem of partitioning communities in the presence of overlapping nodes that may belong to multiple communities. Due to the fact that overlapping and bridging nodes are of paramount importance in maintaining the function of many social and biological networks, our tools open new avenues towards understanding and controlling real complex networks with communities accompanied with the key nodes.

  20. Network communities as a new form of social organization in conditions of postmodern

    Directory of Open Access Journals (Sweden)

    N. V. Burmaha

    2016-03-01

    Full Text Available This article deals with the approach to interpretation of essence of the network community concept in which we propose to consider it as a new form of social organization that is substantiated by the specificity of how our society is functioning in conditions of Postmodern. There were explored two main approaches to network communities studying: the first approach considers social networks in a classic, traditional interpretation of modernity as a special kind of social structure, and the second one represents social networks as a specific virtual formation, a social structure of virtual Internet reality. There were revealed some common features of a social organization and a network community: presence of permanent communication between members of the group, united by certain common interests and goals, as well as presence of the certain hierarchy among all members of the community, and the rules of conduct, implementation of communication. Distinctive features: network community is more informal, offers its members considerable leeway in the implementation of their own goals and satisfying the needs, full virtualization of communication absence of direct interaction during communication, under conditions where the main resource for the interchange in network communities is information. It was shown that in the process of emergence, development and distribution of network communities, the fundamental role is played by modern communications - namely, unification them in a stable set of interconnected networks and, in particular network communities.

  1. Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

    Science.gov (United States)

    Song, Kaida; Wang, Rui; Liu, Yi; Qian, Depei; Zhang, Han; Cai, Jihong

    2015-01-01

    Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

  2. Game Theoretical Analysis on Cooperation Stability and Incentive Effectiveness in Community Networks.

    Directory of Open Access Journals (Sweden)

    Kaida Song

    Full Text Available Community networks, the distinguishing feature of which is membership admittance, appear on P2P networks, social networks, and conventional Web networks. Joining the network costs money, time or network bandwidth, but the individuals get access to special resources owned by the community in return. The prosperity and stability of the community are determined by both the policy of admittance and the attraction of the privileges gained by joining. However, some misbehaving users can get the dedicated resources with some illicit and low-cost approaches, which introduce instability into the community, a phenomenon that will destroy the membership policy. In this paper, we analyze on the stability using game theory on such a phenomenon. We propose a game-theoretical model of stability analysis in community networks and provide conditions for a stable community. We then extend the model to analyze the effectiveness of different incentive policies, which could be used when the community cannot maintain its members in certain situations. Then we verify those models through a simulation. Finally, we discuss several ways to promote community network's stability by adjusting the network's properties and give some proposal on the designs of these types of networks from the points of game theory and stability.

  3. Extracting weights from edge directions to find communities in directed networks

    International Nuclear Information System (INIS)

    Lai, Darong; Lu, Hongtao; Nardini, Christine

    2010-01-01

    Community structures are found to exist ubiquitously in real-world complex networks. We address here the problem of community detection in directed networks. Most of the previous literature ignores edge directions and applies methods designed for community detection in undirected networks, which discards valuable information and often fails when different communities are defined on the basis of incoming and outgoing edges. We suggest extracting information about edge directions using a PageRank random walk and translating such information into edge weights. After extraction we obtain a new weighted directed network in which edge directions can then be safely ignored. We thus transform community detection in directed networks into community detection in reweighted undirected networks. Such an approach can benefit directly from the large volume of algorithms for the detection of communities in undirected networks already developed, since it is not obvious how to extend these algorithms to account for directed networks and the procedure is often difficult. Validations on synthetic and real-world networks demonstrate that the proposed framework can effectively detect communities in directed networks

  4. Rural Embedded Assistants for Community Health (REACH) network: first-person accounts in a community-university partnership.

    Science.gov (United States)

    Brown, Louis D; Alter, Theodore R; Brown, Leigh Gordon; Corbin, Marilyn A; Flaherty-Craig, Claire; McPhail, Lindsay G; Nevel, Pauline; Shoop, Kimbra; Sterner, Glenn; Terndrup, Thomas E; Weaver, M Ellen

    2013-03-01

    Community research and action projects undertaken by community-university partnerships can lead to contextually appropriate and sustainable community improvements in rural and urban localities. However, effective implementation is challenging and prone to failure when poorly executed. The current paper seeks to inform rural community-university partnership practice through consideration of first-person accounts from five stakeholders in the Rural Embedded Assistants for Community Health (REACH) Network. The REACH Network is a unique community-university partnership aimed at improving rural health services by identifying, implementing, and evaluating innovative health interventions delivered by local caregivers. The first-person accounts provide an insider's perspective on the nature of collaboration. The unique perspectives identify three critical challenges facing the REACH Network: trust, coordination, and sustainability. Through consideration of the challenges, we identified several strategies for success. We hope readers can learn their own lessons when considering the details of our partnership's efforts to improve the delivery infrastructure for rural healthcare.

  5. Uncovering the community structure associated with the diffusion dynamics on networks

    International Nuclear Information System (INIS)

    Cheng, Xue-Qi; Shen, Hua-Wei

    2010-01-01

    As two main focuses of the study of complex networks, the community structure and the dynamics on networks have both attracted much attention in various scientific fields. However, it is still an open question how the community structure is associated with the dynamics on complex networks. In this paper, through investigating the diffusion process taking place on networks, we demonstrate that the intrinsic community structure of networks can be revealed by the stable local equilibrium states of the diffusion process. Furthermore, we show that such community structure can be directly identified through the optimization of the conductance of the network, which measures how easily the diffusion among different communities occurs. Tests on benchmark networks indicate that the conductance optimization method significantly outperforms the modularity optimization methods in identifying the community structure of networks. Applications to real world networks also demonstrate the effectiveness of the conductance optimization method. This work provides insights into the multiple topological scales of complex networks, and the community structure obtained can naturally reflect the diffusion capability of the underlying network

  6. Followers are not enough: a multifaceted approach to community detection in online social networks.

    Science.gov (United States)

    Darmon, David; Omodei, Elisa; Garland, Joshua

    2015-01-01

    In online social media networks, individuals often have hundreds or even thousands of connections, which link these users not only to friends, associates, and colleagues, but also to news outlets, celebrities, and organizations. In these complex social networks, a 'community' as studied in the social network literature, can have very different meaning depending on the property of the network under study. Taking into account the multifaceted nature of these networks, we claim that community detection in online social networks should also be multifaceted in order to capture all of the different and valuable viewpoints of 'community.' In this paper we focus on three types of communities beyond follower-based structural communities: activity-based, topic-based, and interaction-based. We analyze a Twitter dataset using three different weightings of the structural network meant to highlight these three community types, and then infer the communities associated with these weightings. We show that interesting insights can be obtained about the complex community structure present in social networks by studying when and how these four community types give rise to similar as well as completely distinct community structure.

  7. American Indian Women and Screening Mammography: Findings from a Qualitative Study in Oklahoma

    Science.gov (United States)

    Tolma, Eleni; Batterton, Chasity; Hamm, Robert M.; Thompson, David; Engelman, Kimberly K.

    2012-01-01

    Background: Breast cancer is an important public health issue within the American Indian (AI) community in Oklahoma; however, there is limited information to explain the low screening mammography rates among AI women. Purpose: To identify the motivational factors affecting an AI woman's decision to obtain a mammogram. Methods: Through the use of…

  8. At the edge? HIV stigma and centrality in a community's social network in Namibia.

    Science.gov (United States)

    Smith, Rachel A; Baker, Michelle

    2012-04-01

    Social network analysis was used to examine the relationship between HIV/AIDS stigmatization, perceived risk, and centrality in the community network (via participation in community groups). The findings from respondents in Keetmanshoop, Namibia (N = 375) showed an interaction between stigma and risk perceptions\\hose who perceived higher HIV risk and stronger HIV stigma participated in fewer community groups and participated in groups with members who participated less widely across the network. In contrast, those who perceived higher HIV risk and weaker HIV stigma participated more, and were in community groups that are located on a greater share of the paths between entities in the network. Taboo, secrecy, resistance, knowing a person living with HIV/AIDS, and desire for diagnosis secrecy were also related to centrality. Findings suggest that the interaction of perceived HIV risk and HIV stigma are related to structural-level features of community networks based on participation in community groups.

  9. Road networks predict human influence on Amazonian bird communities.

    Science.gov (United States)

    Ahmed, Sadia E; Lees, Alexander C; Moura, Nárgila G; Gardner, Toby A; Barlow, Jos; Ferreira, Joice; Ewers, Robert M

    2014-11-22

    Road building can lead to significant deleterious impacts on biodiversity, varying from direct road-kill mortality and direct habitat loss associated with road construction, to more subtle indirect impacts from edge effects and fragmentation. However, little work has been done to evaluate the specific effects of road networks and biodiversity loss beyond the more generalized effects of habitat loss. Here, we compared forest bird species richness and composition in the municipalities of Santarém and Belterra in Pará state, eastern Brazilian Amazon, with a road network metric called 'roadless volume (RV)' at the scale of small hydrological catchments (averaging 3721 ha). We found a significant positive relationship between RV and both forest bird richness and the average number of unique species (species represented by a single record) recorded at each site. Forest bird community composition was also significantly affected by RV. Moreover, there was no significant correlation between RV and forest cover, suggesting that road networks may impact biodiversity independently of changes in forest cover. However, variance partitioning analysis indicated that RV has partially independent and therefore additive effects, suggesting that RV and forest cover are best used in a complementary manner to investigate changes in biodiversity. Road impacts on avian species richness and composition independent of habitat loss may result from road-dependent habitat disturbance and fragmentation effects that are not captured by total percentage habitat cover, such as selective logging, fire, hunting, traffic disturbance, edge effects and road-induced fragmentation. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

  10. Building a Network of Internships for a Diverse Geoscience Community

    Science.gov (United States)

    Sloan, V.; Haacker-Santos, R.; Pandya, R.

    2011-12-01

    Individual undergraduate internship programs, however effective, are not sufficient to address the lack of diversity in the geoscience workforce. Rather than competing with each other for a small pool of students from historically under-represented groups, REU and internship programs might share recruiting efforts and application processes. For example, in 2011, the RESESS program at UNAVCO and the SOARS program at UCAR shared recruiting websites and advertising. This contributed to a substantial increase in the number of applicants to the RESESS program, the majority of which were from historically under-represented groups. RESESS and SOARS shared qualified applications with other REU/internship programs and helped several additional minority students secure summer internships. RESESS and SOARS also leveraged their geographic proximity to pool resources for community building activities, a two-day science field trip, a weekly writing workshop, and our final poster session. This provided our interns with an expanded network of peers and gave our staff opportunities to work together on planning. Recently we have reached out to include other programs and agencies in activities for our interns, such as mentoring high-school students, leading outreach to elementary school students, and exposing our interns to geoscience careers options and graduate schools. Informal feedback from students suggests that they value these interactions and appreciate learning with interns from partner programs. Through this work, we are building a network of program managers who support one another professionally and share effective strategies. We would like to expand that network, and future plans include a workshop with university partners and an expanded list of REU programs to explore further collaborations.

  11. The Community Seismic Network: Enabling Observations Through Citizen Science Participation

    Science.gov (United States)

    Kohler, M. D.; Clayton, R. W.; Heaton, T. H.; Bunn, J.; Guy, R.; Massari, A.; Chandy, K. M.

    2017-12-01

    The Community Seismic Network is a dense accelerometer array deployed in the greater Los Angeles area and represents the future of densely instrumented urban cities where localized vibration measurements are collected continuously throughout the free-field and built environment. The hardware takes advantage of developments in the semiconductor industry in the form of inexpensive MEMS accelerometers that are each coupled with a single board computer. The data processing and archival architecture borrows from developments in cloud computing and network connectedness. The ability to deploy densely in the free field and in upper stories of mid/high-rise buildings is enabled by community hosts for sensor locations. To this end, CSN has partnered with the Los Angeles Unified School District (LAUSD), the NASA-Jet Propulsion Laboratory (JPL), and commercial and civic building owners to host sensors. At these sites, site amplification estimates from RMS noise measurements illustrate the lateral variation in amplification over length scales of 100 m or less, that correlate with gradients in the local geology such as sedimentary basins that abut crystalline rock foothills. This is complemented by high-resolution, shallow seismic velocity models obtained using an H/V method. In addition, noise statistics are used to determine the reliability of sites for ShakeMap and earthquake early warning data. The LAUSD and JPL deployments are examples of how situational awareness and centralized warning products such as ShakeMap and ShakeCast are enabled by citizen science participation. Several buildings have been instrumented with at least one triaxial accelerometer per floor, providing measurements for real-time structural health monitoring through local, customized displays. For real-time and post-event evaluation, the free-field and built environment CSN data and products illustrate the feasibility of order-of-magnitude higher spatial resolution mapping compared to what is currently

  12. Network analysis as a tool for community capacity measurement and assessing partnerships between community-based organizations in Korea.

    Science.gov (United States)

    Jung, Minsoo

    2012-01-01

    The community partnership is a foundation laid by the local community that has been historically and geographically formed to develop itself. This article, an exploratory community network survey for capacity building, assessed collaborations among community-based organizations (CBOs) in the S-district, Republic of Korea, and evaluated methods for the reconstruction of a resident-governing healthy network. Using CBOs' evaluation questionnaire, the author surveyed 83 CBOs that were collected by snowball sampling. The CBOs in the S-district had formed community networks based on vocational associations established in the 1980s and the 1990s. The entire network evidenced a cooperative partnership, in which women's organizations and civic groups carried out essential functions. In the capacity-building process through CBOs, community collaboration can be naturally cultivated, and health promotion programs to improve the residents' health will tend to be more systematic than the current approach and yield higher compliance and practice rates. Thus, it will be necessary to construct an effective partnership of community networks by reorganizing existing exclusive relations.

  13. Dynamic robustness of knowledge collaboration network of open source product development community

    Science.gov (United States)

    Zhou, Hong-Li; Zhang, Xiao-Dong

    2018-01-01

    As an emergent innovative design style, open source product development communities are characterized by a self-organizing, mass collaborative, networked structure. The robustness of the community is critical to its performance. Using the complex network modeling method, the knowledge collaboration network of the community is formulated, and the robustness of the network is systematically and dynamically studied. The characteristics of the network along the development period determine that its robustness should be studied from three time stages: the start-up, development and mature stages of the network. Five kinds of user-loss pattern are designed, to assess the network's robustness under different situations in each of these three time stages. Two indexes - the largest connected component and the network efficiency - are used to evaluate the robustness of the community. The proposed approach is applied in an existing open source car design community. The results indicate that the knowledge collaboration networks show different levels of robustness in different stages and different user loss patterns. Such analysis can be applied to provide protection strategies for the key users involved in knowledge dissemination and knowledge contribution at different stages of the network, thereby promoting the sustainable and stable development of the open source community.

  14. The overlapping community structure of structural brain network in young healthy individuals.

    Directory of Open Access Journals (Sweden)

    Kai Wu

    2011-05-01

    Full Text Available Community structure is a universal and significant feature of many complex networks in biology, society, and economics. Community structure has also been revealed in human brain structural and functional networks in previous studies. However, communities overlap and share many edges and nodes. Uncovering the overlapping community structure of complex networks remains largely unknown in human brain networks. Here, using regional gray matter volume, we investigated the structural brain network among 90 brain regions (according to a predefined anatomical atlas in 462 young, healthy individuals. Overlapped nodes between communities were defined by assuming that nodes (brain regions can belong to more than one community. We demonstrated that 90 brain regions were organized into 5 overlapping communities associated with several well-known brain systems, such as the auditory/language, visuospatial, emotion, decision-making, social, control of action, memory/learning, and visual systems. The overlapped nodes were mostly involved in an inferior-posterior pattern and were primarily related to auditory and visual perception. The overlapped nodes were mainly attributed to brain regions with higher node degrees and nodal efficiency and played a pivotal role in the flow of information through the structural brain network. Our results revealed fuzzy boundaries between communities by identifying overlapped nodes and provided new insights into the understanding of the relationship between the structure and function of the human brain. This study provides the first report of the overlapping community structure of the structural network of the human brain.

  15. Multiplex social ecological network analysis reveals how social changes affect community robustness more than resource depletion.

    Science.gov (United States)

    Baggio, Jacopo A; BurnSilver, Shauna B; Arenas, Alex; Magdanz, James S; Kofinas, Gary P; De Domenico, Manlio

    2016-11-29

    Network analysis provides a powerful tool to analyze complex influences of social and ecological structures on community and household dynamics. Most network studies of social-ecological systems use simple, undirected, unweighted networks. We analyze multiplex, directed, and weighted networks of subsistence food flows collected in three small indigenous communities in Arctic Alaska potentially facing substantial economic and ecological changes. Our analysis of plausible future scenarios suggests that changes to social relations and key households have greater effects on community robustness than changes to specific wild food resources.

  16. A density-based clustering model for community detection in complex networks

    Science.gov (United States)

    Zhao, Xiang; Li, Yantao; Qu, Zehui

    2018-04-01

    Network clustering (or graph partitioning) is an important technique for uncovering the underlying community structures in complex networks, which has been widely applied in various fields including astronomy, bioinformatics, sociology, and bibliometric. In this paper, we propose a density-based clustering model for community detection in complex networks (DCCN). The key idea is to find group centers with a higher density than their neighbors and a relatively large integrated-distance from nodes with higher density. The experimental results indicate that our approach is efficient and effective for community detection of complex networks.

  17. Characteristics of successful aviation leaders of Oklahoma

    Science.gov (United States)

    Kutz, Mary N. Hill

    Scope and method of study. The purpose of the study was to examine the personal traits, skills, practices, behaviors, background, academic, and career success patterns of selected aviation leaders in Oklahoma. A purposive sample of 18 leaders who had achieved a top-ranked position of aviation leadership in an organization or a position of influence in the community was selected for interview. The leaders chosen for interview came from a variety of aviation organizations including government, academia, military, corporate aviation, and air carrier leadership as well as community leadership (specifically those aviation personnel who were engaged in a political or civic leadership role). Findings and conclusions. This study identified no common career choices, educational, family, or other background factors exclusively responsible for leadership success of all of the participants. Some of the more significant findings were that a high percentage of the leaders held undergraduate and advanced degrees; however, success had been achieved by some who had little or no college education. Aviation technical experience was not a prerequisite for aviation leadership success in that a significant number of the participants held no airman rating and some had entered positions of aviation leadership from non-aviation related careers. All had received some positive learning experience from their family background even those backgrounds which were less than desirable. All of the participants had been involved in volunteer civic or humanitarian leadership roles, and all had received numerous honors. The most frequently identified value expressed by the leaders was honesty; the predominant management style was participative with a strong backup style for directing, the most important skills were communication and listening skills, and the most frequently mentioned characteristics of success were honesty, credibility, vision, high standards, love for aviation and fiscal

  18. Liking and hyperlinking: Community detection in online child sexual exploitation networks.

    Science.gov (United States)

    Westlake, Bryce G; Bouchard, Martin

    2016-09-01

    The online sexual exploitation of children is facilitated by websites that form virtual communities, via hyperlinks, to distribute images, videos, and other material. However, how these communities form, are structured, and evolve over time is unknown. Collected using a custom-designed webcrawler, we begin from known child sexual exploitation (CE) seed websites and follow hyperlinks to connected, related, websites. Using a repeated measure design we analyze 10 networks of 300 + websites each - over 4.8 million unique webpages in total, over a period of 60 weeks. Community detection techniques reveal that CE-related networks were dominated by two large communities hosting varied material -not necessarily matching the seed website. Community stability, over 60 weeks, varied across networks. Reciprocity in hyperlinking between community members was substantially higher than within the full network, however, websites were not more likely to connect to homogeneous-content websites. Copyright © 2016 Elsevier Inc. All rights reserved.

  19. Game theory and extremal optimization for community detection in complex dynamic networks.

    Science.gov (United States)

    Lung, Rodica Ioana; Chira, Camelia; Andreica, Anca

    2014-01-01

    The detection of evolving communities in dynamic complex networks is a challenging problem that recently received attention from the research community. Dynamics clearly add another complexity dimension to the difficult task of community detection. Methods should be able to detect changes in the network structure and produce a set of community structures corresponding to different timestamps and reflecting the evolution in time of network data. We propose a novel approach based on game theory elements and extremal optimization to address dynamic communities detection. Thus, the problem is formulated as a mathematical game in which nodes take the role of players that seek to choose a community that maximizes their profit viewed as a fitness function. Numerical results obtained for both synthetic and real-world networks illustrate the competitive performance of this game theoretical approach.

  20. Formation of community-based hypertension practice networks: success, obstacles, and lessons learned.

    Science.gov (United States)

    Dart, Richard A; Egan, Brent M

    2014-06-01

    Community-based practice networks for research and improving the quality of care are growing in size and number but have variable success rates. In this paper, the authors review recent efforts to initiate a community-based hypertension network modeled after the successful Outpatient Quality Improvement Network (O'QUIN) project, located at the Medical University of South Carolina. Key lessons learned and new directions to be explored are highlighted. ©2014 Wiley Periodicals, Inc.

  1. Stability of Ecological Communities and the Architecture of Mutualistic and Trophic Networks

    NARCIS (Netherlands)

    Thebault, E.M.C.; Fontaine, C.

    2010-01-01

    Research on the relationship between the architecture of ecological networks and community stability has mainly focused on one type of interaction at a time, making difficult any comparison between different network types. We used a theoretical approach to show that the network architecture favoring

  2. A Social Network Analysis of Teaching and Research Collaboration in a Teachers' Virtual Learning Community

    Science.gov (United States)

    Lin, Xiaofan; Hu, Xiaoyong; Hu, Qintai; Liu, Zhichun

    2016-01-01

    Analysing the structure of a social network can help us understand the key factors influencing interaction and collaboration in a virtual learning community (VLC). Here, we describe the mechanisms used in social network analysis (SNA) to analyse the social network structure of a VLC for teachers and discuss the relationship between face-to-face…

  3. Place-based attributes predict community membership in a mobile phone communication network.

    Directory of Open Access Journals (Sweden)

    T Trevor Caughlin

    Full Text Available Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97 between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

  4. A cooperative game framework for detecting overlapping communities in social networks

    Science.gov (United States)

    Jonnalagadda, Annapurna; Kuppusamy, Lakshmanan

    2018-02-01

    Community detection in social networks is a challenging and complex task, which received much attention from researchers of multiple domains in recent years. The evolution of communities in social networks happens merely due to the self-interest of the nodes. The interesting feature of community structure in social networks is the multi membership of the nodes resulting in overlapping communities. Assuming the nodes of the social network as self-interested players, the dynamics of community formation can be captured in the form of a game. In this paper, we propose a greedy algorithm, namely, Weighted Graph Community Game (WGCG), in order to model the interactions among the self-interested nodes of the social network. The proposed algorithm employs the Shapley value mechanism to discover the inherent communities of the underlying social network. The experimental evaluation on the real-world and synthetic benchmark networks demonstrates that the performance of the proposed algorithm is superior to the state-of-the-art overlapping community detection algorithms.

  5. Place-based attributes predict community membership in a mobile phone communication network.

    Science.gov (United States)

    Caughlin, T Trevor; Ruktanonchai, Nick; Acevedo, Miguel A; Lopiano, Kenneth K; Prosper, Olivia; Eagle, Nathan; Tatem, Andrew J

    2013-01-01

    Social networks can be organized into communities of closely connected nodes, a property known as modularity. Because diseases, information, and behaviors spread faster within communities than between communities, understanding modularity has broad implications for public policy, epidemiology and the social sciences. Explanations for community formation in social networks often incorporate the attributes of individual people, such as gender, ethnicity or shared activities. High modularity is also a property of large-scale social networks, where each node represents a population of individuals at a location, such as call flow between mobile phone towers. However, whether or not place-based attributes, including land cover and economic activity, can predict community membership for network nodes in large-scale networks remains unknown. We describe the pattern of modularity in a mobile phone communication network in the Dominican Republic, and use a linear discriminant analysis (LDA) to determine whether geographic context can explain community membership. Our results demonstrate that place-based attributes, including sugar cane production, urbanization, distance to the nearest airport, and wealth, correctly predicted community membership for over 70% of mobile phone towers. We observed a strongly positive correlation (r = 0.97) between the modularity score and the predictive ability of the LDA, suggesting that place-based attributes can accurately represent the processes driving modularity. In the absence of social network data, the methods we present can be used to predict community membership over large scales using solely place-based attributes.

  6. Choosing your network: social preferences in an online health community.

    Science.gov (United States)

    Centola, Damon; van de Rijt, Arnout

    2015-01-01

    A growing number of online health communities offer individuals the opportunity to receive information, advice, and support from peers. Recent studies have demonstrated that these new online contacts can be important informational resources, and can even exert significant influence on individuals' behavior in various contexts. However little is known about how people select their health contacts in these virtual domains. This is because selection preferences in peer networks are notoriously difficult to detect. In existing networks, unobserved pressures on tie formation--such as common organizational memberships, introductions to friends of friends, or limitations on accessibility--may mistakenly be interpreted as individual preferences for interacting/not interacting with others. We address these issues by adopting a social media approach to studying network formation. We study social selection using an in vivo study within an online exercise program, in which anonymous participants have equal opportunities for initiating relationships with other program members. This design allows us to identify individuals' preferences for health contacts, and to evaluate what these preferences imply for members' access to new kinds of health information, and for the kinds of social influences to which they are exposed. The study was conducted within a goal-oriented fitness competition, in which participation was greatest among a small core of active individuals. Our results show that the active participants displayed indifference to the fitness and exercise profiles of others, disregarding information about others' fitness levels, exercise preferences, and workout experiences, instead selecting partners almost entirely on the basis of similarities on gender, age, and BMI. Interestingly, the findings suggest that rather than expanding and diversifying their sources of health information, participants' choices limited the value of their online resources by selecting contacts

  7. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

    KAUST Repository

    Cannistraci, C.V.; Alanis-Lobato, G.; Ravasi, Timothy

    2013-01-01

    for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems

  8. Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks

    Directory of Open Access Journals (Sweden)

    Dane Taylor

    2017-09-01

    Full Text Available Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős–Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection and which occur for a given community provided its size surpasses a detectability limit K^{*}. When layers are aggregated via a summation, we obtain K^{*}∝O(sqrt[NL]/T, where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L, then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than O(L^{-1/2}. Moreover, we find that thresholding the summation can, in some cases, cause K^{*} to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

  9. Super-Resolution Community Detection for Layer-Aggregated Multilayer Networks.

    Science.gov (United States)

    Taylor, Dane; Caceres, Rajmonda S; Mucha, Peter J

    2017-01-01

    Applied network science often involves preprocessing network data before applying a network-analysis method, and there is typically a theoretical disconnect between these steps. For example, it is common to aggregate time-varying network data into windows prior to analysis, and the trade-offs of this preprocessing are not well understood. Focusing on the problem of detecting small communities in multilayer networks, we study the effects of layer aggregation by developing random-matrix theory for modularity matrices associated with layer-aggregated networks with N nodes and L layers, which are drawn from an ensemble of Erdős-Rényi networks with communities planted in subsets of layers. We study phase transitions in which eigenvectors localize onto communities (allowing their detection) and which occur for a given community provided its size surpasses a detectability limit K * . When layers are aggregated via a summation, we obtain [Formula: see text], where T is the number of layers across which the community persists. Interestingly, if T is allowed to vary with L , then summation-based layer aggregation enhances small-community detection even if the community persists across a vanishing fraction of layers, provided that T/L decays more slowly than ( L -1/2 ). Moreover, we find that thresholding the summation can, in some cases, cause K * to decay exponentially, decreasing by orders of magnitude in a phenomenon we call super-resolution community detection. In other words, layer aggregation with thresholding is a nonlinear data filter enabling detection of communities that are otherwise too small to detect. Importantly, different thresholds generally enhance the detectability of communities having different properties, illustrating that community detection can be obscured if one analyzes network data using a single threshold.

  10. Community landscapes: an integrative approach to determine overlapping network module hierarchy, identify key nodes and predict network dynamics.

    Directory of Open Access Journals (Sweden)

    István A Kovács

    Full Text Available BACKGROUND: Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. METHODOLOGY/PRINCIPAL FINDINGS: Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1 determine persvasively overlapping modules with high resolution; (2 uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3 allow the determination of key network nodes and (4 help to predict network dynamics. CONCLUSIONS/SIGNIFICANCE: The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction.

  11. Utilization of an interorganizational network analysis to evaluate the development of community capacity among a community-academic partnership.

    Science.gov (United States)

    Clark, Heather R; Ramirez, Albert; Drake, Kelly N; Beaudoin, Christopher E; Garney, Whitney R; Wendel, Monica L; Outley, Corliss; Burdine, James N; Player, Harold D

    2014-01-01

    Following a community health assessment the Brazos Valley Health Partnership (BVHP) organized to address fragmentation of services and local health needs. This regional partnership employs the fundamental principles of community-based participatory research, fostering an equitable partnership with the aim of building community capacity to address local health issues. This article describes changes in relationships as a result of capacity building efforts in a community-academic partnership. Growth in network structure among organizations is hypothesized to be indicative of less fragmentation of services for residents and increased capacity of the BVHP to collectively address local health issues. Each of the participant organizations responded to a series of questions regarding its relationships with other organizations. Each organization was asked about information sharing, joint planning, resource sharing, and formal agreements with other organizations. The network survey has been administered 3 times between 2004 and 2009. Network density increased for sharing information and jointly planning events. Growth in the complexity of relationships was reported for sharing tangible resources and formal agreements. The average number of ties between organizations as well as the strength of relationships increased. This study provides evidence that the community capacity building efforts within these communities have contributed to beneficial changes in interorganizational relationships. Results from this analysis are useful for understanding how a community partnership's efforts to address access to care can strengthen a community's capacity for future action. Increased collaboration also leads to new assets, resources, and the transfer of knowledge and skills.

  12. Oklahoma Study of Educator Supply and Demand: Trends and Projections

    Science.gov (United States)

    Berg-Jacobson, Alex; Levin, Jesse

    2015-01-01

    In June 2014, the Oklahoma State Regents of Higher Education (OSRHE) commissioned American Institutes for Research (AIR) to conduct a study to better understand both historical and future predicted trends of educator supply and demand across Oklahoma. OSRHE commissioned the study in partnership with the Oklahoma Commission for Teacher Preparation…

  13. An Improved Topology-Potential-Based Community Detection Algorithm for Complex Network

    Directory of Open Access Journals (Sweden)

    Zhixiao Wang

    2014-01-01

    Full Text Available Topology potential theory is a new community detection theory on complex network, which divides a network into communities by spreading outward from each local maximum potential node. At present, almost all topology-potential-based community detection methods ignore node difference and assume that all nodes have the same mass. This hypothesis leads to inaccuracy of topology potential calculation and then decreases the precision of community detection. Inspired by the idea of PageRank algorithm, this paper puts forward a novel mass calculation method for complex network nodes. A node’s mass obtained by our method can effectively reflect its importance and influence in complex network. The more important the node is, the bigger its mass is. Simulation experiment results showed that, after taking node mass into consideration, the topology potential of node is more accurate, the distribution of topology potential is more reasonable, and the results of community detection are more precise.

  14. Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design

    Science.gov (United States)

    Ang, Chee Siang; Zaphiris, Panayiotis

    We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.

  15. A Markov random walk under constraint for discovering overlapping communities in complex networks

    International Nuclear Information System (INIS)

    Jin, Di; Yang, Bo; Liu, Dayou; He, Dongxiao; Liu, Jie; Baquero, Carlos

    2011-01-01

    The detection of overlapping communities in complex networks has motivated recent research in relevant fields. Aiming to address this problem, we propose a Markov-dynamics-based algorithm, called UEOC, which means 'unfold and extract overlapping communities'. In UEOC, when identifying each natural community that overlaps, a Markov random walk method combined with a constraint strategy, which is based on the corresponding annealed network (degree conserving random network), is performed to unfold the community. Then, a cutoff criterion with the aid of a local community function, called conductance, which can be thought of as the ratio between the number of edges inside the community and those leaving it, is presented to extract this emerged community from the entire network. The UEOC algorithm depends on only one parameter whose value can be easily set, and it requires no prior knowledge of the hidden community structures. The proposed UEOC has been evaluated both on synthetic benchmarks and on some real-world networks, and has been compared with a set of competing algorithms. The experimental result has shown that UEOC is highly effective and efficient for discovering overlapping communities

  16. Complex brain networks: From topological communities to clustered ...

    Indian Academy of Sciences (India)

    functional connectivity of the human brain has shown that both types of brain networks share .... the areas and also of the whole network, the Pearson correlation coefficient r and ..... Several areas important for intercommunity communication.

  17. The network structure of human personality according to the NEO-PI-R: matching network community structure to factor structure.

    Directory of Open Access Journals (Sweden)

    Rutger Goekoop

    Full Text Available INTRODUCTION: Human personality is described preferentially in terms of factors (dimensions found using factor analysis. An alternative and highly related method is network analysis, which may have several advantages over factor analytic methods. AIM: To directly compare the ability of network community detection (NCD and principal component factor analysis (PCA to examine modularity in multidimensional datasets such as the neuroticism-extraversion-openness personality inventory revised (NEO-PI-R. METHODS: 434 healthy subjects were tested on the NEO-PI-R. PCA was performed to extract factor structures (FS of the current dataset using both item scores and facet scores. Correlational network graphs were constructed from univariate correlation matrices of interactions between both items and facets. These networks were pruned in a link-by-link fashion while calculating the network community structure (NCS of each resulting network using the Wakita Tsurumi clustering algorithm. NCSs were matched against FS and networks of best matches were kept for further analysis. RESULTS: At facet level, NCS showed a best match (96.2% with a 'confirmatory' 5-FS. At item level, NCS showed a best match (80% with the standard 5-FS and involved a total of 6 network clusters. Lesser matches were found with 'confirmatory' 5-FS and 'exploratory' 6-FS of the current dataset. Network analysis did not identify facets as a separate level of organization in between items and clusters. A small-world network structure was found in both item- and facet level networks. CONCLUSION: We present the first optimized network graph of personality traits according to the NEO-PI-R: a 'Personality Web'. Such a web may represent the possible routes that subjects can take during personality development. NCD outperforms PCA by producing plausible modularity at item level in non-standard datasets, and can identify the key roles of individual items and clusters in the network.

  18. Detecting Statistically Significant Communities of Triangle Motifs in Undirected Networks

    Science.gov (United States)

    2016-04-26

    Systems, Statistics & Management Science, University of Alabama, USA. 1 DISTRIBUTION A: Distribution approved for public release. Contents 1 Summary 5...13 5 Application to Real Networks 18 5.1 2012 FBS Football Schedule Network... football schedule network. . . . . . . . . . . . . . . . . . . . . . 21 14 Stem plot of degree-ordered vertices versus the degree for college football

  19. Making big communities small: using network science to understand the ecological and behavioral requirements for community social capital.

    Science.gov (United States)

    Neal, Zachary

    2015-06-01

    The concept of social capital is becoming increasingly common in community psychology and elsewhere. However, the multiple conceptual and operational definitions of social capital challenge its utility as a theoretical tool. The goals of this paper are to clarify two forms of social capital (bridging and bonding), explicitly link them to the structural characteristics of small world networks, and explore the behavioral and ecological prerequisites of its formation. First, I use the tools of network science and specifically the concept of small-world networks to clarify what patterns of social relationships are likely to facilitate social capital formation. Second, I use an agent-based model to explore how different ecological characteristics (diversity and segregation) and behavioral tendencies (homophily and proximity) impact communities' potential for developing social capital. The results suggest diverse communities have the greatest potential to develop community social capital, and that segregation moderates the effects that the behavioral tendencies of homophily and proximity have on community social capital. The discussion highlights how these findings provide community-based researchers with both a deeper understanding of the contextual constraints with which they must contend, and a useful tool for targeting their efforts in communities with the greatest need or greatest potential.

  20. An approach of community evolution based on gravitational relationship refactoring in dynamic networks

    International Nuclear Information System (INIS)

    Yin, Guisheng; Chi, Kuo; Dong, Yuxin; Dong, Hongbin

    2017-01-01

    In this paper, an approach of community evolution based on gravitational relationship refactoring between the nodes in a dynamic network is proposed, and it can be used to simulate the process of community evolution. A static community detection algorithm and a dynamic community evolution algorithm are included in the approach. At first, communities are initialized by constructing the core nodes chains, the nodes can be iteratively searched and divided into corresponding communities via the static community detection algorithm. For a dynamic network, an evolutionary process is divided into three phases, and behaviors of community evolution can be judged according to the changing situation of the core nodes chain in each community. Experiments show that the proposed approach can achieve accuracy and availability in the synthetic and real world networks. - Highlights: • The proposed approach considers both the static community detection and dynamic community evolution. • The approach of community evolution can identify the whole 6 common evolution events. • The proposed approach can judge the evolutionary events according to the variations of the core nodes chains.

  1. Value Co-creation and Co-innovation: Linking Networked Organisations and Customer Communities

    Science.gov (United States)

    Romero, David; Molina, Arturo

    Strategic networks such as Collaborative Networked Organisations (CNOs) and Virtual Customer Communities (VCCs) show a high potential as drivers of value co-creation and collaborative innovation in today’s Networking Era. Both look at the network structures as a source of jointly value creation and open innovation through access to new skills, knowledge, markets and technologies by sharing risk and integrating complementary competencies. This collaborative endeavour has proven to be able to enhance the adaptability and flexibility of CNOs and VCCs value creating systems in order to react in response to external drivers such as collaborative (business) opportunities. This paper presents a reference framework for creating interface networks, also known as ‘experience-centric networks’, as enablers for linking networked organisations and customer communities in order to support the establishment of user-driven and collaborative innovation networks.

  2. Ecological Networks and Community Attachment and Support Among Recently Resettled Refugees.

    Science.gov (United States)

    Soller, Brian; Goodkind, Jessica R; Greene, R Neil; Browning, Christopher R; Shantzek, Cece

    2018-03-25

    Interventions aimed at enhancing mental health are increasingly centered around promoting community attachment and support. However, few have examined and tested the specific ecological factors that give rise to these key community processes. Drawing from insights from the ecological network perspective, we tested whether spatial and social overlap in routine activity settings (e.g., work, school, childcare) with fellow ethnic community members is associated with individuals' attachment to their ethnic communities and access to social resources embedded in their communities. Data on routine activity locations drawn from the Refugee Well-Being Project (based in a city in the Southwestern United States) were used to reconstruct the ecological networks of recently resettled refugee communities, which were two-mode networks that comprise individuals and their routine activity locations. Results indicated that respondents' community attachment and support increased with their ecological network extensity-which taps the extent to which respondents share routine activity locations with other community members. Our study highlights a key ecological process that potentially enhances individuals' ethnic community attachment that extends beyond residential neighborhoods. © Society for Community Research and Action 2018.

  3. Eigenspaces of networks reveal the overlapping and hierarchical community structure more precisely

    International Nuclear Information System (INIS)

    Ma, Xiaoke; Gao, Lin; Yong, Xuerong

    2010-01-01

    Identifying community structure is fundamental for revealing the structure–functionality relationship in complex networks, and spectral algorithms have been shown to be powerful for this purpose. In a traditional spectral algorithm, each vertex of a network is embedded into a spectral space by making use of the eigenvectors of the adjacency matrix or Laplacian matrix of the graph. In this paper, a novel spectral approach for revealing the overlapping and hierarchical community structure of complex networks is proposed by not only using the eigenvalues and eigenvectors but also the properties of eigenspaces of the networks involved. This gives us a better characterization of community. We first show that the communicability between a pair of vertices can be rewritten in term of eigenspaces of a network. An agglomerative clustering algorithm is then presented to discover the hierarchical communities using the communicability matrix. Finally, these overlapping vertices are discovered with the corresponding eigenspaces, based on the fact that the vertices more densely connected amongst one another are more likely to be linked through short cycles. Compared with the traditional spectral algorithms, our algorithm can identify both the overlapping and hierarchical community without increasing the time complexity O(n 3 ), where n is the size of the network. Furthermore, our algorithm can also distinguish the overlapping vertices from bridges. The method is tested by applying it to some computer-generated and real-world networks. The experimental results indicate that our algorithm can reveal community structure more precisely than the traditional spectral approaches

  4. Nearest Neighbor Search in the Metric Space of a Complex Network for Community Detection

    Directory of Open Access Journals (Sweden)

    Suman Saha

    2016-03-01

    Full Text Available The objective of this article is to bridge the gap between two important research directions: (1 nearest neighbor search, which is a fundamental computational tool for large data analysis; and (2 complex network analysis, which deals with large real graphs but is generally studied via graph theoretic analysis or spectral analysis. In this article, we have studied the nearest neighbor search problem in a complex network by the development of a suitable notion of nearness. The computation of efficient nearest neighbor search among the nodes of a complex network using the metric tree and locality sensitive hashing (LSH are also studied and experimented. For evaluation of the proposed nearest neighbor search in a complex network, we applied it to a network community detection problem. Experiments are performed to verify the usefulness of nearness measures for the complex networks, the role of metric tree and LSH to compute fast and approximate node nearness and the the efficiency of community detection using nearest neighbor search. We observed that nearest neighbor between network nodes is a very efficient tool to explore better the community structure of the real networks. Several efficient approximation schemes are very useful for large networks, which hardly made any degradation of results, whereas they save lot of computational times, and nearest neighbor based community detection approach is very competitive in terms of efficiency and time.

  5. The Utrecht Pharmacy Practice network for Education and Research: a network of community and hospital pharmacies in the Netherlands.

    Science.gov (United States)

    Koster, Ellen S; Blom, Lyda; Philbert, Daphne; Rump, Willem; Bouvy, Marcel L

    2014-08-01

    Practice-based networks can serve as effective mechanisms for the development of the profession of pharmacists, on the one hand by supporting student internships and on the other hand by collection of research data and implementation of research outcomes among public health practice settings. This paper presents the characteristics and benefits of the Utrecht Pharmacy Practice network for Education and Research, a practice based research network affiliated with the Department of Pharmaceutical Sciences of Utrecht University. Yearly, this network is used to realize approximately 600 student internships (in hospital and community pharmacies) and 20 research projects. To date, most research has been performed in community pharmacy and research questions frequently concerned prescribing behavior or adherence and subjects related to uptake of regulations in the pharmacy setting. Researchers gain access to different types of data from daily practice, pharmacists receive feedback on the functioning of their own pharmacy and students get in depth insight into pharmacy practice.

  6. Neighborhood Historic Preservation Status and Housing Values in Oklahoma County, Oklahoma

    OpenAIRE

    Rickman, Dan S.

    2009-01-01

    Using county tax assessor data, this paper estimates the property value impacts of his-toric designation of neighborhoods for Oklahoma County, Oklahoma. Methodological contri-butions of the study include allowing for spatial and temporal variation of hedonic prices and historic district property values along with the use of finely-delineated spatial fixed effects. Neighborhood historic designation is found to be associated with significant relative apprecia-tion of housing values in most dist...

  7. The function of communities in protein interaction networks at multiple scales

    Directory of Open Access Journals (Sweden)

    Jones Nick S

    2010-07-01

    Full Text Available Abstract Background If biology is modular then clusters, or communities, of proteins derived using only protein interaction network structure should define protein modules with similar biological roles. We investigate the link between biological modules and network communities in yeast and its relationship to the scale at which we probe the network. Results Our results demonstrate that the functional homogeneity of communities depends on the scale selected, and that almost all proteins lie in a functionally homogeneous community at some scale. We judge functional homogeneity using a novel test and three independent characterizations of protein function, and find a high degree of overlap between these measures. We show that a high mean clustering coefficient of a community can be used to identify those that are functionally homogeneous. By tracing the community membership of a protein through multiple scales we demonstrate how our approach could be useful to biologists focusing on a particular protein. Conclusions We show that there is no one scale of interest in the community structure of the yeast protein interaction network, but we can identify the range of resolution parameters that yield the most functionally coherent communities, and predict which communities are most likely to be functionally homogeneous.

  8. Accurate detection of hierarchical communities in complex networks based on nonlinear dynamical evolution

    Science.gov (United States)

    Zhuo, Zhao; Cai, Shi-Min; Tang, Ming; Lai, Ying-Cheng

    2018-04-01

    One of the most challenging problems in network science is to accurately detect communities at distinct hierarchical scales. Most existing methods are based on structural analysis and manipulation, which are NP-hard. We articulate an alternative, dynamical evolution-based approach to the problem. The basic principle is to computationally implement a nonlinear dynamical process on all nodes in the network with a general coupling scheme, creating a networked dynamical system. Under a proper system setting and with an adjustable control parameter, the community structure of the network would "come out" or emerge naturally from the dynamical evolution of the system. As the control parameter is systematically varied, the community hierarchies at different scales can be revealed. As a concrete example of this general principle, we exploit clustered synchronization as a dynamical mechanism through which the hierarchical community structure can be uncovered. In particular, for quite arbitrary choices of the nonlinear nodal dynamics and coupling scheme, decreasing the coupling parameter from the global synchronization regime, in which the dynamical states of all nodes are perfectly synchronized, can lead to a weaker type of synchronization organized as clusters. We demonstrate the existence of optimal choices of the coupling parameter for which the synchronization clusters encode accurate information about the hierarchical community structure of the network. We test and validate our method using a standard class of benchmark modular networks with two distinct hierarchies of communities and a number of empirical networks arising from the real world. Our method is computationally extremely efficient, eliminating completely the NP-hard difficulty associated with previous methods. The basic principle of exploiting dynamical evolution to uncover hidden community organizations at different scales represents a "game-change" type of approach to addressing the problem of community

  9. Partial Information Community Detection in a Multilayer Network

    Science.gov (United States)

    2016-06-01

    26 3 Methodology 33 3.1 Topology of the Noordin Top Terrorist Network . . . . . . . . . . . . 33 3.2 Partial Information... Topology of Synthetic Network. . . . . . . . . . . . . . . . . . . 69 4.4 Four Discovery Algorithms Discovering Red Vertices in a Synthetic Network 72 4.5...without their expertise and analysis. I have been lucky enough to have learned from the wonderful faculty of Applied Mathe - matics Department at the Naval

  10. A dynamic evolutionary clustering perspective: Community detection in signed networks by reconstructing neighbor sets

    Science.gov (United States)

    Chen, Jianrui; Wang, Hua; Wang, Lina; Liu, Weiwei

    2016-04-01

    Community detection in social networks has been intensively studied in recent years. In this paper, a novel similarity measurement is defined according to social balance theory for signed networks. Inter-community positive links are found and deleted due to their low similarity. The positive neighbor sets are reconstructed by this method. Then, differential equations are proposed to imitate the constantly changing states of nodes. Each node will update its state based on the difference between its state and average state of its positive neighbors. Nodes in the same community will evolve together with time and nodes in the different communities will evolve far away. Communities are detected ultimately when states of nodes are stable. Experiments on real world and synthetic networks are implemented to verify detection performance. The thorough comparisons demonstrate the presented method is more efficient than two acknowledged better algorithms.

  11. Heuristic Artificial Bee Colony Algorithm for Uncovering Community in Complex Networks

    Directory of Open Access Journals (Sweden)

    Yuquan Guo

    2017-01-01

    Full Text Available Community structure is important for us to understand the functions and structure of the complex networks. In this paper, Heuristic Artificial Bee Colony (HABC algorithm based on swarm intelligence is proposed for uncovering community. The proposed HABC includes initialization, employed bee searching, onlooker searching, and scout bee searching. In initialization stage, the nectar sources with simple community structure are generated through network dynamic algorithm associated with complete subgraph. In employed bee searching and onlooker searching stages, the searching function is redefined to address the community problem. The efficiency of searching progress can be improved by a heuristic function which is an average agglomerate probability of two neighbor communities. Experiments are carried out on artificial and real world networks, and the results demonstrate that HABC will have better performance in terms of comparing with the state-of-the-art algorithms.

  12. FISCAL STRUCTURE OF OKLAHOMA, AN OVERVIEW.

    Science.gov (United States)

    SANDMEYER, ROBERT L.

    THE REPORT WAS DIVIDED INTO THREE MAJOR SECTIONS--(1) THE PRODUCTION POSSIBILITY CURVE WAS USED TO DEMONSTRATE THE PROBLEM OF RESOURCE ALLOCATION BETWEEN THE PUBLIC AND PRIVATE SECTORS, (2) STATE AND LOCAL REVENUES WERE EXAMINED IN TERMS OF FISCAL CAPACITY AND TAX EFFORT, AND (3) EXPENDITURES ON SELECTED FUNCTIONS OF GOVERNMENT IN OKLAHOMA WERE…

  13. The rise of China in the International Trade Network: a community core detection approach.

    Science.gov (United States)

    Zhu, Zhen; Cerina, Federica; Chessa, Alessandro; Caldarelli, Guido; Riccaboni, Massimo

    2014-01-01

    Theory of complex networks proved successful in the description of a variety of complex systems ranging from biology to computer science and to economics and finance. Here we use network models to describe the evolution of a particular economic system, namely the International Trade Network (ITN). Previous studies often assume that globalization and regionalization in international trade are contradictory to each other. We re-examine the relationship between globalization and regionalization by viewing the international trade system as an interdependent complex network. We use the modularity optimization method to detect communities and community cores in the ITN during the years 1995-2011. We find rich dynamics over time both inter- and intra-communities. In particular, the Asia-Oceania community disappeared and reemerged over time along with a switch in leadership from Japan to China. We provide a multilevel description of the evolution of the network where the global dynamics (i.e., communities disappear or reemerge) and the regional dynamics (i.e., community core changes between community members) are related. Moreover, simulation results show that the global dynamics can be generated by a simple dynamic-edge-weight mechanism.

  14. The development of a network for community-based obesity prevention: the CO-OPS Collaboration

    Science.gov (United States)

    2011-01-01

    Background Community-based interventions are a promising approach and an important component of a comprehensive response to obesity. In this paper we describe the Collaboration of COmmunity-based Obesity Prevention Sites (CO-OPS Collaboration) in Australia as an example of a collaborative network to enhance the quality and quantity of obesity prevention action at the community level. The core aims of the CO-OPS Collaboration are to: identify and analyse the lessons learned from a range of community-based initiatives aimed at tackling obesity, and; to identify the elements that make community-based obesity prevention initiatives successful and share the knowledge gained with other communities. Methods Key activities of the collaboration to date have included the development of a set of Best Practice Principles and knowledge translation and exchange activities to promote the application (or use) of evidence, evaluation and analysis in practice. Results The establishment of the CO-OPS Collaboration is a significant step toward strengthening action in this area, by bringing together research, practice and policy expertise to promote best practice, high quality evaluation and knowledge translation and exchange. Future development of the network should include facilitation of further evidence generation and translation drawing from process, impact and outcome evaluation of existing community-based interventions. Conclusions The lessons presented in this paper may help other networks like CO-OPS as they emerge around the globe. It is important that networks integrate with each other and share the experience of creating these networks. PMID:21349185

  15. Sparks in the Fog: Social and Economic Mechanisms as Enablers for Community Network Clouds

    Directory of Open Access Journals (Sweden)

    Muhammad Amin KHAN

    2014-10-01

    Full Text Available Internet and communication technologies have lowered the costs of enabling individuals and communities to collaborate together. This collaboration has provided new services like user-generated content and social computing, as evident from success stories like Wikipedia. Through collaboration, collectively built infrastructures like community wireless mesh networks where users provide the communication network, have also emerged. Community networks have demonstrated successful bandwidth sharing, but have not been able to extend their collective effort to other computing resources like storage and processing. The success of cloud computing has been enabled by economies of scale and the need for elastic, flexible and on-demand provisioning of computing services. The consolidation of today’s cloud technologies offers now the possibility of collectively built community clouds, building upon user-generated content and user-provided networks towards an ecosystem of cloud services. We explore in this paper how social and economic mechanisms can play a role in overcoming the barriers of voluntary resource provisioning in such community clouds, by analysing the costs involved in building these services and how they give value to the participants. We indicate socio-economic policies and how they can be implemented in community networks, to ease the uptake and ensure the sustainability of community clouds.

  16. Detecting overlapping community structure of networks based on vertex–vertex correlations

    International Nuclear Information System (INIS)

    Zarei, Mina; Izadi, Dena; Samani, Keivan Aghababaei

    2009-01-01

    Using the NMF (non-negative matrix factorization) method, the structure of overlapping communities in complex networks is investigated. For the feature matrix of the NMF method we introduce a vertex–vertex correlation matrix. The method is applied to some computer-generated and real-world networks. Simulations show that this feature matrix gives more reasonable results

  17. Community-Based Research Networks: Development and Lessons Learned in an Emerging Field.

    Science.gov (United States)

    Stoecker, Randy; Ambler, Susan H.; Cutforth, Nick; Donohue, Patrick; Dougherty, Dan; Marullo, Sam; Nelson, Kris S.; Stutts, Nancy B.

    2003-01-01

    Compares seven multi-institutional community-based research networks in Appalachia; Colorado; District of Columbia; Minneapolis-St. Paul; Philadelphia; Richmond, Virginia; and Trenton, New Jersey. After reviewing the histories of the networks, conducts a comparative SWOT analysis, showing their common and unique strengths, weaknesses,…

  18. The Illinois Community College Sustainability Network--A Successful Model

    Science.gov (United States)

    Jacobson, Bert

    2010-01-01

    In 2008, the Illinois Department of Commerce and Economic Opportunity (DCEO), Bureau of Energy and Recycling, funded a pilot project creating a network of Sustainability Centers. The pilot project demonstrated that networked campus sustainability centers are an efficient mechanism to reach consumers, business, and industry. All 48 community…

  19. Friendship Concept and Community Network Structure among Elementary School and University Students.

    Science.gov (United States)

    Hernández-Hernández, Ana María; Viga-de Alva, Dolores; Huerta-Quintanilla, Rodrigo; Canto-Lugo, Efrain; Laviada-Molina, Hugo; Molina-Segui, Fernanda

    2016-01-01

    We use complex network theory to study the differences between the friendship concepts in elementary school and university students. Four friendship networks were identified from surveys. Three of these networks are from elementary schools; two are located in the rural area of Yucatán and the other is in the urban area of Mérida, Yucatán. We analyzed the structure and the communities of these friendship networks and found significant differences among those at the elementary schools compared with those at the university. In elementary schools, the students make friends mainly in the same classroom, but there are also links among different classrooms because of the presence of siblings and relatives in the schools. These kinds of links (sibling-friend or relative-friend) are called, in this work, "mixed links". The classification of the communities is based on their similarity with the classroom composition. If the community is composed principally of students in different classrooms, the community is classified as heterogeneous. These kinds of communities appear in the elementary school friendship networks mainly because of the presence of relatives and siblings. Once the links between siblings and relatives are removed, the communities resembled the classroom composition. On the other hand, the university students are more selective in choosing friends and therefore, even when they have friends in the same classroom, those communities are quite different to the classroom composition. Also, in the university network, we found heterogeneous communities even when the presence of sibling and relatives is negligible. These differences made up a topological structure quite different at different academic levels. We also found differences in the network characteristics. Once these differences are understood, the topological structure of the friendship network and the communities shaped in an elementary school could be predicted if we know the total number of students

  20. Water Use in Oklahoma 1950-2005

    Science.gov (United States)

    Tortorelli, Robert L.

    2009-01-01

    Comprehensive planning for water resources development and use in Oklahoma requires a historical perspective on water resources. The U.S. Geological Survey, in cooperation with the Oklahoma Water Resources Board, summarized the 1950-2005 water-use information for Oklahoma. This report presents 1950-2005 estimates of freshwater withdrawal for water use in Oklahoma by source and category in 5-year intervals. Withdrawal source was either surface water or groundwater. Withdrawal categories include: public supply, irrigation, livestock and aquaculture, thermoelectric-power generation (cooling water), domestic and commercial, and industrial and mining. Withdrawal data were aggregated and tabulated by county, major river basin, and principal aquifer. The purpose of this report is to summarize water-use data in Oklahoma through: (1) presentation of detailed information on freshwater withdrawals by source, county, major river basin, and principal aquifer for 2005; (2) comparison of water use by source, category, major river basin, and principal aquifer at 5-year intervals from 1990-2005; and (3) comparison of water use on a statewide basis by source and category at 5-year intervals from 1950-2005. Total withdrawals from surface-water and groundwater sources during 2005 were 1,559 million gallons per day-989 million gallons a day or 63 percent from surface-water sources and 570 million gallons per day or 37 percent from groundwater sources. The three largest water use categories were: public supply, 646 million gallons per day or 41 percent of total withdrawals; irrigation, 495 million gallons per day or 32 percent of total withdrawals; and livestock and aquaculture, 181 million gallons per day or 12 percent of total withdrawals. All other categories were 237 million gallons per day or 15 percent of total withdrawals. The influence of public supply on the total withdrawals can be seen in the eastern two-thirds of Oklahoma; whereas, the influence of irrigation on total

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

    Directory of Open Access Journals (Sweden)

    Ruopeng An

    2017-06-01

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

  2. Nuclear Community in network; La comunidad nuclear en la Red

    Energy Technology Data Exchange (ETDEWEB)

    Tejedor, E.

    2014-02-01

    The internet has revolutionized the ways of communication and many companies/ organizations have adapted to the change but others have not, or have done it halfway. This presentation is a review of the main characteristics of virtual communities and their typology. The status of the Nuclear Online Community, both pro nuclear and antinuclear is analysed , and their main similarities and differences are discussed. The Pro nuclear Online Community is formed gradually. This presentation attempts to define some ways to increase the scope of the Community and encourage greater dissemination of the characteristics of nuclear energy. (Author)

  3. Links between real and virtual networks: a comparative study of online communities in Japan and Korea.

    Science.gov (United States)

    Ishii, Kenichi; Ogasahara, Morihiro

    2007-04-01

    The present study explores how online communities affect real-world personal relations based on a cross-cultural survey conducted in Japan and Korea. Findings indicate that the gratifications of online communities moderate the effects of online communities on social participation. Online communities are categorized into a real-group-based community and a virtual-network-based community. The membership of real-group-based online community is positively correlated with social bonding gratification and negatively correlated with information- seeking gratification. Japanese users prefer more virtual-network-based online communities, while their Korean counterparts prefer real-group-based online communities. Korean users are more active in online communities and seek a higher level of socializing gratifications, such as social bonding and making new friends, when compared with their Japanese counterparts. These results indicate that in Korea, personal relations via the online community are closely associated with the real-world personal relations, but this is not the case in Japan. This study suggests that the effects of the Internet are culture-specific and that the online community can serve a different function in different cultural environments.

  4. Network Analysis of a Virtual Community of Learning of Economics Educators

    Science.gov (United States)

    Fontainha, Elsa; Martins, Jorge Tiago; Vasconcelos, Ana Cristina

    2015-01-01

    Introduction: This paper aims at understanding virtual communities of learning in terms of dynamics, types of knowledge shared by participants, and network characteristics such as size, relationships, density, and centrality of participants. It looks at the relationships between these aspects and the evolution of communities of learning. It…

  5. Automated Library Networking in American Public Community College Learning Resources Centers.

    Science.gov (United States)

    Miah, Adbul J.

    1994-01-01

    Discusses the need for community colleges to assess their participation in automated library networking systems (ALNs). Presents results of questionnaires sent to 253 community college learning resource center directors to determine their use of ALNs. Reviews benefits of automation and ALN activities, planning and communications, institution size,…

  6. Paradoxes of Social Networking in a Structured Web 2.0 Language Learning Community

    Science.gov (United States)

    Loiseau, Mathieu; Zourou, Katerina

    2012-01-01

    This paper critically inquires into social networking as a set of mechanisms and associated practices developed in a structured Web 2.0 language learning community. This type of community can be roughly described as learning spaces featuring (more or less) structured language learning resources displaying at least some notions of language learning…

  7. Exploring anti-community structure in networks with application to incompatibility of traditional Chinese medicine

    Science.gov (United States)

    Zhu, Jiajing; Liu, Yongguo; Zhang, Yun; Liu, Xiaofeng; Xiao, Yonghua; Wang, Shidong; Wu, Xindong

    2017-11-01

    Community structure is one of the most important properties in networks, in which a node shares its most connections with the others in the same community. On the contrary, the anti-community structure means the nodes in the same group have few or no connections with each other. In Traditional Chinese Medicine (TCM), the incompatibility problem of herbs is a challenge to the clinical medication safety. In this paper, we propose a new anti-community detection algorithm, Random non-nEighboring nOde expansioN (REON), to find anti-communities in networks, in which a new evaluation criterion, anti-modularity, is designed to measure the quality of the obtained anti-community structure. In order to establish anti-communities in REON, we expand the node set by non-neighboring node expansion and regard the node set with the highest anti-modularity as an anti-community. Inspired by the phenomenon that the node with higher degree has greater contribution to the anti-modularity, an improved algorithm called REONI is developed by expanding node set by the non-neighboring node with the maximum degree, which greatly enhances the efficiency of REON. Experiments on synthetic and real-world networks demonstrate the superiority of the proposed algorithms over the existing methods. In addition, by applying REONI to the herb network, we find that it can discover incompatible herb combinations.

  8. Interaction Patterns in Web-based Knowledge Communities: Two-Mode Network Approach

    NARCIS (Netherlands)

    Vollenbroek, Wouter Bernardus; de Vries, Sjoerd A.; Fred, Ana; Dietz, Jan; Aveiro, David; Liu, Kecheng; Bernardino, Jorge; Filipe, Joaquim

    2016-01-01

    The importance of web-based knowledge communities (WKCs) in the 'network society' is growing. This trend is seen in many disciplines, like education, government, finance and other profit- and non-profit organisations. There is a need for understanding the development of these online communities in

  9. Magnets and Seekers: A Network Perspective on Academic Integration inside Two Residential Communities

    Science.gov (United States)

    Smith, Rachel A.

    2015-01-01

    Residential learning communities aim to foster increased academic and social integration, ideally leading to greater student success. However, the concept of academic integration is often conceptualized and measured at the individual level, rather than the theoretically more consistent community level. Network analysis provides a paradigm and…

  10. Writing Programs as Distributed Networks: A Materialist Approach to University-Community Digital Media Literacy

    Science.gov (United States)

    Comstock, Michelle

    2006-01-01

    This article addresses how community-university digital media literacy projects are redefining literacy, literate practices, and institutions. Using Actor-Network Theory (ANT), which emphasizes the organizing process itself, I analyze the shifting definitions of literacy within one particular university-community collaboration. My analysis…

  11. Gaseous Oxidized Mercury Dry Deposition Measurements in the FourCorners Area and Eastern Oklahoma, U.S.A.

    Science.gov (United States)

    Gaseous oxidized mercury (GOM) dry deposition measurements using surrogate surface passive samplers were collected in the Four Corners area and eastern Oklahoma from August, 2009–August, 2011. Using data from a six site area network, a characterization of the magnitude and spatia...

  12. Applying information network analysis to fire-prone landscapes: implications for community resilience

    Directory of Open Access Journals (Sweden)

    Derric B. Jacobs

    2017-03-01

    Full Text Available Resilient communities promote trust, have well-developed networks, and can adapt to change. For rural communities in fire-prone landscapes, current resilience strategies may prove insufficient in light of increasing wildfire risks due to climate change. It is argued that, given the complexity of climate change, adaptations are best addressed at local levels where specific social, cultural, political, and economic conditions are matched with local risks and opportunities. Despite the importance of social networks as key attributes of community resilience, research using social network analysis on coupled human and natural systems is scarce. Furthermore, the extent to which local communities in fire-prone areas understand climate change risks, accept the likelihood of potential changes, and have the capacity to develop collaborative mitigation strategies is underexamined, yet these factors are imperative to community resiliency. We apply a social network framework to examine information networks that affect perceptions of wildfire and climate change in Central Oregon. Data were collected using a mailed questionnaire. Analysis focused on the residents' information networks that are used to gain awareness of governmental activities and measures of community social capital. A two-mode network analysis was used to uncover information exchanges. Results suggest that the general public develops perceptions about climate change based on complex social and cultural systems rather than as patrons of scientific inquiry and understanding. It appears that perceptions about climate change itself may not be the limiting factor in these communities' adaptive capacity, but rather how they perceive local risks. We provide a novel methodological approach in understanding rural community adaptation and resilience in fire-prone landscapes and offer a framework for future studies.

  13. Community Garden Information Systems: Analyzing and Strengthening Community-Based Resource Sharing Networks

    Science.gov (United States)

    Loria, Kristen

    2013-01-01

    Extension professionals play an increasingly central role in supporting community garden and other community-based agriculture projects. With growing interest in community gardens as tools to improve community health and vitality, the best strategies for supporting these projects should be explored. Due to the importance of inter-personal networks…

  14. Cluster synchronization of community network with distributed time delays via impulsive control

    International Nuclear Information System (INIS)

    Leng Hui; Wu Zhao-Yan

    2016-01-01

    Cluster synchronization is an important dynamical behavior in community networks and deserves further investigations. A community network with distributed time delays is investigated in this paper. For achieving cluster synchronization, an impulsive control scheme is introduced to design proper controllers and an adaptive strategy is adopted to make the impulsive controllers unified for different networks. Through taking advantage of the linear matrix inequality technique and constructing Lyapunov functions, some synchronization criteria with respect to the impulsive gains, instants, and system parameters without adaptive strategy are obtained and generalized to the adaptive case. Finally, numerical examples are presented to demonstrate the effectiveness of the theoretical results. (paper)

  15. Fluctuating interaction network and time-varying stability of a natural fish community

    Science.gov (United States)

    Ushio, Masayuki; Hsieh, Chih-Hao; Masuda, Reiji; Deyle, Ethan R.; Ye, Hao; Chang, Chun-Wei; Sugihara, George; Kondoh, Michio

    2018-02-01

    Ecological theory suggests that large-scale patterns such as community stability can be influenced by changes in interspecific interactions that arise from the behavioural and/or physiological responses of individual species varying over time. Although this theory has experimental support, evidence from natural ecosystems is lacking owing to the challenges of tracking rapid changes in interspecific interactions (known to occur on timescales much shorter than a generation time) and then identifying the effect of such changes on large-scale community dynamics. Here, using tools for analysing nonlinear time series and a 12-year-long dataset of fortnightly collected observations on a natural marine fish community in Maizuru Bay, Japan, we show that short-term changes in interaction networks influence overall community dynamics. Among the 15 dominant species, we identify 14 interspecific interactions to construct a dynamic interaction network. We show that the strengths, and even types, of interactions change with time; we also develop a time-varying stability measure based on local Lyapunov stability for attractor dynamics in non-equilibrium nonlinear systems. We use this dynamic stability measure to examine the link between the time-varying interaction network and community stability. We find seasonal patterns in dynamic stability for this fish community that broadly support expectations of current ecological theory. Specifically, the dominance of weak interactions and higher species diversity during summer months are associated with higher dynamic stability and smaller population fluctuations. We suggest that interspecific interactions, community network structure and community stability are dynamic properties, and that linking fluctuating interaction networks to community-level dynamic properties is key to understanding the maintenance of ecological communities in nature.

  16. Influence of network communities and transition to web 3.0 on change of approaches

    Directory of Open Access Journals (Sweden)

    Ринат Гинаятович Рамазанов

    2018-12-01

    Full Text Available The article presents the genesis of network communities, the transformation of the interaction of participants in the evolution of this form of communication. The main characteristics and stages inherent in changing the formats of network interaction are described. The concept of networked communities is multifaceted and includes various processes of socialization: from the behaviour of people in interest groups to the formation of large international online communities. The evolution of such communities includes the transition from guest books and forums to more complex systems with many additional tools for networking. The introduction of modern technologies in all spheres of life leads to the formation of a qualitatively new distinctive model of education. In such conditions, the roles of participants in the educational process are changing, more attention is paid to self-education and development at the expense of the internal need for learning, the organizational and methodological component of the educational process is being reconstructed, and new conditions are created for the development of distance learning technologies. All this allows us to develop a more competitive system of interaction, make management processes transparent and create conditions for the independent development of each participant in the network space. Online network communities are a powerful tool that must be used in the education system in various interpretations, and as an additional resource that allows the principle of “lifelong learning” to be introduced into the teacher’s self-education system.

  17. Local communities obstruct global consensus: Naming game on multi-local-world networks

    Science.gov (United States)

    Lou, Yang; Chen, Guanrong; Fan, Zhengping; Xiang, Luna

    2018-02-01

    Community structure is essential for social communications, where individuals belonging to the same community are much more actively interacting and communicating with each other than those in different communities within the human society. Naming game, on the other hand, is a social communication model that simulates the process of learning a name of an object within a community of humans, where the individuals can generally reach global consensus asymptotically through iterative pair-wise conversations. The underlying network indicates the relationships among the individuals. In this paper, three typical topologies, namely random-graph, small-world and scale-free networks, are employed, which are embedded with the multi-local-world community structure, to study the naming game. Simulations show that (1) the convergence process to global consensus is getting slower as the community structure becomes more prominent, and eventually might fail; (2) if the inter-community connections are sufficiently dense, neither the number nor the size of the communities affects the convergence process; and (3) for different topologies with the same (or similar) average node-degree, local clustering of individuals obstruct or prohibit global consensus to take place. The results reveal the role of local communities in a global naming game in social network studies.

  18. The community structure of the European network of interlocking directorates 2005-2010.

    Directory of Open Access Journals (Sweden)

    Eelke M Heemskerk

    Full Text Available The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer.

  19. The community structure of the European network of interlocking directorates 2005-2010.

    Science.gov (United States)

    Heemskerk, Eelke M; Daolio, Fabio; Tomassini, Marco

    2013-01-01

    The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer.

  20. The Community Structure of the European Network of Interlocking Directorates 2005–2010

    Science.gov (United States)

    Heemskerk, Eelke M.; Daolio, Fabio; Tomassini, Marco

    2013-01-01

    The boards of directors at large European companies overlap with each other to a sizable extent both within and across national borders. This could have important economic, political and management consequences. In this work we study in detail the topological structure of the networks that arise from this phenomenon. Using a comprehensive information database, we reconstruct the implicit networks of shared directorates among the top 300 European firms in 2005 and 2010, and suggest a number of novel ways to explore the trans-nationality of such business elite networks. Powerful community detection heuristics indicate that geography still plays an important role: there exist clear communities and they have a distinct national character. Nonetheless, from 2005 to 2010 we observe a densification of the boards interlocks network and a larger transnational orientation in its communities. Together with central actors and assortativity analyses, we provide statistical evidence that, at the level of corporate governance, Europe is getting closer. PMID:23894318

  1. Startup : Philippine Community eCentres Network | IDRC ...

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

    ... served as a model for networking the telecentres and pointing future directions. ... long-term climate action to reduce social inequality, promote greater gender parity, ... including heat stress, water management, and climate-related migration.

  2. Multi-scale analysis of the European airspace using network community detection.

    Directory of Open Access Journals (Sweden)

    Gérald Gurtner

    Full Text Available We show that the European airspace can be represented as a multi-scale traffic network whose nodes are airports, sectors, or navigation points and links are defined and weighted according to the traffic of flights between the nodes. By using a unique database of the air traffic in the European airspace, we investigate the architecture of these networks with a special emphasis on their community structure. We propose that unsupervised network community detection algorithms can be used to monitor the current use of the airspace and improve it by guiding the design of new ones. Specifically, we compare the performance of several community detection algorithms, both with fixed and variable resolution, and also by using a null model which takes into account the spatial distance between nodes, and we discuss their ability to find communities that could be used to define new control units of the airspace.

  3. Social network, social support, and risk of incident stroke: Atherosclerosis Risk in Communities study.

    Science.gov (United States)

    Nagayoshi, Mako; Everson-Rose, Susan A; Iso, Hiroyasu; Mosley, Thomas H; Rose, Kathryn M; Lutsey, Pamela L

    2014-10-01

    Having a small social network and lack of social support have been associated with incident coronary heart disease; however, epidemiological evidence for incident stroke is limited. We assessed the longitudinal association of a small social network and lack of social support with risk of incident stroke and evaluated whether the association was partly mediated by vital exhaustion and inflammation. The Atherosclerosis Risk in Communities study measured social network and social support in 13 686 men and women (mean, 57 years; 56% women; 24% black; 76% white) without a history of stroke. Social network was assessed by the 10-item Lubben Social Network Scale and social support by a 16-item Interpersonal Support Evaluation List-Short Form. During a median follow-up of 18.6 years, 905 incident strokes occurred. Relative to participants with a large social network, those with a small social network had a higher risk of stroke (hazard ratio [95% confidence interval], 1.44 [1.02-2.04]) after adjustment for demographics, socioeconomic variables, marital status, behavioral risk factors, and major stroke risk factors. Vital exhaustion, but not inflammation, partly mediated the association between a small social network and incident stroke. Social support was unrelated to incident stroke. In this sample of US community-dwelling men and women, having a small social network was associated with excess risk of incident stroke. As with other cardiovascular conditions, having a small social network may be associated with a modestly increased risk of incident stroke. © 2014 American Heart Association, Inc.

  4. Discursive Deployments: Mobilizing Support for Municipal and Community Wireless Networks in the U.S.

    Energy Technology Data Exchange (ETDEWEB)

    Alvarez, Rosio; Rodriguez, Juana Maria

    2008-08-16

    This paper examines Municipal Wireless (MW) deployments in the United States. In particular, the interest is in understanding how discourse has worked to mobilize widespread support for MW networks. We explore how local governments discursively deploy the language of social movements to create a shared understanding of the networking needs of communities. Through the process of"framing" local governments assign meaning to the MW networks in ways intended to mobilize support anddemobilize opposition. The mobilizing potential of a frame varies and is dependent on its centrality and cultural resonance. We examine the framing efforts of MW networks by using a sample of Request for Proposals for community wireless networks, semi-structured interviews and local media sources. Prominent values that are central to a majority of the projects and others that are culturally specific are identified and analyzed for their mobilizing potency.

  5. Creation and Sharing of Environmental Knowledge across Communities and Networks

    DEFF Research Database (Denmark)

    Søndergård, Bent; Hansen, Ole Erik; Holm, Jesper

    2004-01-01

    Environmental communication is analysed with reference an understanding of knowledge as situated enacted practice. Exchange of environmental knowledge is conceptualised as processes of creating and transfer/translation of knowledge in and between communities of practices and as conditioned...

  6. 76 FR 17584 - Approval and Promulgation of Implementation Plans; Oklahoma; Regional Haze State Implementation...

    Science.gov (United States)

    2011-03-30

    ..., Springlake Campus, Business Conference Center, Meeting Rooms H and I, 1900 Springlake Drive, Oklahoma City... Campus, Business Conference Center, Meeting Rooms H and I, 1900 Springlake Drive, Oklahoma City, Oklahoma... City Zoo and Kirkpatrick Center. Parking for the [[Page 17585

  7. Communities detection as a tool to assess a reform of the Italian interlocking directorship network

    Science.gov (United States)

    Drago, Carlo; Ricciuti, Roberto

    2017-01-01

    Interlocking directorships are important communication channels among companies and may have anticompetitive effect. A corporate governance reform was introduced in 2011 to prevent interlocking directorships in the financial sector. We apply community detection techniques to the analysis of the networks in 2009 and 2012 to ascertain the effect of such reform on the Italian directorship network. We find that, although the number of interlocking directorships decreases in 2012, the reduction takes place mainly at the periphery of the network. The network core is stable, allowing the most connected companies to keep their strategic position.

  8. Detection of stable community structures within gut microbiota co-occurrence networks from different human populations.

    Science.gov (United States)

    Jackson, Matthew A; Bonder, Marc Jan; Kuncheva, Zhana; Zierer, Jonas; Fu, Jingyuan; Kurilshikov, Alexander; Wijmenga, Cisca; Zhernakova, Alexandra; Bell, Jordana T; Spector, Tim D; Steves, Claire J

    2018-01-01

    Microbes in the gut microbiome form sub-communities based on shared niche specialisations and specific interactions between individual taxa. The inter-microbial relationships that define these communities can be inferred from the co-occurrence of taxa across multiple samples. Here, we present an approach to identify comparable communities within different gut microbiota co-occurrence networks, and demonstrate its use by comparing the gut microbiota community structures of three geographically diverse populations. We combine gut microbiota profiles from 2,764 British, 1,023 Dutch, and 639 Israeli individuals, derive co-occurrence networks between their operational taxonomic units, and detect comparable communities within them. Comparing populations we find that community structure is significantly more similar between datasets than expected by chance. Mapping communities across the datasets, we also show that communities can have similar associations to host phenotypes in different populations. This study shows that the community structure within the gut microbiota is stable across populations, and describes a novel approach that facilitates comparative community-centric microbiome analyses.

  9. Providing interoperability of eHealth communities through peer-to-peer networks.

    Science.gov (United States)

    Kilic, Ozgur; Dogac, Asuman; Eichelberg, Marco

    2010-05-01

    Providing an interoperability infrastructure for Electronic Healthcare Records (EHRs) is on the agenda of many national and regional eHealth initiatives. Two important integration profiles have been specified for this purpose, namely, the "Integrating the Healthcare Enterprise (IHE) Cross-enterprise Document Sharing (XDS)" and the "IHE Cross Community Access (XCA)." IHE XDS describes how to share EHRs in a community of healthcare enterprises and IHE XCA describes how EHRs are shared across communities. However, the current version of the IHE XCA integration profile does not address some of the important challenges of cross-community exchange environments. The first challenge is scalability. If every community that joins the network needs to connect to every other community, i.e., a pure peer-to-peer network, this solution will not scale. Furthermore, each community may use a different coding vocabulary for the same metadata attribute, in which case, the target community cannot interpret the query involving such an attribute. Yet another important challenge is that each community may (and typically will) have a different patient identifier domain. Querying for the patient identifiers in the target community using patient demographic data may create patient privacy concerns. In this paper, we address each of these challenges and show how they can be handled effectively in a superpeer-based peer-to-peer architecture.

  10. Analysis of the social network development of a virtual community for Australian intensive care professionals.

    Science.gov (United States)

    Rolls, Kaye Denise; Hansen, Margaret; Jackson, Debra; Elliott, Doug

    2014-11-01

    Social media platforms can create virtual communities, enabling healthcare professionals to network with a broad range of colleagues and facilitate knowledge exchange. In 2003, an Australian state health department established an intensive care mailing list to address the professional isolation experienced by senior intensive care nurses. This article describes the social network created within this virtual community by examining how the membership profile evolved from 2003 to 2009. A retrospective descriptive design was used. The data source was a deidentified member database. Since 2003, 1340 healthcare professionals subscribed to the virtual community with 78% of these (n = 1042) still members at the end of 2009. The membership profile has evolved from a single-state nurse-specific network to an Australia-wide multidisciplinary and multiorganizational intensive care network. The uptake and retention of membership by intensive care clinicians indicated that they appeared to value involvement in this virtual community. For healthcare organizations, a virtual community may be a communications option for minimizing professional and organizational barriers and promoting knowledge flow. Further research is, however, required to demonstrate a link between these broader social networks, enabling the exchange of knowledge and improved patient outcomes.

  11. Identifying the community structure of the food-trade international multi-network

    Science.gov (United States)

    Torreggiani, S.; Mangioni, G.; Puma, M. J.; Fagiolo, G.

    2018-05-01

    Achieving international food security requires improved understanding of how international trade networks connect countries around the world through the import-export flows of food commodities. The properties of international food trade networks are still poorly documented, especially from a multi-network perspective. In particular, nothing is known about the multi-network’s community structure. Here we find that the individual crop-specific layers of the multi-network have densely connected trading groups, a consistent characteristic over the period 2001–2011. Further, the multi-network is characterized by low variability over this period but with substantial heterogeneity across layers in each year. In particular, the layers are mostly assortative: more-intensively connected countries tend to import from and export to countries that are themselves more connected. We also fit econometric models to identify social, economic and geographic factors explaining the probability that any two countries are co-present in the same community. Our estimates indicate that the probability of country pairs belonging to the same food trade community depends more on geopolitical and economic factors—such as geographical proximity and trade-agreement co-membership—than on country economic size and/or income. These community-structure findings of the multi-network are especially valuable for efforts to understand past and emerging dynamics in the global food system, especially those that examine potential ‘shocks’ to global food trade.

  12. Understanding interactions in virtual HIV communities: a social network analysis approach.

    Science.gov (United States)

    Shi, Jingyuan; Wang, Xiaohui; Peng, Tai-Quan; Chen, Liang

    2017-02-01

    This study investigated the driving mechanism of building interaction ties among the people living with HIV/AIDS in one of the largest virtual HIV communities in China using social network analysis. Specifically, we explained the probability of forming interaction ties with homophily and popularity characteristics. The exponential random graph modeling results showed that members in this community tend to form homophilous ties in terms of shared location and interests. Moreover, we found a tendency away from popularity effect. This suggests that in this community, resources and information were not disproportionally received by a few of members, which could be beneficial to the overall community.

  13. Use of community engagement strategies to increase research participation in practice-based research networks (PBRNs).

    Science.gov (United States)

    Spears, William; Tsoh, Janice Y; Potter, Michael B; Weller, Nancy; Brown, Anthony E; Campbell-Voytal, Kimberly; Getrich, Christina M; Sussman, Andrew L; Pascoe, John; Neale, Anne Victoria

    2014-01-01

    Practice-based research networks (PBRNs) are increasingly encouraged to use community engagement approaches. The extent to which PBRNs engage clinic and community partners in strategies to recruit and retain participants from their local communities (specifically racial/ethnic communities) is the focus of this study. The design was a cross-sectional survey of PBRN directors in the United States. Survey respondents indicated whether their research network planned for, implemented, and has capacity for activities that engage clinic and community partners in 7 recommended strategies organized into study phases, called the cycle of trust. The objectives of the national survey were to (1) describe the extent to which PBRNs across the United States routinely implement the strategies recommended for recruiting diverse patient groups and (2) identify factors associated with implementing the recommended strategies. The survey response rate was 63%. Activities that build trust often are used more with clinic partners than with community partners. PBRNs that adopt engagement strategies when working with clinic and community partners have less difficulty in recruiting diverse populations. Multivariate analysis showed that the targeting racial/ethnic communities for study recruitment, Clinical and Translational Science Award affiliation, and planning to use community engagement strategies were independent correlates of PBRN implementation of the recommended strategies. PBRNs that successfully engage racial/ethnic communities as research partners use community engagement strategies. New commitments are needed to support PBRN researchers in developing relationships with the communities in which their patients live. Stable PBRN infrastructure funding that appreciates the value of maintaining community engagement between funded studies is critical to the research enterprise that values translating research findings into generalizable care models for patients in the community.

  14. Partnership capacity for community health improvement plan implementation: findings from a social network analysis.

    Science.gov (United States)

    McCullough, J Mac; Eisen-Cohen, Eileen; Salas, S Bianca

    2016-07-13

    Many health departments collaborate with community organizations on community health improvement processes. While a number of resources exist to plan and implement a community health improvement plan (CHIP), little empirical evidence exists on how to leverage and expand partnerships when implementing a CHIP. The purpose of this study was to identify characteristics of the network involved in implementing the CHIP in one large community. The aims of this analysis are to: 1) identify essential network partners (and thereby highlight potential network gaps), 2) gauge current levels of partner involvement, 3) understand and effectively leverage network resources, and 4) enable a data-driven approach for future collaborative network improvements. We collected primary data via survey from n = 41 organizations involved in the Health Improvement Partnership of Maricopa County (HIPMC), in Arizona. Using the previously validated Program to Analyze, Record, and Track Networks to Enhance Relationships (PARTNER) tool, organizations provided information on existing ties with other coalition members, including frequency and depth of partnership and eight categories of perceived value/trust of each current partner organization. The coalition's overall network had a density score of 30 %, degree centralization score of 73 %, and trust score of 81 %. Network maps are presented to identify existing relationships between HIPMC members according to partnership frequency and intensity, duration of involvement in the coalition, and self-reported contributions to the coalition. Overall, number of ties and other partnership measures were positively correlated with an organization's perceived value and trustworthiness as rated by other coalition members. Our study presents a novel use of social network analysis methods to evaluate the coalition of organizations involved in implementing a CHIP in an urban community. The large coalition had relatively low network density but high

  15. Bioinformatics Training Network (BTN): a community resource for bioinformatics trainers

    DEFF Research Database (Denmark)

    Schneider, Maria V.; Walter, Peter; Blatter, Marie-Claude

    2012-01-01

    and clearly tagged in relation to target audiences, learning objectives, etc. Ideally, they would also be peer reviewed, and easily and efficiently accessible for downloading. Here, we present the Bioinformatics Training Network (BTN), a new enterprise that has been initiated to address these needs and review...

  16. Growing up with Social Networks and Online Communities

    Science.gov (United States)

    Strom, Paris; Strom, Robert

    2012-01-01

    This presentation examines child and adolescent social networking with an emphasis on how this unprecedented form of communication can be used to contribute to healthy growth and development. Most literature about child and adolescent relationships reflects yesterday's world, a time when face-to-face encounters were the only concern. Students saw…

  17. How plants connect pollination and herbivory networks and their contribution to community stability.

    Science.gov (United States)

    Sauve, Alix M C; Thébault, Elisa; Pocock, Michael J O; Fontaine, Colin

    2016-04-01

    Pollination and herbivory networks have mainly been studied separately, highlighting their distinct structural characteristics and the related processes and dynamics. However, most plants interact with both pollinators and herbivores, and there is evidence that both types of interaction affect each other. Here we investigated the way plants connect these mutualistic and antagonistic networks together, and the consequences for community stability. Using an empirical data set, we show that the way plants connect pollination and herbivory networks is not random and promotes community stability. Analyses of the structure of binary and quantitative networks show different results: the plants' generalism with regard to pollinators is positively correlated to their generalism with regard to herbivores when considering binary interactions, but not when considering quantitative interactions. We also show that plants that share the same pollinators do not share the same herbivores. However, the way plants connect pollination and herbivory networks promotes stability for both binary and quantitative networks. Our results highlight the relevance of considering the diversity of interaction types in ecological communities, and stress the need to better quantify the costs and benefits of interactions, as well as to develop new metrics characterizing the way different interaction types are combined within ecological networks.

  18. Networks and learning: communities, practices and the metaphor of networks–a commentary

    Directory of Open Access Journals (Sweden)

    Bruce Ingraham

    2004-12-01

    Full Text Available In issue 12(1, Jones (2004 in his article ‘Networks and learning: communities, practices and the metaphor of networks' sets out to address three inter-related sets of issues: … firstly that learning technology needs to take account of the wider debate about networks and secondly that research in this field needs to address the theoretical and practical issues raised by advances in the field of networks. A third point is that the idea of the network acts as a powerful metaphor even if we are able to discount any particular theory generated in its support. The network metaphor can act as a unifying concept allowing us to bring together apparently disparate elements of the field.

  19. Mining Community-Level Influence in Microblogging Network: A Case Study on Sina Weibo

    Directory of Open Access Journals (Sweden)

    Yufei Liu

    2017-01-01

    Full Text Available Social influence analysis is important for many social network applications, including recommendation and cybersecurity analysis. We observe that the influence of community including multiple users outweighs the individual influence. Existing models focus on the individual influence analysis, but few studies estimate the community influence that is ubiquitous in online social network. A major challenge lies in that researchers need to take into account many factors, such as user influence, social trust, and user relationship, to model community-level influence. In this paper, aiming to assess the community-level influence effectively and accurately, we formulate the problem of modeling community influence and construct a community-level influence analysis model. It first eliminates the zombie fans and then calculates the user influence. Next, it calculates the user final influence by combining the user influence and the willingness of diffusing theme information. Finally, it evaluates the community influence by comprehensively studying the user final influence, social trust, and relationship tightness between intrausers of communities. To handle real-world applications, we propose a community-level influence analysis algorithm called CIAA. Empirical studies on a real-world dataset from Sina Weibo demonstrate the superiority of the proposed model.

  20. Closer to Learning: Social Networks, Trust, and Professional Communities

    Science.gov (United States)

    Liou, Yi-Hwa; Daly, Alan J.

    2014-01-01

    Researchers, educators, and policymakers suggest the use of professional learning communities as one important approach to the improvement of teaching and learning. However, relatively little research examines the interplay of professional interactions (structural social capital) around instructional practices and key elements of professional…

  1. Heterogeneous Community-based mobility model for human opportunistic network

    DEFF Research Database (Denmark)

    Hu, Liang; Dittmann, Lars

    2009-01-01

    a heterogeneous community-based random way-point (HC-RWP) mobility model that captures the four important properties of real human mobility. These properties are based on both intuitive observations of daily human mobility and analysis of empirical mobility traces. By discrete event simulation, we show HC...

  2. Social Relation Networks in UT-Online Community Forum

    Science.gov (United States)

    Farisi, Mohammad Imam

    2012-01-01

    So far, the existence of a virtual community forum has become a reality and social necessity in an era cybertech. It was also viewed as the electronic frontier of 21st century society that was undoubtedly for reorganizing and redefining to awareness of human being, that ways of their perceptions and explorations no longer limited by time, space,…

  3. Improving the recommender algorithms with the detected communities in bipartite networks

    Science.gov (United States)

    Zhang, Peng; Wang, Duo; Xiao, Jinghua

    2017-04-01

    Recommender system offers a powerful tool to make information overload problem well solved and thus gains wide concerns of scholars and engineers. A key challenge is how to make recommendations more accurate and personalized. We notice that community structures widely exist in many real networks, which could significantly affect the recommendation results. By incorporating the information of detected communities in the recommendation algorithms, an improved recommendation approach for the networks with communities is proposed. The approach is examined in both artificial and real networks, the results show that the improvement on accuracy and diversity can be 20% and 7%, respectively. This reveals that it is beneficial to classify the nodes based on the inherent properties in recommender systems.

  4. Fox Chase Network: Fox Chase Cancer Center's community hospital affiliation program.

    Science.gov (United States)

    Higman, S A; McKay, F J; Engstrom, P F; O'Grady, M A; Young, R C

    2000-01-01

    Fox Chase Cancer Center developed a format for affiliation with community providers in 1986. Fox Chase Network was formed to establish hospital-based community cancer centers to increase access to patients involved in clinical research. Under this program, the Fox Chase Network now contributes 500 patients per year to prevention and clinical research studies. As relationships with community providers form, patient referrals have increased at Fox Chase Cancer Center and for each Fox Chase Network member. A dedicated staff is required to operate the central office on a day-to-day basis as well as at each affiliate. We have found this to be a critical element in each program's success. New challenges in the cancer business-increasing volumes with declining revenue-have caused us to reconfigure the services offered to affiliates, while maintaining true to our mission: to reduce the burden of human cancer.

  5. Community detection in complex networks using deep auto-encoded extreme learning machine

    Science.gov (United States)

    Wang, Feifan; Zhang, Baihai; Chai, Senchun; Xia, Yuanqing

    2018-06-01

    Community detection has long been a fascinating topic in complex networks since the community structure usually unveils valuable information of interest. The prevalence and evolution of deep learning and neural networks have been pushing forward the advancement in various research fields and also provide us numerous useful and off the shelf techniques. In this paper, we put the cascaded stacked autoencoders and the unsupervised extreme learning machine (ELM) together in a two-level embedding process and propose a novel community detection algorithm. Extensive comparison experiments in circumstances of both synthetic and real-world networks manifest the advantages of the proposed algorithm. On one hand, it outperforms the k-means clustering in terms of the accuracy and stability thus benefiting from the determinate dimensions of the ELM block and the integration of sparsity restrictions. On the other hand, it endures smaller complexity than the spectral clustering method on account of the shrinkage in time spent on the eigenvalue decomposition procedure.

  6. Efficiency disparities among community hospitals in Tennessee: do size, location, ownership, and network matter?

    Science.gov (United States)

    Roh, Chul-Young; Moon, M Jae; Jung, Kwangho

    2013-11-01

    This study examined the impact of ownership, size, location, and network on the relative technical efficiency of community hospitals in Tennessee for the 2002-2006 period, by applying data envelopment analysis (DEA) to measure technical efficiency (decomposed into scale efficiency and pure technical efficiency). Data envelopment analysis results indicate that medium-size hospitals (126-250 beds) are more efficient than their counterparts. Interestingly, public hospitals are significantly more efficient than private and nonprofit hospitals in Tennessee, and rural hospitals are more efficient than urban hospitals. This is the first study to investigate whether hospital networks with other health care providers affect hospital efficiency. Results indicate that community hospitals with networks are more efficient than non-network hospitals. From a management and policy perspective, this study suggests that public policies should induce hospitals to downsize or upsize into optional size, and private hospitals and nonprofit hospitals should change their organizational objectives from profit-driven to quality-driven.

  7. Complex networks from experimental horizontal oil–water flows: Community structure detection versus flow pattern discrimination

    International Nuclear Information System (INIS)

    Gao, Zhong-Ke; Fang, Peng-Cheng; Ding, Mei-Shuang; Yang, Dan; Jin, Ning-De

    2015-01-01

    We propose a complex network-based method to distinguish complex patterns arising from experimental horizontal oil–water two-phase flow. We first use the adaptive optimal kernel time–frequency representation (AOK TFR) to characterize flow pattern behaviors from the energy and frequency point of view. Then, we infer two-phase flow complex networks from experimental measurements and detect the community structures associated with flow patterns. The results suggest that the community detection in two-phase flow complex network allows objectively discriminating complex horizontal oil–water flow patterns, especially for the segregated and dispersed flow patterns, a task that existing method based on AOK TFR fails to work. - Highlights: • We combine time–frequency analysis and complex network to identify flow patterns. • We explore the transitional flow behaviors in terms of betweenness centrality. • Our analysis provides a novel way for recognizing complex flow patterns. • Broader applicability of our method is demonstrated and articulated

  8. Loneliness, social support networks, mood and wellbeing in community-dwelling elderly.

    Science.gov (United States)

    Golden, Jeannette; Conroy, Ronán M; Bruce, Irene; Denihan, Aisling; Greene, Elaine; Kirby, Michael; Lawlor, Brian A

    2009-07-01

    Both loneliness and social networks have been linked with mood and wellbeing. However, few studies have examined these factors simultaneously in community-dwelling participants. The aim of this study was to examine the relationship between social network, loneliness, depression, anxiety and quality of life in community dwelling older people living in Dublin. One thousand two hundred and ninety-nine people aged 65 and over, recruited through primary care practices, were interviewed in their own homes using the GMS-AGECAT. Social network was assessed using Wenger's typology. 35% of participants were lonely, with 9% describing it as painful and 6% as intrusive. Similarly, 34% had a non-integrated social network. However, the two constructs were distinct: 32% of participants with an integrated social network reported being lonely. Loneliness was higher in women, the widowed and those with physical disability and increased with age, but when age-related variables were controlled for this association was non-significant. Wellbeing, depressed mood and hopelessness were all independently associated with both loneliness and non-integrated social network. In particular, loneliness explained the excess risk of depression in the widowed. The population attributable risk (PAR) associated with loneliness was 61%, compared with 19% for non-integrated social network. Taken together they had a PAR of 70% Loneliness and social networks both independently affect mood and wellbeing in the elderly, underlying a very significant proportion of depressed mood.

  9. Community intervention to increase neighborhood social network among Japanese older adults.

    Science.gov (United States)

    Harada, Kazuhiro; Masumoto, Kouhei; Katagiri, Keiko; Fukuzawa, Ai; Chogahara, Makoto; Kondo, Narihiko; Okada, Shuichi

    2018-03-01

    Strengthening neighborhood social networks is important for promoting health among older adults. However, effective intervention strategies aimed at increasing older adults' social networks have not yet been established. The present study examined whether a university-led community intervention that provided communication opportunities could increase older Japanese adults' neighborhood social networks. The present study used a quasi-experimental design. Before the intervention, using postal mail, we carried out a baseline questionnaire survey that was sent to all people living in the Tsurukabuto community aged ≥60 years (n = 1769), of whom 1068 responded. For the community intervention, 18 event-based programs were provided over the course of 1 year at Kobe University. Academic staff at Kobe University organized all the programs. During the program, social interactions among participants were promoted. A follow-up survey was distributed to those who responded to the baseline survey, and 710 individuals answered the question about their participation in the intervention programs (138 respondents were participants, 572 were non-participants). The neighborhood social network was measured in both the baseline and follow-up surveys. Analysis of covariance showed that the changes in neighborhood social network among participants in the program was significantly higher than the changes among non-participants (P = 0.046) after adjusting for the baseline score of social network. The present study found that participants of the intervention expanded their neighborhood social network, but non-participants did not. This finding shows that community interventions using university resources could increase older adults' neighborhood social networks. Geriatr Gerontol Int 2018; 18: 462-469. © 2017 Japan Geriatrics Society.

  10. Evaluating Community-Academic Partnerships of the South Carolina Healthy Brain Research Network.

    Science.gov (United States)

    Soltani, Suzan Neda; Kannaley, Kristie; Tang, Weizhou; Gibson, Andrea; Olscamp, Kate; Friedman, Daniela B; Khan, Samira; Houston, Julie; Wilcox, Sara; Levkoff, Sue E; Hunter, Rebecca H

    2017-07-01

    Community-academic partnerships have a long history of support from public health researchers and practitioners as an effective way to advance research and solutions to issues that are of concern to communities and their citizens. Data on the development and evaluation of partnerships focused on healthy aging and cognitive health were limited. The purpose of this article is to examine how community partners view the benefits and barriers of a community-academic partner group established to support activities of the South Carolina Healthy Brain Research Network (SC-HBRN). The SC-HBRN is part of the national Healthy Brain Research Network, a thematic research network funded by the Centers for Disease Control and Prevention (CDC). It is focused on improving the scientific and research translation agenda on cognitive health and healthy aging. Semistructured interviews, conducted at end of Year 2 of the 5-year partnership, were used to collect data from partners of the SC-HBRN. Reported benefits of the partnership were information sharing and networking, reaching a broader audience, and humanizing research. When asked to describe what they perceived as barriers to the collaborative, partners described some lack of clarity regarding goals of the network and opportunities to contribute to the partnership. Study results can guide and strengthen other public health-focused partnerships.

  11. Sharing and community curation of mass spectrometry data with Global Natural Products Social Molecular Networking

    DEFF Research Database (Denmark)

    Wang, Mingxun; Carver, Jeremy J.; Pevzner, Pavel

    2016-01-01

    are well-suited to high-throughput characterization of NP, there is a pressing need for an infrastructure to enable sharing and curation of data. We present Global Natural Products Social Molecular Networking (GNPS; http://gnps.ucsd.edu), an open-access knowledge base for community-wide organization...... and sharing of raw, processed or identified tandem mass (MS/MS) spectrometry data. In GNPS, crowdsourced curation of freely available community-wide reference MS libraries will underpin improved annotations. Data-driven social-networking should facilitate identification of spectra and foster collaborations...

  12. A New Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Complex Networks

    Directory of Open Access Journals (Sweden)

    Guoqiang Chen

    2013-01-01

    Full Text Available Community detection in dynamic networks is an important research topic and has received an enormous amount of attention in recent years. Modularity is selected as a measure to quantify the quality of the community partition in previous detection methods. But, the modularity has been exposed to resolution limits. In this paper, we propose a novel multiobjective evolutionary algorithm for dynamic networks community detection based on the framework of nondominated sorting genetic algorithm. Modularity density which can address the limitations of modularity function is adopted to measure the snapshot cost, and normalized mutual information is selected to measure temporal cost, respectively. The characteristics knowledge of the problem is used in designing the genetic operators. Furthermore, a local search operator was designed, which can improve the effectiveness and efficiency of community detection. Experimental studies based on synthetic datasets show that the proposed algorithm can obtain better performance than the compared algorithms.

  13. Spectral methods for the detection of network community structure: a comparative analysis

    International Nuclear Information System (INIS)

    Shen, Hua-Wei; Cheng, Xue-Qi

    2010-01-01

    Spectral analysis has been successfully applied to the detection of community structure of networks, respectively being based on the adjacency matrix, the standard Laplacian matrix, the normalized Laplacian matrix, the modularity matrix, the correlation matrix and several other variants of these matrices. However, the comparison between these spectral methods is less reported. More importantly, it is still unclear which matrix is more appropriate for the detection of community structure. This paper answers the question by evaluating the effectiveness of these five matrices against benchmark networks with heterogeneous distributions of node degree and community size. Test results demonstrate that the normalized Laplacian matrix and the correlation matrix significantly outperform the other three matrices at identifying the community structure of networks. This indicates that it is crucial to take into account the heterogeneous distribution of node degree when using spectral analysis for the detection of community structure. In addition, to our surprise, the modularity matrix exhibits very similar performance to the adjacency matrix, which indicates that the modularity matrix does not gain benefits from using the configuration model as a reference network with the consideration of the node degree heterogeneity

  14. Energy Spectral Behaviors of Communication Networks of Open-Source Communities.

    Directory of Open Access Journals (Sweden)

    Jianmei Yang

    Full Text Available Large-scale online collaborative production activities in open-source communities must be accompanied by large-scale communication activities. Nowadays, the production activities of open-source communities, especially their communication activities, have been more and more concerned. Take CodePlex C # community for example, this paper constructs the complex network models of 12 periods of communication structures of the community based on real data; then discusses the basic concepts of quantum mapping of complex networks, and points out that the purpose of the mapping is to study the structures of complex networks according to the idea of quantum mechanism in studying the structures of large molecules; finally, according to this idea, analyzes and compares the fractal features of the spectra in different quantum mappings of the networks, and concludes that there are multiple self-similarity and criticality in the communication structures of the community. In addition, this paper discusses the insights and application conditions of different quantum mappings in revealing the characteristics of the structures. The proposed quantum mapping method can also be applied to the structural studies of other large-scale organizations.

  15. Investigating student communities with network analysis of interactions in a physics learning center

    Directory of Open Access Journals (Sweden)

    Eric Brewe

    2012-01-01

    Full Text Available Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

  16. Investigating student communities with network analysis of interactions in a physics learning center

    Science.gov (United States)

    Brewe, Eric; Kramer, Laird; Sawtelle, Vashti

    2012-06-01

    Developing a sense of community among students is one of the three pillars of an overall reform effort to increase participation in physics, and the sciences more broadly, at Florida International University. The emergence of a research and learning community, embedded within a course reform effort, has contributed to increased recruitment and retention of physics majors. We utilize social network analysis to quantify interactions in Florida International University’s Physics Learning Center (PLC) that support the development of academic and social integration. The tools of social network analysis allow us to visualize and quantify student interactions and characterize the roles of students within a social network. After providing a brief introduction to social network analysis, we use sequential multiple regression modeling to evaluate factors that contribute to participation in the learning community. Results of the sequential multiple regression indicate that the PLC learning community is an equitable environment as we find that gender and ethnicity are not significant predictors of participation in the PLC. We find that providing students space for collaboration provides a vital element in the formation of a supportive learning community.

  17. Epidemic spreading in weighted scale-free networks with community structure

    International Nuclear Information System (INIS)

    Chu, Xiangwei; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

    2009-01-01

    Many empirical studies reveal that the weights and community structure are ubiquitous in various natural and artificial networks. In this paper, based on the SI disease model, we investigate the epidemic spreading in weighted scale-free networks with community structure. Two exponents, α and β, are introduced to weight the internal edges and external edges, respectively; and a tunable probability parameter q is also introduced to adjust the strength of community structure. We find the external weighting exponent β plays a much more important role in slackening the epidemic spreading and reducing the danger brought by the epidemic than the internal weighting exponent α. Moreover, a novel result we find is that the strong community structure is no longer helpful for slackening the danger brought by the epidemic in the weighted cases. In addition, we show the hierarchical dynamics of the epidemic spreading in the weighted scale-free networks with communities which is also displayed in the famous BA scale-free networks

  18. Label Propagation with α-Degree Neighborhood Impact for Network Community Detection

    Directory of Open Access Journals (Sweden)

    Heli Sun

    2014-01-01

    Full Text Available Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach with α-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of its α-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on the α-degree neighborhood impact of all the nodes. The α-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scope α can be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.

  19. Discrete particle swarm optimization for identifying community structures in signed social networks.

    Science.gov (United States)

    Cai, Qing; Gong, Maoguo; Shen, Bo; Ma, Lijia; Jiao, Licheng

    2014-10-01

    Modern science of networks has facilitated us with enormous convenience to the understanding of complex systems. Community structure is believed to be one of the notable features of complex networks representing real complicated systems. Very often, uncovering community structures in networks can be regarded as an optimization problem, thus, many evolutionary algorithms based approaches have been put forward. Particle swarm optimization (PSO) is an artificial intelligent algorithm originated from social behavior such as birds flocking and fish schooling. PSO has been proved to be an effective optimization technique. However, PSO was originally designed for continuous optimization which confounds its applications to discrete contexts. In this paper, a novel discrete PSO algorithm is suggested for identifying community structures in signed networks. In the suggested method, particles' status has been redesigned in discrete form so as to make PSO proper for discrete scenarios, and particles' updating rules have been reformulated by making use of the topology of the signed network. Extensive experiments compared with three state-of-the-art approaches on both synthetic and real-world signed networks demonstrate that the proposed method is effective and promising. Copyright © 2014 Elsevier Ltd. All rights reserved.

  20. Community Broadband Networks and the Opportunity for E-Government Services

    DEFF Research Database (Denmark)

    Williams, Idongesit

    2017-01-01

    Community Broadband Networks (CBN) facilitate Broadband connectivity in underserved areas in many countries. The lack of Broadband connectivity is one of the reasons for the slow diffusion of e-government services in many countries.This article explains how CBNs can be enabled by governments...... to facilitate the delivery of e–government services in underserved areas in the developed and developing countries.The Community Based Broadband Mobilization (CBNM) models are used as explanatory tools....

  1. Social networks, social support and psychiatric symptoms: social determinants and associations within a multicultural community population.

    Science.gov (United States)

    Smyth, Natasha; Siriwardhana, Chesmal; Hotopf, Matthew; Hatch, Stephani L

    2015-07-01

    Little is known about how social networks and social support are distributed within diverse communities and how different types of each are associated with a range of psychiatric symptoms. This study aims to address such shortcomings by: (1) describing the demographic and socioeconomic characteristics of social networks and social support in a multicultural population and (2) examining how each is associated with multiple mental health outcomes. Data is drawn from the South East London Community Health Study; a cross-sectional study of 1,698 adults conducted between 2008 and 2010. The findings demonstrate variation in social networks and social support by socio-demographic factors. Ethnic minority groups reported larger family networks but less perceived instrumental support. Older individuals and migrant groups reported lower levels of particular network and support types. Individuals from lower socioeconomic groups tended to report less social networks and support across the indicators measured. Perceived emotional and instrumental support, family and friend network size emerged as protective factors for common mental disorder, personality dysfunction and psychotic experiences. In contrast, both social networks and social support appear less relevant for hazardous alcohol use. The findings both confirm established knowledge that social networks and social support exert differential effects on mental health and furthermore suggest that the particular type of social support may be important. In contrast, different types of social network appear to impact upon poor mental health in a more uniform way. Future psychosocial strategies promoting mental health should consider which social groups are vulnerable to reduced social networks and poor social support and which diagnostic groups may benefit most.

  2. Frameworks for Understanding the Nature of Interactions, Networking, and Community in a Social Networking Site for Academic Practice

    Directory of Open Access Journals (Sweden)

    Grainne Conole

    2011-03-01

    Full Text Available This paper describes a new social networking site, Cloudworks, which has been developed to enable discussion and sharing of learning and teaching ideas/designs and to promote reflective academic practice. The site aims to foster new forms of social and participatory practices (peer critiquing, sharing, user-generated content, aggregation, and personalisation within an educational context. One of the key challenges in the development of the site has been to understand the user interactions and the changing patterns of user behaviour as it evolves. The paper explores the extent to which four frameworks that have been used in researching networked learning contexts can provide insights into the patterns of user behaviour that we see in Cloudworks. The paper considers this within the current debate about the new types of interactions, networking, and community being observed as users adapt to and appropriate new technologies.

  3. Reconfiguration of Cortical Networks in MDD Uncovered by Multiscale Community Detection with fMRI.

    Science.gov (United States)

    He, Ye; Lim, Sol; Fortunato, Santo; Sporns, Olaf; Zhang, Lei; Qiu, Jiang; Xie, Peng; Zuo, Xi-Nian

    2018-04-01

    Major depressive disorder (MDD) is known to be associated with altered interactions between distributed brain regions. How these regional changes relate to the reorganization of cortical functional systems, and their modulation by antidepressant medication, is relatively unexplored. To identify changes in the community structure of cortical functional networks in MDD, we performed a multiscale community detection algorithm on resting-state functional connectivity networks of unmedicated MDD (uMDD) patients (n = 46), medicated MDD (mMDD) patients (n = 38), and healthy controls (n = 50), which yielded a spectrum of multiscale community partitions. we selected an optimal resolution level by identifying the most stable community partition for each group. uMDD and mMDD groups exhibited a similar reconfiguration of the community structure of the visual association and the default mode systems but showed different reconfiguration profiles in the frontoparietal control (FPC) subsystems. Furthermore, the central system (somatomotor/salience) and 3 frontoparietal subsystems showed strengthened connectivity with other communities in uMDD but, with the exception of 1 frontoparietal subsystem, returned to control levels in mMDD. These findings provide evidence for reconfiguration of specific cortical functional systems associated with MDD, as well as potential effects of medication in restoring disease-related network alterations, especially those of the FPC system.

  4. 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.

  5. Betweenness centrality and its applications from modeling traffic flows to network community detection

    Science.gov (United States)

    Ren, Yihui

    network and we demonstrate that the changes can propagate globally, affecting traffic several hundreds of miles away. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events. In the second part of the thesis we focus on network deconstruction and community detection problems, both intensely studied topics in network science, using a weighted betweenness centrality approach. We present an algorithm that solves both problems efficiently and accurately and demonstrate that on both benchmark networks and data networks.

  6. Convergent evolution of modularity in metabolic networks through different community structures

    Directory of Open Access Journals (Sweden)

    Zhou Wanding

    2012-09-01

    Full Text Available Abstract Background It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. Results In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxomony. Conclusions We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct enzymes in the organism’s metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability. Further, our results

  7. Convergent evolution of modularity in metabolic networks through different community structures.

    Science.gov (United States)

    Zhou, Wanding; Nakhleh, Luay

    2012-09-14

    It has been reported that the modularity of metabolic networks of bacteria is closely related to the variability of their living habitats. However, given the dependency of the modularity score on the community structure, it remains unknown whether organisms achieve certain modularity via similar or different community structures. In this work, we studied the relationship between similarities in modularity scores and similarities in community structures of the metabolic networks of 1021 species. Both similarities are then compared against the genetic distances. We revisited the association between modularity and variability of the microbial living environments and extended the analysis to other aspects of their life style such as temperature and oxygen requirements. We also tested both topological and biological intuition of the community structures identified and investigated the extent of their conservation with respect to the taxonomy. We find that similar modularities are realized by different community structures. We find that such convergent evolution of modularity is closely associated with the number of (distinct) enzymes in the organism's metabolome, a consequence of different life styles of the species. We find that the order of modularity is the same as the order of the number of the enzymes under the classification based on the temperature preference but not on the oxygen requirement. Besides, inspection of modularity-based communities reveals that these communities are graph-theoretically meaningful yet not reflective of specific biological functions. From an evolutionary perspective, we find that the community structures are conserved only at the level of kingdoms. Our results call for more investigation into the interplay between evolution and modularity: how evolution shapes modularity, and how modularity affects evolution (mainly in terms of fitness and evolvability). Further, our results call for exploring new measures of modularity and network

  8. Fiscal Equity of Teacher Salaries and Compensation in Oklahoma

    Science.gov (United States)

    Maiden, Jeffrey; Evans, Nancy O.

    2009-01-01

    This quantitative study investigated the degree to which financial resources supporting teachers was equitably distributed in Oklahoma. Teachers are an important resource and their importance is being increasingly emphasized as educators attempt to increase student achievement. Every student educated in Oklahoma should have an equal right to…

  9. Repeated and random components in Oklahoma's monthly precipitation record

    Science.gov (United States)

    Precipitation across Oklahoma exhibits a high degree of spatial and temporal variability and creates numerous water resources management challenges. The monthly precipitation record of the Central Oklahoma climate division was evaluated in a proof-of-concept to establish whether a simple monthly pre...

  10. The Oklahoma PN/ADN Articulation Project Report.

    Science.gov (United States)

    Oklahoma State Regents for Higher Education, Oklahoma City.

    In response to a critical nursing shortage in the state of Oklahoma, the Oklahoma Practical Nursing (PN)/Associate Degree Nursing (ADN) Articulation Project Coordinating Committee was formed in spring 1990 to develop a proposal for program articulation. A curriculum matrix was designed and adopted for use by five regional subcommittees which…

  11. 76 FR 42723 - Land Acquisitions; Osage Nation of Oklahoma

    Science.gov (United States)

    2011-07-19

    ..., Osage County, State of Oklahoma, According to the United States Government survey thereof, Less and... Oklahoma, according to the United States Government survey thereof, described as follows: Commencing at the...; Thence South and parallel to the West line of the SE/4 NE/4 a distance of 1319.11 feet to a point on the...

  12. Oklahoma Aerospace Intellectual Capital/Educational Recommendations: An Inquiry of Oklahoma Aerospace Executives

    Science.gov (United States)

    Nelson, Erin M.

    2010-01-01

    Scope and Method of Study: The purpose of this qualitative study was to conduct detailed personal interviews with aerospace industry executives/managers from both the private and military sectors from across Oklahoma to determine their perceptions of intellectual capital needs of the industry. Interviews with industry executives regarding…

  13. Critical Social Network Analysis in Community Colleges: Peer Effects and Credit Attainment

    Science.gov (United States)

    González Canché, Manuel S.; Rios-Aguilar, Cecilia

    2014-01-01

    This chapter discusses the importance of conducting critical social network analysis (CSNA) in higher education. To illustrate the benefits of CSNA, the authors use existing institutional data to examine peer effects in community colleges. The chapter ends with a discussion of the implications of using a CSNA approach to measure inequities in…

  14. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Directory of Open Access Journals (Sweden)

    Brian R Granger

    2016-04-01

    Full Text Available The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space, a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  15. Visualization of Metabolic Interaction Networks in Microbial Communities Using VisANT 5.0.

    Science.gov (United States)

    Granger, Brian R; Chang, Yi-Chien; Wang, Yan; DeLisi, Charles; Segrè, Daniel; Hu, Zhenjun

    2016-04-01

    The complexity of metabolic networks in microbial communities poses an unresolved visualization and interpretation challenge. We address this challenge in the newly expanded version of a software tool for the analysis of biological networks, VisANT 5.0. We focus in particular on facilitating the visual exploration of metabolic interaction between microbes in a community, e.g. as predicted by COMETS (Computation of Microbial Ecosystems in Time and Space), a dynamic stoichiometric modeling framework. Using VisANT's unique metagraph implementation, we show how one can use VisANT 5.0 to explore different time-dependent ecosystem-level metabolic networks. In particular, we analyze the metabolic interaction network between two bacteria previously shown to display an obligate cross-feeding interdependency. In addition, we illustrate how a putative minimal gut microbiome community could be represented in our framework, making it possible to highlight interactions across multiple coexisting species. We envisage that the "symbiotic layout" of VisANT can be employed as a general tool for the analysis of metabolism in complex microbial communities as well as heterogeneous human tissues. VisANT is freely available at: http://visant.bu.edu and COMETS at http://comets.bu.edu.

  16. Urban ecological stewardship: understanding the structure, function and network of community-based urban land management

    Science.gov (United States)

    Erika s. Svendsen; Lindsay K. Campbell

    2008-01-01

    Urban environmental stewardship activities are on the rise in cities throughout the Northeast. Groups participating in stewardship activities range in age, size, and geography and represent an increasingly complex and dynamic arrangement of civil society, government and business sectors. To better understand the structure, function and network of these community-based...

  17. Must Invisible Colleges Be Invisible? An Approach to Examining Large Communities of Network Users.

    Science.gov (United States)

    Ruth, Stephen R.; Gouet, Raul

    1993-01-01

    Discussion of characteristics of users of computer-mediated communication systems and scientific networks focuses on a study of the scientific community in Chile. Topics addressed include users and nonusers; productivity; educational level; academic specialty; age; gender; international connectivity; public policy issues; and future research…

  18. Community access networks: how to connect the next billion to the ...

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

    Community access networks: how to connect the next billion to the Internet ... services is a prerequisite to sustainable socio-economic development. ... It will provide case studies and formulate recommendations with respect to ... An IDRC delegation will join international delegates and city representatives at the ICLEI World ...

  19. Social Networking Technologies as Vehicles of Support for Women in Learning Communities

    Science.gov (United States)

    Burgess, Kimberly R.

    2009-01-01

    Women have long since used social networking as a means of coping with their struggles, educating and empowering themselves, engaging in broader social movements, and building international advocacy. Internet communities that are designed and facilitated to be inclusive of women's experiences can be important social spaces where women feel…

  20. Case Study IV: Carnegie Foundation for the Advancement of Teaching's Networked Improvement Communities (NICs)

    Science.gov (United States)

    Coburn, Cynthia E.; Penuel, William R.; Geil, Kimberly E.

    2015-01-01

    The Carnegie Foundation for the Advancement of Teaching is a nonprofit, operating foundation with a long tradition of developing and studying ways to improve teaching practice. For the past three years, the Carnegie Foundation has initiated three different Networked Improvement Communities (NICs). The first, Quantway, is addressing the high…

  1. Community access networks: how to connect the next billion to the ...

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

    Community access networks: how to connect the next billion to the Internet. Despite recent progress with mobile technology diffusion, more than four billion people worldwide are unconnected and have limited access to global communication infrastructure. The cost of implementing connectivity infrastructure in underserved ...

  2. Cocoon: A lightweight opportunistic networking middleware for community-oriented smart mobile applications

    NARCIS (Netherlands)

    Türkes, Okan; Scholten, Johan; Havinga, Paul J.M.

    2016-01-01

    Modern society is surrounded by an ample spectrum of smart mobile devices. This ubiquity forms a high potential for community-oriented opportunistic ad hoc networking applications. Nevertheless, today’s smart mobile devices such as smartphones, tablets, and wristbands are still onerous to

  3. How do we make community owned information networks work for the poor?

    CSIR Research Space (South Africa)

    Morris, CF

    2009-01-01

    Full Text Available asked in this paper is how do we make community owned information networks work for the poor? A case study from Angola shares key lessons learnt in developing shared cost models in telecentres in the face of exorbitantly high connectivity costs. The real...

  4. Social Embeddedness and Late-Life Parenthood : Community Activity, Close Ties, and Support Networks

    NARCIS (Netherlands)

    Wenger, G. Clare; Dykstra, Pearl A.; Melkas, Tuula; Knipscheer, Kees C.P.M.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people’s involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show

  5. Social embeddedness and late-life parenthood: community activity, close ties and support networks

    NARCIS (Netherlands)

    Wenger, G.; Dykstra, P.A.; Melkas, T.; Knipscheer, K.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people’s involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show

  6. Social embeddedness and late-life parenthood: Community activity, close ties, and support networks

    NARCIS (Netherlands)

    Wenger, G.C.; Dykstra, P.A.; Melkas, T.; Knipscheer, C.P.M.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people's involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show

  7. Social Embeddedness and Late-Life Parenthood: Community Activity, Close Ties, and Support Networks

    Science.gov (United States)

    Wenger, G. Clare; Dykstra, Pearl A.; Melkas, Tuula; Knipscheer, Kees C. P. M.

    2007-01-01

    This article focuses on the ways in which patterns of marriage and fertility shape older people's involvement in community groups and their support networks. The data are from Australia, Finland, Germany, Israel, Japan, the Netherlands, Spain, the United Kingdom, and the United States. Findings show that childless older adults, regardless of…

  8. Socio-semantic Networks of Research Publications in the Learning Analytics Community

    NARCIS (Netherlands)

    Fazeli, Soude; Drachsler, Hendrik; Sloep, Peter

    2013-01-01

    Fazeli, S., Drachsler, H., & Sloep, P. B. (2013). Socio-semantic Networks of Research Publications in the Learning Analytics Community. In M. d'Aquin, S. Dietze, H. Drachsler, E. Herder, & D. Taibi (Eds.), Linked data challenge, Learning Analytic and Knowledge (LAK13) (pp. 6-10). Vol. 974, Leuven,

  9. A game theoretic algorithm to detect overlapping community structure in networks

    Science.gov (United States)

    Zhou, Xu; Zhao, Xiaohui; Liu, Yanheng; Sun, Geng

    2018-04-01

    Community detection can be used as an important technique for product and personalized service recommendation. A game theory based approach to detect overlapping community structure is introduced in this paper. The process of the community formation is converted into a game, when all agents (nodes) cannot improve their own utility, the game process will be terminated. The utility function is composed of a gain and a loss function and we present a new gain function in this paper. In addition, different from choosing action randomly among join, quit and switch for each agent to get new label, two new strategies for each agent to update its label are designed during the game, and the strategies are also evaluated and compared for each agent in order to find its best result. The overlapping community structure is naturally presented when the stop criterion is satisfied. The experimental results demonstrate that the proposed algorithm outperforms other similar algorithms for detecting overlapping communities in networks.

  10. Building a sense of virtual community: the role of the features of social networking sites.

    Science.gov (United States)

    Chen, Chi-Wen; Lin, Chiun-Sin

    2014-07-01

    In recent years, social networking sites have received increased attention because of the potential of this medium to transform business by building virtual communities. However, theoretical and empirical studies investigating how specific features of social networking sites contribute to building a sense of virtual community (SOVC)-an important dimension of a successful virtual community-are rare. Furthermore, SOVC scales have been developed, and research on this issue has been called for, but few studies have heeded this call. On the basis of prior literature, this study proposes that perceptions of the three most salient features of social networking sites-system quality (SQ), information quality (IQ), and social information exchange (SIE)-play a key role in fostering SOVC. In particular, SQ is proposed to increase IQ and SIE, and SIE is proposed to enhance IQ, both of which thereafter build SOVC. The research model was examined in the context of Facebook, one of the most popular social networking sites in the world. We adopted Blanchard's scales to measure SOVC. Data gathered using a Web-based questionnaire, and analyzed with partial least squares, were utilized to test the model. The results demonstrate that SIE, SQ, and IQ are the factors that form SOVC. The findings also suggest that SQ plays a fundamental role in supporting SIE and IQ in social networking sites. Implications for theory, practice, and future research directions are discussed.

  11. Investigating the social configuration of a community to understand how networked learning activities take place: The OERu case study

    NARCIS (Netherlands)

    Schreurs, Bieke; Van den Beemt, Antoine; Prinsen, Fleur; De Laat, Maarten; Witthaus, Gaby; Conole, Grainne

    2015-01-01

    Examining how OER (Open Educational Resources) communities come to live, function or learn can support in empowering educators in the use of open educational resources. In this paper we investigate how an OER community functions through its networked learning activities. Networked learning

  12. Community-Based Social Networks: Generation of Power Law Degree Distribution and IP Solutions to the KPP

    Science.gov (United States)

    Wu, Wentao

    2012-01-01

    The objective of this thesis is two-fold: (1) to investigate the degree distribution property of community-based social networks (CSNs) and (2) to provide solutions to a pertinent problem, the Key Player Problem. In the first part of this thesis, we consider a growing community-based network in which the ability of nodes competing for links to new…

  13. Biological assessment of environmental flows for Oklahoma

    Science.gov (United States)

    Fisher, William L.; Seilheimer, Titus S.; Taylor, Jason M.

    2012-01-01

    Large-scale patterns in fish assemblage structure and functional groups are influenced by alterations in streamflow regime. In this study, we defined an objective threshold for alteration for Oklahoma streams using a combination of the expected range of 27 flow indices and a discriminant analysis to predict flow regime group. We found that fish functional groups in reference flow conditions had species that were more intolerant to flow alterations and preferences for stream habitat and faster flowing water. In contrast, altered sites had more tolerant species that preferred lentic habitat and slower water velocity. Ordination graphs of the presence and functional groups of species revealed an underlying geographical pattern roughly conforming to ecoregions, although there was separation between reference and altered sites within the larger geographical framework. Additionally, we found that reservoir construction and operation significantly altered fish assemblages in two different systems, Bird Creek in central Oklahoma and the Kiamichi River in southeastern Oklahoma. The Bird Creek flow regime shifted from a historically intermittent stream to one with stable perennial flows, and changes in fish assemblage structure covaried with changes in all five components of the flow regime. In contrast, the Kiamichi River flow regime did not change significantly for most flow components despite shifts in fish assemblage structure; however, most of the species associated with shifts in assemblage structure in the Kiamichi River system were characteristic of lentic environments and were likely related more to proximity of reservoirs in the drainage system than changes in flow. The spatial patterns in fish assemblage response to flow alteration, combined with different temporal responses of hydrology and fish assemblage structure at sites downstream of reservoirs, indicate that interactions between flow regime and aquatic biota vary depending on ecological setting. This

  14. 75 FR 65524 - United Auto Workers Local 1999, Oklahoma City, OK; Notice of Negative Determination Regarding...

    Science.gov (United States)

    2010-10-25

    ... DEPARTMENT OF LABOR Employment and Training Administration [TA-W-71,863] United Auto Workers Local... workers and former workers of United Auto Workers Local 1999, Oklahoma City, Oklahoma (the subject firm... Auto Workers Local 1999, Oklahoma City, Oklahoma, was based on the findings that the workers at the...

  15. The Oklahoma Geographic Information Retrieval System

    Science.gov (United States)

    Blanchard, W. A.

    1982-01-01

    The Oklahoma Geographic Information Retrieval System (OGIRS) is a highly interactive data entry, storage, manipulation, and display software system for use with geographically referenced data. Although originally developed for a project concerned with coal strip mine reclamation, OGIRS is capable of handling any geographically referenced data for a variety of natural resource management applications. A special effort has been made to integrate remotely sensed data into the information system. The timeliness and synoptic coverage of satellite data are particularly useful attributes for inclusion into the geographic information system.

  16. Understanding the structure of community collaboration: the case of one Canadian health promotion network.

    Science.gov (United States)

    Barnes, Martha; Maclean, Joanne; Cousens, Laura

    2010-06-01

    In 2004, over 6.8 million Canadians were considered overweight, with an additional 2.4 million labeled clinically obese. Due to these escalating levels of obesity in Canada, physical activity is being championed by politicians, physicians, educators and community members as a means to address this health crisis. In doing so, many organizations are being called upon to provide essential physical activity services and programs to combat rising obesity rates. Yet, strategies for achieving these organizations' mandates, which invariably involve stretching already scarce resources, are difficult to implement and sustain. One strategy for improving the health and physical activity levels of people in communities has been the creation of inter-organizational networks of service providers. Yet, little is known about whether networks are effective in addressing policy issues in non-clinical health settings. The purpose of this investigation was 2-fold; to use whole network analysis to determine the structure of one health promotion network in Canada, and to identify the types of ties shared by actors in the health network. Findings revealed a network wherein information sharing constituted the basis for collaboration, whereas efforts related to sharing resources, marketing and/or fundraising endeavors were less evident.

  17. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

    International Nuclear Information System (INIS)

    Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

    2015-01-01

    Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug–target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

  18. Common neighbours and the local-community-paradigm for topological link prediction in bipartite networks

    Science.gov (United States)

    Daminelli, Simone; Thomas, Josephine Maria; Durán, Claudio; Vittorio Cannistraci, Carlo

    2015-11-01

    Bipartite networks are powerful descriptions of complex systems characterized by two different classes of nodes and connections allowed only across but not within the two classes. Unveiling physical principles, building theories and suggesting physical models to predict bipartite links such as product-consumer connections in recommendation systems or drug-target interactions in molecular networks can provide priceless information to improve e-commerce or to accelerate pharmaceutical research. The prediction of nonobserved connections starting from those already present in the topology of a network is known as the link-prediction problem. It represents an important subject both in many-body interaction theory in physics and in new algorithms for applied tools in computer science. The rationale is that the existing connectivity structure of a network can suggest where new connections can appear with higher likelihood in an evolving network, or where nonobserved connections are missing in a partially known network. Surprisingly, current complex network theory presents a theoretical bottle-neck: a general framework for local-based link prediction directly in the bipartite domain is missing. Here, we overcome this theoretical obstacle and present a formal definition of common neighbour index and local-community-paradigm (LCP) for bipartite networks. As a consequence, we are able to introduce the first node-neighbourhood-based and LCP-based models for topological link prediction that utilize the bipartite domain. We performed link prediction evaluations in several networks of different size and of disparate origin, including technological, social and biological systems. Our models significantly improve topological prediction in many bipartite networks because they exploit local physical driving-forces that participate in the formation and organization of many real-world bipartite networks. Furthermore, we present a local-based formalism that allows to intuitively

  19. Ground-water quality assessment of the central Oklahoma Aquifer, Oklahoma; project description

    Science.gov (United States)

    Christenson, S.C.; Parkhurst, D.L.

    1987-01-01

    In April 1986, the U.S. Geological Survey began a pilot program to assess the quality of the Nation's surface-water and ground-water resources. The program, known as the National Water-Quality Assessment (NAWQA) program, is designed to acquire and interpret information about a variety of water-quality issues. The Central Oklahoma aquifer project is one of three ground-water pilot projects that have been started. The NAWQA program also incudes four surface-water pilot projects. The Central Oklahoma aquifer project, as part of the pilot NAWQA program, will develop and test methods for performing assessments of ground-water quality. The objectives of the Central Oklahoma aquifer assessment are: (1) To investigate regional ground-water quality throughout the aquifer in the manner consistent with the other pilot ground-water projects, emphasizing the occurrence and distribution of potentially toxic substances in ground water, including trace elements, organic compounds, and radioactive constituents; (2) to describe relations between ground-water quality, land use, hydrogeology, and other pertinent factors; and (3) to provide a general description of the location, nature, and possible causes of selected prevalent water-quality problems within the study unit; and (4) to describe the potential for water-quality degradation of ground-water zones within the study unit. The Central Oklahoma aquifer, which includes in descending order the Garber Sandstone and Wellington Formation, the Chase Group, the Council Grove Group, the Admire Group, and overlying alluvium and terrace deposits, underlies about 3,000 square miles of central Oklahoma and is used extensively for municipal, industrial, commercial, and domestic water supplies. The aquifer was selected for study by the NAWQA program because it is a major source for water supplies in central Oklahoma and because it has several known or suspected water-quality problems. Known problems include concentrations of arsenic, chromium

  20. Results of the Community Health Applied Research Network (CHARN) National Research Capacity Survey of Community Health Centers.

    Science.gov (United States)

    Song, Hui; Li, Vivian; Gillespie, Suzanne; Laws, Reesa; Massimino, Stefan; Nelson, Christine; Singal, Robbie; Wagaw, Fikirte; Jester, Michelle; Weir, Rosy Chang

    2015-01-01

    The mission of the Community Health Applied Research Network (CHARN) is to build capacity to carry out Patient-Centered Outcomes Research at community health centers (CHCs), with the ultimate goal to improve health care for vulnerable populations. The CHARN Needs Assessment Staff Survey investigates CHCs' involvement in research, as well as their need for research training and resources. Results will be used to guide future training. The survey was developed and implemented in partnership with CHARN CHCs. Data were collected across CHARN CHCs. Data analysis and reports were conducted by the CHARN data coordinating center (DCC). Survey results highlighted gaps in staff research training, and these gaps varied by staff role. There is considerable variation in research involvement, partnerships, and focus both within and across CHCs. Development of training programs to increase research capacity should be tailored to address the specific needs and roles of staff involved in research.

  1. Modeling community succession and assembly: A novel method for network evolution

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2012-06-01

    Full Text Available The process of modeling community succession and assembly is in some sense a method for network evolution, as done by Barabasi and Albert (1999. It is also one of the methods to create a sample networkfrom the statistic network I proposed earlier. I think that the mechanism of network evolution supposed by Barabasi and Albert is most likely applicable to the natural phenomena with emergency property. For natural phenomena without emergency property, the present study indicated that a scale-free network may be produced through a new mechanism, i.e., whether the connection of a taxon x occurs, dependent on the type and property of taxon y (in particular, the degree of its direct correlation with x to be connected but not necessarily the existing number of connections of taxon y, as proposed in present study.

  2. The Community Child Health Network Life Stress Interview: a brief chronic stress measure for community health research.

    Science.gov (United States)

    Tanner Stapleton, Lynlee R; Dunkel Schetter, Christine; Dooley, Larissa N; Guardino, Christine M; Huynh, Jan; Paek, Cynthia; Clark-Kauffman, Elizabeth; Schafer, Peter; Woolard, Richard; Lanzi, Robin Gaines

    2016-07-01

    Chronic stress is implicated in many theories as a contributor to a wide range of physical and mental health problems. The current study describes the development of a chronic stress measure that was based on the UCLA Life Stress Interview (LSI) and adapted in collaboration with community partners for use in a large community health study of low-income, ethnically diverse parents of infants in the USA (Community Child Health Network [CCHN]). We describe the instrument, its purpose and adaptations, implementation, and results of a reliability study in a subsample of the larger study cohort. Interviews with 272 mothers were included in the present study. Chronic stress was assessed using the CCHN LSI, an instrument designed for administration by trained community interviewers to assess four domains of chronic stress, each rated by interviewers. Significant correlations ranging from small to moderate in size between chronic stress scores on this measure, other measures of stress, biomarkers of allostatic load, and mental health provide initial evidence of construct and concurrent validity. Reliability data for interviewer ratings are also provided. This relatively brief interview (15 minutes) is available for use and may be a valuable tool for researchers seeking to measure chronic stress reliably and validly in future studies with time constraints.

  3. Connecting primary care clinics and community pharmacies through a nationwide electronic prescribing network: A qualitative study

    Directory of Open Access Journals (Sweden)

    Marie-Pierre Gagnon

    2015-10-01

    Full Text Available Background The use of medication is at the heart of primary care, but is also the cause for major health concerns. It is therefore important to examine the prescription of medication process.Objective This study identifies the barriers and facilitators perceived by community pharmacists and primary care physicians concerning the adoption of a nationwide electronic prescribing (e-prescribing network in the province of Quebec, Canada.Methods We used purposive sampling to identify the most intensive users of the e-prescribing network. We conducted phone and in-person interviews. Interviews were transcribed, and we analysed their content with NVivo, using the clinical adoption framework (CAF for the codification of the data.Results We interviewed 33 pharmacists, 2 pharmacy technicians, 11 physicians and 3 clinic managers. Adoption of the e-prescribing network was fairly low. The respondents underlined adaptation of their work environment, openness to change and perception of benefits as facilitators to the adoption of the network. However, important barriers were perceived, including system quality issues and paper prescriptions being the only legal document in the prescribing process. Even if respondents recognised that the e-prescribing network can offer substantial benefits to the prescribing process, issues still persisted and raised barriers to the full use of such a network, especially in a context where different local information systems are connected within a nationwide e-prescribing network.Conclusion This study, based on the CAF, provides a better understanding of the factors related to the adoption of a nationwide e-prescribing network connecting primary care clinics and community pharmacies. 

  4. Context-Aware Community Construction in Proximity-Based Mobile Networks

    Directory of Open Access Journals (Sweden)

    Na Yu

    2015-01-01

    Full Text Available Sensor-equipped mobile devices have allowed users to participate in various social networking services. We focus on proximity-based mobile social networking environments where users can share information obtained from different places via their mobile devices when they are in proximity. Since people are more likely to share information if they can benefit from the sharing or if they think the information is of interest to others, there might exist community structures where users who share information more often are grouped together. Communities in proximity-based mobile networks represent social groups where connections are built when people are in proximity. We consider information influence (i.e., specify who shares information with whom as the connection and the space and time related to the shared information as the contexts. To model the potential information influences, we construct an influence graph by integrating the space and time contexts into the proximity-based contacts of mobile users. Further, we propose a two-phase strategy to detect and track context-aware communities based on the influence graph and show how the context-aware community structure improves the performance of two types of mobile social applications.

  5. Detecting community structure using label propagation with consensus weight in complex network

    International Nuclear Information System (INIS)

    Liang Zong-Wen; Li Jian-Ping; Yang Fan; Petropulu Athina

    2014-01-01

    Community detection is a fundamental work to analyse the structural and functional properties of complex networks. The label propagation algorithm (LPA) is a near linear time algorithm to find a good community structure. Despite various subsequent advances, an important issue of this algorithm has not yet been properly addressed. Random update orders within the algorithm severely hamper the stability of the identified community structure. In this paper, we executed the basic label propagation algorithm on networks multiple times, to obtain a set of consensus partitions. Based on these consensus partitions, we created a consensus weighted graph. In this consensus weighted graph, the weight value of the edge was the proportion value that the number of node pairs allocated in the same cluster was divided by the total number of partitions. Then, we introduced consensus weight to indicate the direction of label propagation. In label update steps, by computing the mixing value of consensus weight and label frequency, a node adopted the label which has the maximum mixing value instead of the most frequent one. For extending to different networks, we introduced a proportion parameter to adjust the proportion of consensus weight and label frequency in computing mixing value. Finally, we proposed an approach named the label propagation algorithm with consensus weight (LPAcw), and the experimental results showed that the LPAcw could enhance considerably both the stability and the accuracy of community partitions. (interdisciplinary physics and related areas of science and technology)

  6. Topical Network of Breast Cancer Information in a Korean American Online Community: A Semantic Network Analysis

    Science.gov (United States)

    Park, Min Sook; Park, Hyejin

    2016-01-01

    Introduction: Health information-seeking and sharing online has become immensely intertwined with day-to-day information-seeking of US immigrants with health concerns. Despite the consistent recognition of unique health needs among different US immigrant communities, little is known about the distinctive patterns and extent of health information…

  7. Migrant Workers' Community in China: Relationships among Social Networks, Life Satisfaction and Political Participation

    Directory of Open Access Journals (Sweden)

    Qingwen Xu

    2011-11-01

    Full Text Available The millions of persons migrating from China´s rural areas to urban spaces have contributed greatly to the country´s decades-long economic growth, and the influx of migrants has changed the fabric of China´s urban social and economic life. These internal migrants, similar to many international immigrants, depend heavily on their social networks, which are often developed in their rural villages, for jobs, housing, financial assistance, and social support both during and after migration. Consequently, migrants´ networks function distinctly in well-being and behavior. Using data from the 2006 China General Social Survey, this article seeks to 1 investigate the existence of migrant sub-groups in China, 2 understand the characteristics of social networks among sub-groups, and 3 explore the relationships social networks hold to life satisfaction and political participation among China´s migrant population. This article asserts that China´s migrant population includes several sub-groups emerging on the basis of gender, education, age, and marital status, which in turn produce different patterns of ties and social interactions among their social networks. While this article finds very different employment patterns among migrant sub-groups, migrant networks do not appear to strongly influence perceptions and behaviors, such as life satisfaction and political participation. This article also argues that individual networks could facilitate the development of migrant communities in cities.

  8. Real-time community detection in full social networks on a laptop

    Science.gov (United States)

    Chamberlain, Benjamin Paul; Levy-Kramer, Josh; Humby, Clive

    2018-01-01

    For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide

  9. Real-time community detection in full social networks on a laptop.

    Directory of Open Access Journals (Sweden)

    Benjamin Paul Chamberlain

    Full Text Available For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph. As global social networks (e.g., Facebook and Twitter are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to

  10. Real-time community detection in full social networks on a laptop.

    Science.gov (United States)

    Chamberlain, Benjamin Paul; Levy-Kramer, Josh; Humby, Clive; Deisenroth, Marc Peter

    2018-01-01

    For a broad range of research and practical applications it is important to understand the allegiances, communities and structure of key players in society. One promising direction towards extracting this information is to exploit the rich relational data in digital social networks (the social graph). As global social networks (e.g., Facebook and Twitter) are very large, most approaches make use of distributed computing systems for this purpose. Distributing graph processing requires solving many difficult engineering problems, which has lead some researchers to look at single-machine solutions that are faster and easier to maintain. In this article, we present an approach for analyzing full social networks on a standard laptop, allowing for interactive exploration of the communities in the locality of a set of user specified query vertices. The key idea is that the aggregate actions of large numbers of users can be compressed into a data structure that encapsulates the edge weights between vertices in a derived graph. Local communities can be constructed by selecting vertices that are connected to the query vertices with high edge weights in the derived graph. This compression is robust to noise and allows for interactive queries of local communities in real-time, which we define to be less than the average human reaction time of 0.25s. We achieve single-machine real-time performance by compressing the neighborhood of each vertex using minhash signatures and facilitate rapid queries through Locality Sensitive Hashing. These techniques reduce query times from hours using industrial desktop machines operating on the full graph to milliseconds on standard laptops. Our method allows exploration of strongly associated regions (i.e., communities) of large graphs in real-time on a laptop. It has been deployed in software that is actively used by social network analysts and offers another channel for media owners to monetize their data, helping them to continue to provide

  11. Detecting Micro-seismicity and Long-duration Tremor-like Events from the Oklahoma Wavefield Experiment

    Science.gov (United States)

    Li, C.; Li, Z.; Peng, Z.; Zhang, C.; Nakata, N.

    2017-12-01

    Oklahoma has experienced abrupt increase of induced seismicity in the last decade. An important way to fully understand seismic activities in Oklahoma is to obtain more complete earthquake catalogs and detect different types of seismic events. The IRIS Community Wavefield Demonstration Experiment was deployed near Enid, Oklahoma in Summer of 2016. The dataset from this ultra-dense array provides an excellent opportunity for detecting microseismicity in that region with wavefield approaches. Here we examine continuous waveforms recorded by 3 seismic lines using local coherence for ultra-dense arrays (Li et al., 2017), which is a measure of cross-correlation of waveform at each station with its nearby stations. So far we have detected more than 5,000 events from 06/22/2016 to 07/20/2016, and majority of them are not listed on the regional catalog of Oklahoma or global catalogs, indicating that they are local events. We also identify 15-20 long-period long-duration events, some of them lasting for more than 500 s. Such events have been found at major plate-boundary faults (also known as deep tectonic tremor), as well as during hydraulic fracturing, slow-moving landslides and glaciers. Our next step is to locate these possible tremor-like events with their relative arrival times across the array and compare their occurrence times with solid-earth tides and injection histories to better understand their driving mechanisms.

  12. Livelihood diversification in tropical coastal communities: a network-based approach to analyzing 'livelihood landscapes'.

    Directory of Open Access Journals (Sweden)

    Joshua E Cinner

    Full Text Available BACKGROUND: Diverse livelihood portfolios are frequently viewed as a critical component of household economies in developing countries. Within the context of natural resources governance in particular, the capacity of individual households to engage in multiple occupations has been shown to influence important issues such as whether fishers would exit a declining fishery, how people react to policy, the types of resource management systems that may be applicable, and other decisions about natural resource use. METHODOLOGY/PRINCIPAL FINDINGS: This paper uses network analysis to provide a novel methodological framework for detailed systemic analysis of household livelihood portfolios. Paying particular attention to the role of natural resource-based occupations such as fisheries, we use network analyses to map occupations and their interrelationships- what we refer to as 'livelihood landscapes'. This network approach allows for the visualization of complex information about dependence on natural resources that can be aggregated at different scales. We then examine how the role of natural resource-based occupations changes along spectra of socioeconomic development and population density in 27 communities in 5 western Indian Ocean countries. Network statistics, including in- and out-degree centrality, the density of the network, and the level of network centralization are compared along a multivariate index of community-level socioeconomic development and a gradient of human population density. The combination of network analyses suggests an increase in household-level specialization with development for most occupational sectors, including fishing and farming, but that at the community-level, economies remained diversified. CONCLUSIONS/SIGNIFICANCE: The novel modeling approach introduced here provides for various types of livelihood portfolio analyses at different scales of social aggregation. Our livelihood landscapes approach provides insights

  13. The Quake-Catcher Network: Improving Earthquake Strong Motion Observations Through Community Engagement

    Science.gov (United States)

    Cochran, E. S.; Lawrence, J. F.; Christensen, C. M.; Chung, A. I.; Neighbors, C.; Saltzman, J.

    2010-12-01

    The Quake-Catcher Network (QCN) involves the community in strong motion data collection by utilizing volunteer computing techniques and low-cost MEMS accelerometers. Volunteer computing provides a mechanism to expand strong-motion seismology with minimal infrastructure costs, while promoting community participation in science. Micro-Electro-Mechanical Systems (MEMS) triaxial accelerometers can be attached to a desktop computer via USB and are internal to many laptops. Preliminary shake table tests show the MEMS accelerometers can record high-quality seismic data with instrument response similar to research-grade strong-motion sensors. QCN began distributing sensors and software to K-12 schools and the general public in April 2008 and has grown to roughly 1500 stations worldwide. We also recently tested whether sensors could be quickly deployed as part of a Rapid Aftershock Mobilization Program (RAMP) following the 2010 M8.8 Maule, Chile earthquake. Volunteers are recruited through media reports, web-based sensor request forms, as well as social networking sites. Using data collected to date, we examine whether a distributed sensing network can provide valuable seismic data for earthquake detection and characterization while promoting community participation in earthquake science. We utilize client-side triggering algorithms to determine when significant ground shaking occurs and this metadata is sent to the main QCN server. On average, trigger metadata are received within 1-10 seconds from the observation of a trigger; the larger data latencies are correlated with greater server-station distances. When triggers are detected, we determine if the triggers correlate to others in the network using spatial and temporal clustering of incoming trigger information. If a minimum number of triggers are detected then a QCN-event is declared and an initial earthquake location and magnitude is estimated. Initial analysis suggests that the estimated locations and magnitudes are

  14. A CLEAN Network Initiative - Accelerating Transition to Post Carbon and Resilient Communities through Education and Engagement

    Science.gov (United States)

    Ledley, T. S.; Niepold, F., III; Bozuwa, J.; Davis, A.; Fraser, J.; Kretser, J.; Poppleton, K. L. I.; Qusba, L.; Ruggiero, K.; Spitzer, W.; Stylinski, C.

    2016-12-01

    The Climate Literacy and Energy Awareness Network (CLEAN) was formed in 2008 to help climate and energy literacy stakeholders implement the Climate and Energy Literacy Essential Principles to enable effective and responsible decisions with regard to actions that may affect climate. The ongoing conversations of the CLEAN Network have cultivated a culture of shared resources and expertise and allowed for the development of collective impact strategies. However, it has become clear that to accelerate and scale change, effective mitigation, adaptation, and resilience strategies must be developed by a diverse network of stakeholders at the community level to deal with the local impacts of climate change and move toward decarbonized and resilient economies. A group of CLEAN Network members, experienced in establishing effective networks and representing mature climate change education programs, came together to discuss at the community level 1) how we can collectively enable larger scale efforts to 2) develop effective strategies, 3) identify gaps in the system that limit action, and 4) coordinate possible vectors for interceding to advance community level decisions related to climate. We will describe our Theory of Change, based on both the power of communities and increasing climate literacy as a key requirement for sustained progress on the crisis climate change presents. From our Theory of Change, we have begun to outline a national monitoring strategy that can provide communities a measured way to understand their local readiness to respond to the impacts of climate change and understand the magnitude of those impacts in relation to their political and ecological economies. The scale would help describe the robustness of their programs and partnerships to address those impacts, the political climate for working in advance of pending change, and the degree of citizen engagement in resilience planning and action. The goal is to provide a common tool equivalent to GDP

  15. Design to Thrive Creating Social Networks and Online Communities that Last

    CERN Document Server

    Howard, Tharon W

    2010-01-01

    Social networks and online communities are reshaping the way people communicate, both in their personal and professional lives. What makes some succeed and others fail? What draws a user in? What makes them join? What keeps them coming back? Entrepreneurs and businesses are turning to user experience practitioners to figure this out. Though they are well-equipped to evaluate and create a variety of interfaces, social networks require a different set of design principles and ways of thinking about the user in order to be successful. .. .. Design to Thrive presents tried and tested design method

  16. Combining Community Engagement and Scientific Approaches in Next-Generation Monitor Siting: The Case of the Imperial County Community Air Network

    Directory of Open Access Journals (Sweden)

    Michelle Wong

    2018-03-01

    Full Text Available Air pollution continues to be a global public health threat, and the expanding availability of small, low-cost air sensors has led to increased interest in both personal and crowd-sourced air monitoring. However, to date, few low-cost air monitoring networks have been developed with the scientific rigor or continuity needed to conduct public health surveillance and inform policy. In Imperial County, California, near the U.S./Mexico border, we used a collaborative, community-engaged process to develop a community air monitoring network that attains the scientific rigor required for research, while also achieving community priorities. By engaging community residents in the project design, monitor siting processes, data dissemination, and other key activities, the resulting air monitoring network data are relevant, trusted, understandable, and used by community residents. Integration of spatial analysis and air monitoring best practices into the network development process ensures that the data are reliable and appropriate for use in research activities. This combined approach results in a community air monitoring network that is better able to inform community residents, support research activities, guide public policy, and improve public health. Here we detail the monitor siting process and outline the advantages and challenges of this approach.

  17. Natural Resources Information System for the State of Oklahoma

    International Nuclear Information System (INIS)

    Mankin, C.J.

    1992-01-01

    The objective of this research program was to continue developing, editing, maintaining, utilizing and making publicly available the Natural Resources Information System (NRIS) for the State of Oklahoma. The Oklahoma Geological Survey, working with Geological Information Systems at the University of Oklahoma's Sarkeys Energy Center, undertook to construct this information system in response to the need for a computerized, centrally located library containing accurate, detailed information on the state's natural resources. Particular emphasis during this phase of development was placed on computerizing information related to the energy needs of the nation, specifically oil and gas

  18. Social and place-focused communities in location-based online social networks

    Science.gov (United States)

    Brown, Chloë; Nicosia, Vincenzo; Scellato, Salvatore; Noulas, Anastasios; Mascolo, Cecilia

    2013-06-01

    Thanks to widely available, cheap Internet access and the ubiquity of smartphones, millions of people around the world now use online location-based social networking services. Understanding the structural properties of these systems and their dependence upon users' habits and mobility has many potential applications, including resource recommendation and link prediction. Here, we construct and characterise social and place-focused graphs by using longitudinal information about declared social relationships and about users' visits to physical places collected from a popular online location-based social service. We show that although the social and place-focused graphs are constructed from the same data set, they have quite different structural properties. We find that the social and location-focused graphs have different global and meso-scale structure, and in particular that social and place-focused communities have negligible overlap. Consequently, group inference based on community detection performed on the social graph alone fails to isolate place-focused groups, even though these do exist in the network. By studying the evolution of tie structure within communities, we show that the time period over which location data are aggregated has a substantial impact on the stability of place-focused communities, and that information about place-based groups may be more useful for user-centric applications than that obtained from the analysis of social communities alone.

  19. An investigation into possibilities for implementation of a virtual community of practice delivered via a mobile social network for rural community media in the Eastern Cape, South Africa

    Directory of Open Access Journals (Sweden)

    Oliva Muwanga-Zake

    2017-03-01

    Full Text Available Background: The purpose of this article is to provide an overview of how a virtual community of practice can be delivered via a mobile social networking framework to support rural community media in the Eastern Cape Province of South Africa. Objectives: The article presents the results of a study conducted to ascertain the possibilities of utilising mobile social networking as a means to provide access to required information and knowledge to rural community media through creation of a virtual community of practice. Improving the operational effectiveness of rural community media as a component of the rural community communication process would serve to improve the entire rural community communication process as well, making them more effective tools for availing relevant news and information to rural communities and reflecting the realities of rural communities to their broader environment. Method: The study was conducted on rural community media small micro and medium enterprises (SMMEs in the Eastern Cape Province of South Africa. The study applied an interpretive research philosophy, qualitative research design and multiple–case study approach. Primary data were collected through semi-structured interviews supported by a questionnaire, with secondary data collected via literature review, observation and documentation analysis. Results: Findings were that rural community media do make use of social media and mobile devices in operating their business, require access to generic and domain specific support services and actively engage their peers and stakeholders in this respect, although no formalised structure existed. The authors’ recommendation is to create a formalised virtual community of practice through the establishment of a mobile social network. Conclusion: Because of the fact that rural community SMMEs already utilise mobile devices and social media to operate their businesses, development of a solution based on a mobile social

  20. Networking between community health programs: a case study outlining the effectiveness, barriers and enablers

    Directory of Open Access Journals (Sweden)

    Grills Nathan J

    2012-07-01

    Full Text Available Abstract Background In India, since the 1990s, there has been a burgeoning of NGOs involved in providing primary health care. This has resulted in a complex NGO-Government interface which is difficult for lone NGOs to navigate. The Uttarakhand Cluster, India, links such small community health programs together to build NGO capacity, increase visibility and better link to the government schemes and the formal healthcare system. This research, undertaken between 1998 and 2011, aims to examine barriers and facilitators to such linking, or clustering, and the effectiveness of this clustering approach. Methods Interviews, indicator surveys and participant observation were used to document the process and explore the enablers, the barriers and the effectiveness of networks improving community health. Results The analysis revealed that when activating, framing, mobilising and synthesizing the Uttarakhand Cluster, key brokers and network players were important in bridging between organisations. The ties (or relationships that held the cluster together included homophily around common faith, common friendships and geographical location and common mission. Self interest whereby members sought funds, visibility, credibility, increased capacity and access to trainings was also a commonly identified motivating factor for networking. Barriers to network synthesizing included lack of funding, poor communication, limited time and lack of human resources. Risk aversion and mistrust remained significant barriers to overcome for such a network. Conclusions In conclusion, specific enabling factors allowed the clustering approach to be effective at increasing access to resources, creating collaborative opportunities and increasing visibility, credibility and confidence of the cluster members. These findings add to knowledge regarding social network formation and collaboration, and such knowledge will assist in the conceptualisation, formation and success of

  1. Networking between community health programs: a case study outlining the effectiveness, barriers and enablers.

    Science.gov (United States)

    Grills, Nathan J; Robinson, Priscilla; Phillip, Maneesh

    2012-07-19

    In India, since the 1990s, there has been a burgeoning of NGOs involved in providing primary health care. This has resulted in a complex NGO-Government interface which is difficult for lone NGOs to navigate. The Uttarakhand Cluster, India, links such small community health programs together to build NGO capacity, increase visibility and better link to the government schemes and the formal healthcare system. This research, undertaken between 1998 and 2011, aims to examine barriers and facilitators to such linking, or clustering, and the effectiveness of this clustering approach. Interviews, indicator surveys and participant observation were used to document the process and explore the enablers, the barriers and the effectiveness of networks improving community health. The analysis revealed that when activating, framing, mobilising and synthesizing the Uttarakhand Cluster, key brokers and network players were important in bridging between organisations. The ties (or relationships) that held the cluster together included homophily around common faith, common friendships and geographical location and common mission. Self interest whereby members sought funds, visibility, credibility, increased capacity and access to trainings was also a commonly identified motivating factor for networking. Barriers to network synthesizing included lack of funding, poor communication, limited time and lack of human resources. Risk aversion and mistrust remained significant barriers to overcome for such a network. In conclusion, specific enabling factors allowed the clustering approach to be effective at increasing access to resources, creating collaborative opportunities and increasing visibility, credibility and confidence of the cluster members. These findings add to knowledge regarding social network formation and collaboration, and such knowledge will assist in the conceptualisation, formation and success of potential health networks in India and other developing world countries.

  2. Traveling salesman problems with PageRank Distance on complex networks reveal community structure

    Science.gov (United States)

    Jiang, Zhongzhou; Liu, Jing; Wang, Shuai

    2016-12-01

    In this paper, we propose a new algorithm for community detection problems (CDPs) based on traveling salesman problems (TSPs), labeled as TSP-CDA. Since TSPs need to find a tour with minimum cost, cities close to each other are usually clustered in the tour. This inspired us to model CDPs as TSPs by taking each vertex as a city. Then, in the final tour, the vertices in the same community tend to cluster together, and the community structure can be obtained by cutting the tour into a couple of paths. There are two challenges. The first is to define a suitable distance between each pair of vertices which can reflect the probability that they belong to the same community. The second is to design a suitable strategy to cut the final tour into paths which can form communities. In TSP-CDA, we deal with these two challenges by defining a PageRank Distance and an automatic threshold-based cutting strategy. The PageRank Distance is designed with the intrinsic properties of CDPs in mind, and can be calculated efficiently. In the experiments, benchmark networks with 1000-10,000 nodes and varying structures are used to test the performance of TSP-CDA. A comparison is also made between TSP-CDA and two well-established community detection algorithms. The results show that TSP-CDA can find accurate community structure efficiently and outperforms the two existing algorithms.

  3. Dam-breach analysis and flood-inundation mapping for selected dams in Oklahoma City, Oklahoma, and near Atoka, Oklahoma

    Science.gov (United States)

    Shivers, Molly J.; Smith, S. Jerrod; Grout, Trevor S.; Lewis, Jason M.

    2015-01-01

    Dams provide beneficial functions such as flood control, recreation, and storage of water supplies, but they also entail risk; dam breaches and resultant floods can cause substantial property damage and loss of life. The State of Oklahoma requires each owner of a high-hazard dam, which the Federal Emergency Management Agency defines as dams for which failure or improper operation probably will cause loss of human life, to develop an emergency action plan specific to that dam. Components of an emergency action plan are to simulate a flood resulting from a possible dam breach and map the resulting downstream flood-inundation areas. The resulting flood-inundation maps can provide valuable information to city officials, emergency managers, and local residents for planning an emergency response if a dam breach occurs.

  4. Construction of HMI Network System for Individualized Maternity Intervention Service against Birth Defects in Community

    Institute of Scientific and Technical Information of China (English)

    Xu-huai HU

    2007-01-01

    The paper expounds the community maternity service system against birth defects,from the viewpoint of individualized service in family planning. We have utilized modern information technology to develop health management information (HMI) network with individualized maternity, and to establish the community service system for intervention of birth defects. The service system applied the concept of modern health management information to implementing informational management for screening,treatment, following up, outcome monitoring, so as to provide a base for promotion of health, diagnosis, treatment as well as scientific research, with the prenatal screening of Down's syndrome as a model. The introduction to informational network during the processes of service has been carried out with regards to its composition, function and application, while introducing the effects of computerized case record individualized in prevention, management and research of Down's syndrome.

  5. It Takes More than a Village: Building a Network of Safety in Nepal's Mountain Communities.

    Science.gov (United States)

    Adams, Vincanne; Craig, Sienna; Samen, Arlene; Bhatta, Surya

    2016-12-01

    Purpose This report from the field details the ways that one small maternal child health NGO, which began its work in Tibet and now works in the mountain communities of Nepal, has established a model for integrated healthcare delivery and support it calls the "network of safety." Description It discusses some of the challenges faced both by the NGO and by the rural mountain communities with whom it partners, as well as with the government of Nepal. Conclusion This report describes and analyzes successful efforts to reduce maternal and infant mortality in a culturally astute, durable, and integrated way, as well as examples of innovation and success experienced by enacting the network of safety model.

  6. The Community Integration Questionnaire - Revised: Australian normative data and measurement of electronic social networking.

    Science.gov (United States)

    Callaway, Libby; Winkler, Dianne; Tippett, Alice; Herd, Natalie; Migliorini, Christine; Willer, Barry

    2016-06-01

    Consideration of the relationship between meaningful participation, health and wellbeing underpins occupational therapy intervention, and drives measurement of community integration following acquired brain injury (ABI). However, utility of community integration measures has been limited to date by lack of normative data against which to compare outcomes, and none examine the growing use of electronic social networking (ESN) for social participation. This research had four aims: (i) develop and pilot items assessing ESN to add to the Community Integration Questionnaire, producing the Community Integration Questionnaire-Revised (CIQ-R); (ii) examine factor structure of the CIQ-R; (iii) collect Australian CIQ-R normative data; and (iv) assess test-retest reliability of the revised measure. Australia. A convenience sample of adults without ABI (N = 124) was used to develop and pilot ESN items. A representative general population sample of adults without ABI aged 18-64 years (N = 1973) was recruited to gather normative CIQ-R data. Cross-sectional survey. Demographic items and the CIQ-R. The CIQ-R demonstrated acceptable psychometric properties, with minor modification to the original scoring based on the factor analyses provided. Large representative general population CIQ-R normative data have been established, detailing contribution of a range of independent demographic variables to community integration. The addition of electronic social networking items to the CIQ-R offers a contemporary method of assessing community integration following ABI. Normative CIQ-R data enhance the understanding of community integration in the general population, allowing occupational therapists and other clinicians to make more meaningful comparisons between groups. © 2016 Occupational Therapy Australia.

  7. Using Network Analysis to Characterize Biogeographic Data in a Community Archive

    Science.gov (United States)

    Wellman, T. P.; Bristol, S.

    2017-12-01

    Informative measures are needed to evaluate and compare data from multiple providers in a community-driven data archive. This study explores insights from network theory and other descriptive and inferential statistics to examine data content and application across an assemblage of publically available biogeographic data sets. The data are archived in ScienceBase, a collaborative catalog of scientific data supported by the U.S Geological Survey to enhance scientific inquiry and acuity. In gaining understanding through this investigation and other scientific venues our goal is to improve scientific insight and data use across a spectrum of scientific applications. Network analysis is a tool to reveal patterns of non-trivial topological features in the data that do not exhibit complete regularity or randomness. In this work, network analyses are used to explore shared events and dependencies between measures of data content and application derived from metadata and catalog information and measures relevant to biogeographic study. Descriptive statistical tools are used to explore relations between network analysis properties, while inferential statistics are used to evaluate the degree of confidence in these assessments. Network analyses have been used successfully in related fields to examine social awareness of scientific issues, taxonomic structures of biological organisms, and ecosystem resilience to environmental change. Use of network analysis also shows promising potential to identify relationships in biogeographic data that inform programmatic goals and scientific interests.

  8. Health system tests CRM data base. Community Health Network uses direct mail to boost physicians.

    Science.gov (United States)

    Botvin, Judith D

    2003-01-01

    A six-month pilot patient retention project for Community Health Network (CHN), Indianapolis, ran from July 2002 to January 2003. It was a direct mail campaign on behalf of some members of the group practices owned by CHN, designed to test the use of the system's CRM database. Patients of the physicians received personal, dynamically-generated cards reminding them to schedule appointments and tests. Each mailing cost $1.76, including production and mailing.

  9. Purpose-Driven Communities in Multiplex Networks: Thresholding User-Engaged Layer Aggregation

    Science.gov (United States)

    2016-06-01

    18 Figure 2.5 Understanding modularity using different community partitions of a network: (a) Optimal M = 0.41 (b) Suboptimal M = 0.22 (c...Pagnani for the thesis writing workshops and reviewing my writing . Her guidance helped me to become a better writer. The writing process is...background, job title, or history of employment. Which arrangement is correct? All arrangements are technically correct, but one relationship or set

  10. Healthcare Supported by Data Mule Networks in Remote Communities of the Amazon Region

    OpenAIRE

    Coutinho, Mauro Margalho; Efrat, Alon; Johnson, Thienne; Richa, Andrea; Liu, Mengxue

    2014-01-01

    This paper investigates the feasibility of using boats as data mule nodes, carrying medical ultrasound videos from remote and isolated communities in the Amazon region in Brazil, to the main city of that area. The videos will be used by physicians to perform remote analysis and follow-up routine of prenatal examinations of pregnant women. Two open source simulators (the ONE and NS-2) were used to evaluate the results obtained utilizing a CoDPON (continuous displacement plan oriented network)....

  11. Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks

    Science.gov (United States)

    Amin, Hayder; Maccione, Alessandro; Nieus, Thierry

    2017-01-01

    Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs), interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities) that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity. PMID:28749937

  12. Network approach for local and community governance of energy: The case of Oxfordshire

    International Nuclear Information System (INIS)

    Parag, Yael; Hamilton, Jo; White, Vicki; Hogan, Bernie

    2013-01-01

    One of the many barriers to the incorporation of local and community actors in emerging energy governance structures and policy delivery mechanisms is the lack of thorough understanding of how they work in practice, and how best to support and develop effective local energy governance. Taking a meso-level perspective and a network approach to governance, this paper sheds some new light on this issue, by focusing on the relation, channels of communication and interactions between low carbon community groups (LCCGs) and other actors. Based on data gathered from LCCGs in Oxfordshire, UK, via network survey and interviews the research maps the relations in terms of the exchanges of information and financial support, and presents a relation-based structure of local energy governance. Analysis reveals the intensity of energy related information exchanges that is taking place at the county level and highlights the centrality of intermediary organization in facilitating information flow. The analysis also identifies actors that are not very dominant in their amount of exchanges, but fill ‘weak-tie’ functions between otherwise disconnected LCCGs or other actors in the network. As an analytical tool the analysis could be useful for various state and non-state actors that want to better understand and support – financially and otherwise – actors that enable energy related local action. - Highlights: • We used social network analysis to examine local and community governance of energy. • We examined information and financial support flow within the network. • Analysis highlights central and peripheral actors in the local governance structure. • The findings highlight the central role intermediary organizations have in local governance structures

  13. Recurrently connected and localized neuronal communities initiate coordinated spontaneous activity in neuronal networks.

    Directory of Open Access Journals (Sweden)

    Davide Lonardoni

    2017-07-01

    Full Text Available Developing neuronal systems intrinsically generate coordinated spontaneous activity that propagates by involving a large number of synchronously firing neurons. In vivo, waves of spikes transiently characterize the activity of developing brain circuits and are fundamental for activity-dependent circuit formation. In vitro, coordinated spontaneous spiking activity, or network bursts (NBs, interleaved within periods of asynchronous spikes emerge during the development of 2D and 3D neuronal cultures. Several studies have investigated this type of activity and its dynamics, but how a neuronal system generates these coordinated events remains unclear. Here, we investigate at a cellular level the generation of network bursts in spontaneously active neuronal cultures by exploiting high-resolution multielectrode array recordings and computational network modelling. Our analysis reveals that NBs are generated in specialized regions of the network (functional neuronal communities that feature neuronal links with high cross-correlation peak values, sub-millisecond lags and that share very similar structural connectivity motifs providing recurrent interactions. We show that the particular properties of these local structures enable locally amplifying spontaneous asynchronous spikes and that this mechanism can lead to the initiation of NBs. Through the analysis of simulated and experimental data, we also show that AMPA currents drive the coordinated activity, while NMDA and GABA currents are only involved in shaping the dynamics of NBs. Overall, our results suggest that the presence of functional neuronal communities with recurrent local connections allows a neuronal system to generate spontaneous coordinated spiking activity events. As suggested by the rules used for implementing our computational model, such functional communities might naturally emerge during network development by following simple constraints on distance-based connectivity.

  14. Community-directed mass drug administration is undermined by status seeking in friendship networks and inadequate trust in health advice networks

    NARCIS (Netherlands)

    Chami, Goylette F.; Kontoleon, Andreas A.; Bulte, Erwin; Fenwick, Alan; Kabatereine, Narcis B.; Tukahebwa, Edridah M.; Dunne, David W.

    2017-01-01

    Over 1.9 billion individuals require preventive chemotherapy through mass drug administration (MDA). Community-directed MDA relies on volunteer community medicine distributors (CMDs) and their achievement of high coverage and compliance. Yet, it is unknown if village social networks influence

  15. Community-directed mass drug administration is undermined by status seeking in friendship networks and inadequate trust in health advice networks

    NARCIS (Netherlands)

    Chami, Goylette F.; Kontoleon, Andreas A.; Bulte, Erwin; Fenwick, Alan; Kabatereine, Narcis B.; Tukahebwa, Edridah M.; Dunne, David W.

    2017-01-01

    Over 1.9 billion individuals require preventive chemotherapy through mass drug administration (MDA). Community-directed MDA relies on volunteer community medicine distributors (CMDs) and their achievement of high coverage and compliance. Yet, it is unknown if village social networks influence

  16. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications.

    Science.gov (United States)

    Karyotis, Vasileios; Tsitseklis, Konstantinos; Sotiropoulos, Konstantinos; Papavassiliou, Symeon

    2018-04-15

    In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan-Newman (GN) community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT) testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

  17. Big Data Clustering via Community Detection and Hyperbolic Network Embedding in IoT Applications

    Directory of Open Access Journals (Sweden)

    Vasileios Karyotis

    2018-04-01

    Full Text Available In this paper, we present a novel data clustering framework for big sensory data produced by IoT applications. Based on a network representation of the relations among multi-dimensional data, data clustering is mapped to node clustering over the produced data graphs. To address the potential very large scale of such datasets/graphs that test the limits of state-of-the-art approaches, we map the problem of data clustering to a community detection one over the corresponding data graphs. Specifically, we propose a novel computational approach for enhancing the traditional Girvan–Newman (GN community detection algorithm via hyperbolic network embedding. The data dependency graph is embedded in the hyperbolic space via Rigel embedding, allowing more efficient computation of edge-betweenness centrality needed in the GN algorithm. This allows for more efficient clustering of the nodes of the data graph in terms of modularity, without sacrificing considerable accuracy. In order to study the operation of our approach with respect to enhancing GN community detection, we employ various representative types of artificial complex networks, such as scale-free, small-world and random geometric topologies, and frequently-employed benchmark datasets for demonstrating its efficacy in terms of data clustering via community detection. Furthermore, we provide a proof-of-concept evaluation by applying the proposed framework over multi-dimensional datasets obtained from an operational smart-city/building IoT infrastructure provided by the Federated Interoperable Semantic IoT/cloud Testbeds and Applications (FIESTA-IoT testbed federation. It is shown that the proposed framework can be indeed used for community detection/data clustering and exploited in various other IoT applications, such as performing more energy-efficient smart-city/building sensing.

  18. A Community-based Partnership for a Sustainable GNSS Geodetic Network

    Science.gov (United States)

    Dokka, R. K.

    2009-12-01

    Geodetic networks offer unparalleled opportunities to monitor and understand many of the rhythms of the Earth most vital to the sustainability of modern and future societies, i.e., crustal motions, sea-level, and the weather. For crustal deformation studies, the advantage is clear. Modern measurements allow us to document not only the permanent strains incurred over a seismic cycle, for example, but also the ephemeral strains that are critical for understanding the underlying physical mechanism. To be effective for science, however, geodetic networks must be properly designed, capitalized, and maintained over sufficient time intervals to fully capture the processes in action. Unfortunately, most networks lack interoperability and lack a business plan to ensure long term sustainability. The USA, for example, lacks a unified nation-wide GNSS network that can sustain its self over the coming years, decades, and century. Current federal priorities do not yet envision such a singular network. Publicly and privately funded regional networks exist, but tend to be parochial in scope, and optimized for a special user community, e.g., surveying, crustal motions, etc. Data sharing is common, but the lack of input at the beginning limits the functionality of the system for non-primary users. Funding for private networks depend heavily on the user demand, business cycle, and regulatory requirements. Agencies funding science networks offer no guarantee of sustained support. An alternative model (GULFNet) developed in Louisiana is meeting user needs, is sustainable, and is helping engineers, surveyors, and geologists become more spatially enabled. The common denominator among all participants is the view that accurate, precise, and timely geodetic data have tangible value for all segments of society. Although operated by a university (LSU), GULFNet is a community-based partnership between public and private sectors. GULFNet simultaneously achieves scientific goals by providing

  19. Building international collaborative capacity: contributions of community psychologists to a European network.

    Science.gov (United States)

    García-Ramírez, Manuel; Paloma, Virginia; Suarez-Balcazar, Yolanda; Balcazar, Fabricio

    2009-09-01

    Europe is in the process of building a more participative, just, and inclusive European Union. The European Social Fund, which is an initiative developed to actively promote multinational partnerships that address pressing social issues, is a good example of the European transformation. This article describes the steps taken to develop and evaluate the activities of an international network promoting collaborative capacity among regional partners involved in the prevention of labor discrimination toward immigrants in three European countries-Spain, Belgium, and Italy. An international team of community psychologists proposed an empowering approach to assess the collaborative capacity of the network. This approach consisted of three steps: (1) establishing a collaborative relationship among partners, (2) building collaborative capacity, and (3) evaluating the collaborative capacity of the network. We conclude with lessons learned from the process and provide recommendations for addressing the challenges inherent in international collaboration processes.

  20. Networks and learning: communities, practices and the metaphor of networks–a response

    Directory of Open Access Journals (Sweden)

    Chris Jones

    2004-12-01

    Full Text Available I am pleased to have the opportunity to react to Bruce Ingraham's response to my article ‘Networks and learning: communities, practices and the metaphor of networks' (Jones, 2004. It is rare to have a dialogue with someone who has taken the time and trouble to consider what you have written for a journal. All too often reviewing is a one-way process with the reviewer remaining anonymous. It is all the more pleasant to have a response to what you have written that gets to grips with some of the issues that the author also finds troubling. It is in that spirit that I write this reaction to Ingraham; it is an opportunity for me to develop some of the points he has identified as problematic in the original article. I want to concentrate on two main issues, firstly the network metaphor itself and secondly the usefulness of abstraction and representations of various types.

  1. Enhanced Detectability of Community Structure in Multilayer Networks through Layer Aggregation.

    Science.gov (United States)

    Taylor, Dane; Shai, Saray; Stanley, Natalie; Mucha, Peter J

    2016-06-03

    Many systems are naturally represented by a multilayer network in which edges exist in multiple layers that encode different, but potentially related, types of interactions, and it is important to understand limitations on the detectability of community structure in these networks. Using random matrix theory, we analyze detectability limitations for multilayer (specifically, multiplex) stochastic block models (SBMs) in which L layers are derived from a common SBM. We study the effect of layer aggregation on detectability for several aggregation methods, including summation of the layers' adjacency matrices for which we show the detectability limit vanishes as O(L^{-1/2}) with increasing number of layers, L. Importantly, we find a similar scaling behavior when the summation is thresholded at an optimal value, providing insight into the common-but not well understood-practice of thresholding pairwise-interaction data to obtain sparse network representations.

  2. Invaders of pollination networks in the Galápagos Islands: emergence of novel communities

    DEFF Research Database (Denmark)

    Travset, A.; Heleno, R.; Chamorro, S.

    2013-01-01

    The unique biodiversity of most oceanic archipelagos is currently threatened by the introduction of alien species that can displace native biota, disrupt native ecological interactions, and profoundly affect community structure and stability. We investigated the threat of aliens on pollination...... networks in the species-rich lowlands of five Gala´pagos Islands. Twenty per cent of all species (60 plants and 220 pollinators) in the pooled network were aliens, being involved in 38 per cent of the interactions. Most aliens were insects, especially dipterans (36%), hymenopterans (30%) and lepidopterans...... (14%). These alien insects had more links than either endemic pollinators or non-endemic natives, some even acting as island hubs. Aliens linked mostly to generalized species, increasing nestedness and thus network stability. Moreover, they infiltrated all seven connected modules (determined...

  3. Awareness campaign. Orthopedic Hospital of Oklahoma launches awareness campaign.

    Science.gov (United States)

    2007-01-01

    The Orthopedic Hospital of Oklahoma is a 25-bed inpatient and outpatient center with one focus: Orthopedics. To acquaint people with its services and build brand awareness to drive market share, the hospital launched a print campaign featuring actual patients.

  4. A protorothyridid captorhinomorph reptile from the Lower Permian of Oklahoma

    National Research Council Canada - National Science Library

    Reisz, Robert R

    1980-01-01

    A new primitive captorhinomorph reptile has been found near Fort Sill, Oklahoma, in fissure fill deposits believed to be contemporaneous with the lower part of the Arroyo Formation, Clear Fork Group (Leonardian...

  5. Building Capacity for Earthquake Monitoring: Linking Regional Networks with the Global Community

    Science.gov (United States)

    Willemann, R. J.; Lerner-Lam, A.

    2006-12-01

    Installing or upgrading a seismic monitoring network is often among the mitigation efforts after earthquake disasters, and this is happening in response to the events both in Sumatra during December 2004 and in Pakistan during October 2005. These networks can yield improved hazard assessment, more resilient buildings where they are most needed, and emergency relief directed more quickly to the worst hit areas after the next large earthquake. Several commercial organizations are well prepared for the fleeting opportunity to provide the instruments that comprise a seismic network, including sensors, data loggers, telemetry stations, and the computers and software required for the network center. But seismic monitoring requires more than hardware and software, no matter how advanced. A well-trained staff is required to select appropriate and mutually compatible components, install and maintain telemetered stations, manage and archive data, and perform the analyses that actually yield the intended benefits. Monitoring is more effective when network operators cooperate with a larger community through free and open exchange of data, sharing information about working practices, and international collaboration in research. As an academic consortium, a facility operator and a founding member of the International Federation of Digital Seismographic Networks, IRIS has access to a broad range of expertise with the skills that are required to help design, install, and operate a seismic network and earthquake analysis center, and stimulate the core training for the professional teams required to establish and maintain these facilities. But delivering expertise quickly when and where it is unexpectedly in demand requires advance planning and coordination in order to respond to the needs of organizations that are building a seismic network, either with tight time constraints imposed by the budget cycles of aid agencies following a disastrous earthquake, or as part of more informed

  6. Partnership effectiveness in primary community care networks: A national empirical analysis of partners' coordination infrastructure designs.

    Science.gov (United States)

    Lin, Blossom Yen-Ju; Lin, Yung-Kai; Lin, Cheng-Chieh

    2010-01-01

    Previous empirical and managerial studies have ignored the effectiveness of integrated health networks. It has been argued that the varying definitions and strategic imperatives of integrated organizations may have complicated the assessment of the outcomes/performance of varying models, particularly when their market structures and contexts differed. This study aimed to empirically verify a theoretical perspective on the coordination infrastructure designs and the effectiveness of the primary community care networks (PCCNs) formed and funded by the Bureau of National Health Insurance since March 2003. The PCCNs present a model to replace the traditional fragmented providers in Taiwan's health care. The study used a cross-sectional mailed survey designed to ascertain partnership coordination infrastructure and integration of governance, clinical care, bonding, finances, and information. The outcome indicators were PCCNs' perceived performance and willingness to remain within the network. Structural equation modeling examined the causal relationships, controlling for organizational and environmental factors. Primary data collection occurred from February through December 2005, via structured questionnaires sent to 172 PCCNs. Using the individual PCCN as the unit of analysis, the results found that a network's efforts regarding coordination infrastructures were positively related to the PCCN's perceived performance and willingness to remain within the network. In addition, PCCNs practicing in rural areas and in areas with higher density of medical resources had better perceived effectiveness and willingness to cooperate in the network.Practical Implication: The lack of both an operational definition and an information about system-wide integration may have obstructed understanding of integrated health networks' organizational dynamics. This study empirically examined individual PCCNs and offers new insights on how to improve networks' organizational design and

  7. The Relationship Between Low-Frequency Motions and Community Structure of Residue Network in Protein Molecules.

    Science.gov (United States)

    Sun, Weitao

    2018-01-01

    The global shape of a protein molecule is believed to be dominant in determining low-frequency deformational motions. However, how structure dynamics relies on residue interactions remains largely unknown. The global residue community structure and the local residue interactions are two important coexisting factors imposing significant effects on low-frequency normal modes. In this work, an algorithm for community structure partition is proposed by integrating Miyazawa-Jernigan empirical potential energy as edge weight. A sensitivity parameter is defined to measure the effect of local residue interaction on low-frequency movement. We show that community structure is a more fundamental feature of residue contact networks. Moreover, we surprisingly find that low-frequency normal mode eigenvectors are sensitive to some local critical residue interaction pairs (CRIPs). A fair amount of CRIPs act as bridges and hold distributed structure components into a unified tertiary structure by bonding nearby communities. Community structure analysis and CRIP detection of 116 catalytic proteins reveal that breaking up of a CRIP can cause low-frequency allosteric movement of a residue at the far side of protein structure. The results imply that community structure and CRIP may be the structural basis for low-frequency motions.

  8. Brand community integration and customer satisfaction of social media network sites among students

    Directory of Open Access Journals (Sweden)

    Hayford Amegbe

    2017-11-01

    Full Text Available The aim of the study was to examine how consumers integrate into brand communities on social media network sites (SNSs and how it affects overall satisfaction of social media sites users among students. The study depends on the service-dominant logic (SDL to develop the constructs for hypotheses testing. The study used a cross-sectional survey research design. The data were col-lected using a web-based survey of university of Nairobi Students. In all, a total of 608 students participated in the survey. The data was analyzed using structural equation modeling with AMOS software. The results revealed that frequency of usage of SNSs and duration of usage positively affect the self –perceived relevance of SNSs. Also, the self-perceived relevance leads to building brand community which finally leads to customer satisfaction. The research was limited to only students of Nairobi and selecting students in itself, which is a limitation as well as limiting it to uni-versity of Nairobi. The younger or the millennial are not the only users of SNSs. We have older generations as well, who also use SNSs for various activities such as professional development among others. Understanding why consumers of social media network site would integrate brand community is seminal for both local and foreign firms doing business in a developing country. This would enable marketing practitioners to craft marketing strategies best for community brand build-ing.

  9. Weaving networks of responsibility: community work in development programs in rural Malawi.

    Science.gov (United States)

    Rosenthal, Anat

    2012-01-01

    The need to cope with the impact of the AIDS epidemic on communities in Africa has resulted in the emergence of numerous community health and development programs. Initiated by governments, international nongovernmental organizations (NGOs), and local organizations, such programs target local communities with the goal of building care and support mechanisms in the local level. Based on ethnographic field research in rural Malawi, and drawing from the cross-disciplinary debate on development work, the article explores the work of an NGO offering health and care programs to orphans and vulnerable children. Through analyzing the organization's scope of work, the article demonstrates how the NGO acts to structure local social networks as instruments of care and offers a new reading of the role of NGOs in which the limitations of development work and the work of NGOs are understood within their local context and not only in the context of broad cultural critique.

  10. LP-LPA: A link influence-based label propagation algorithm for discovering community structures in networks

    Science.gov (United States)

    Berahmand, Kamal; Bouyer, Asgarali

    2018-03-01

    Community detection is an essential approach for analyzing the structural and functional properties of complex networks. Although many community detection algorithms have been recently presented, most of them are weak and limited in different ways. Label Propagation Algorithm (LPA) is a well-known and efficient community detection technique which is characterized by the merits of nearly-linear running time and easy implementation. However, LPA has some significant problems such as instability, randomness, and monster community detection. In this paper, an algorithm, namely node’s label influence policy for label propagation algorithm (LP-LPA) was proposed for detecting efficient community structures. LP-LPA measures link strength value for edges and nodes’ label influence value for nodes in a new label propagation strategy with preference on link strength and for initial nodes selection, avoid of random behavior in tiebreak states, and efficient updating order and rule update. These procedures can sort out the randomness issue in an original LPA and stabilize the discovered communities in all runs of the same network. Experiments on synthetic networks and a wide range of real-world social networks indicated that the proposed method achieves significant accuracy and high stability. Indeed, it can obviously solve monster community problem with regard to detecting communities in networks.

  11. 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.

  12. From link-prediction in brain connectomes and protein interactomes to the local-community-paradigm in complex networks.

    KAUST Repository

    Cannistraci, C.V.

    2013-04-08

    Growth and remodelling impact the network topology of complex systems, yet a general theory explaining how new links arise between existing nodes has been lacking, and little is known about the topological properties that facilitate link-prediction. Here we investigate the extent to which the connectivity evolution of a network might be predicted by mere topological features. We show how a link/community-based strategy triggers substantial prediction improvements because it accounts for the singular topology of several real networks organised in multiple local communities - a tendency here named local-community-paradigm (LCP). We observe that LCP networks are mainly formed by weak interactions and characterise heterogeneous and dynamic systems that use self-organisation as a major adaptation strategy. These systems seem designed for global delivery of information and processing via multiple local modules. Conversely, non-LCP networks have steady architectures formed by strong interactions, and seem designed for systems in which information/energy storage is crucial.

  13. Biological signatures of dynamic river networks from a coupled landscape evolution and neutral community model

    Science.gov (United States)

    Stokes, M.; Perron, J. T.

    2017-12-01

    Freshwater systems host exceptionally species-rich communities whose spatial structure is dictated by the topology of the river networks they inhabit. Over geologic time, river networks are dynamic; drainage basins shrink and grow, and river capture establishes new connections between previously separated regions. It has been hypothesized that these changes in river network structure influence the evolution of life by exchanging and isolating species, perhaps boosting biodiversity in the process. However, no general model exists to predict the evolutionary consequences of landscape change. We couple a neutral community model of freshwater organisms to a landscape evolution model in which the river network undergoes drainage divide migration and repeated river capture. Neutral community models are macro-ecological models that include stochastic speciation and dispersal to produce realistic patterns of biodiversity. We explore the consequences of three modes of speciation - point mutation, time-protracted, and vicariant (geographic) speciation - by tracking patterns of diversity in time and comparing the final result to an equilibrium solution of the neutral model on the final landscape. Under point mutation, a simple model of stochastic and instantaneous speciation, the results are identical to the equilibrium solution and indicate the dominance of the species-area relationship in forming patterns of diversity. The number of species in a basin is proportional to its area, and regional species richness reaches its maximum when drainage area is evenly distributed among sub-basins. Time-protracted speciation is also modeled as a stochastic process, but in order to produce more realistic rates of diversification, speciation is not assumed to be instantaneous. Rather, each new species must persist for a certain amount of time before it is considered to be established. When vicariance (geographic speciation) is included, there is a transient signature of increased

  14. Looking at learning communities with the appropriate glasses: hints and ideas from network sciences

    Directory of Open Access Journals (Sweden)

    Fabio Nascimbeni

    2013-02-01

    Full Text Available 0 0 1 229 1263 USAL 10 2 1490 14.0 Normal 0 21 false false false ES JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Tabla normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin-top:0cm; mso-para-margin-right:0cm; mso-para-margin-bottom:10.0pt; mso-para-margin-left:0cm; line-height:115%; mso-pagination:widow-orphan; font-size:11.0pt; font-family:Calibri; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-ansi-language:ES; mso-fareast-language:EN-US;} The level of network thinking within education – intended as the capacity to look at learning systems and communities by focussing on the relations among the involved actors (primarily teachers and learners and not only on the actors characteristics – is growing, with different speeds depending on the educational sector, but not at the pace needed to keep up with the increasingly network nature of our societies. We claim that educational research and practices should increase their capacity to look at learning communities through appropriate “networking-sensitive” glasses, and get equipped with tools and methods – such as Social network Analysis - to properly understand and support these networks. The application of Social Network Analysis to education, especially in the case of distance learning, can allow understanding the patterns of interactions between teachers and learners, and can facilitate the consolidation of new approaches to understand collaboration mechanisms. The paper presents and discusses - from a learning viewpoint - a brief overview of the main theoretical and practical contributions coming from Social Network Analysis – such as the “random graphs”, the “small-worlds” or the “weak-ties” theories – together with some general

  15. Understanding the social and community support networks of American Indian women cancer survivors.

    Science.gov (United States)

    Burnette, Catherine E; Liddell, Jessica; Roh, Soonhee; Lee, Yeon-Shim; Lee, Hee Yun

    2018-04-02

    Cancer is the leading cause of death among American Indian and Alaska Native (AI/AN) women, and although cancer disparities among AI women are alarming, there is little research focused on the topic of social support and cancer treatment and outcomes. A community advisory board was used to develop and administer the project, and a qualitative descriptive study methodology was used. This research was conducted in partnership with two community-based hospitals in the Northern Plains. The sample included 43 AI female cancer survivors who were interviewed with a semi-structured interview guide. The data were analyzed using content analysis. Emergent themes revealed that AI cancer survivors' non-familial support systems included friends (n = 12), support groups (n = 6), churches (n = 10), co-workers (n = 5), communities (n = 4), support from health practitioners (n = 3) and additional forms of support. Results indicate that survivors' networks are diverse, and support broad prevention programs that reach out to churches, community groups, and online forums. These sources of supports can be enhanced through sustainable community-based infrastructures.

  16. Community Structure Analysis of Gene Interaction Networks in Duchenne Muscular Dystrophy.

    Directory of Open Access Journals (Sweden)

    Tejaswini Narayanan

    Full Text Available Duchenne Muscular Dystrophy (DMD is an important pathology associated with the human skeletal muscle and has been studied extensively. Gene expression measurements on skeletal muscle of patients afflicted with DMD provides the opportunity to understand the underlying mechanisms that lead to the pathology. Community structure analysis is a useful computational technique for understanding and modeling genetic interaction networks. In this paper, we leverage this technique in combination with gene expression measurements from normal and DMD patient skeletal muscle tissue to study the structure of genetic interactions in the context of DMD. We define a novel framework for transforming a raw dataset of gene expression measurements into an interaction network, and subsequently apply algorithms for community structure analysis for the extraction of topological communities. The emergent communities are analyzed from a biological standpoint in terms of their constituent biological pathways, and an interpretation that draws correlations between functional and structural organization of the genetic interactions is presented. We also compare these communities and associated functions in pathology against those in normal human skeletal muscle. In particular, differential enhancements are observed in the following pathways between pathological and normal cases: Metabolic, Focal adhesion, Regulation of actin cytoskeleton and Cell adhesion, and implication of these mechanisms are supported by prior work. Furthermore, our study also includes a gene-level analysis to identify genes that are involved in the coupling between the pathways of interest. We believe that our results serve to highlight important distinguishing features in the structural/functional organization of constituent biological pathways, as it relates to normal and DMD cases, and provide the mechanistic basis for further biological investigations into specific pathways differently regulated

  17. Back propagation artificial neural network for community Alzheimer's disease screening in China.

    Science.gov (United States)

    Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

    2013-01-25

    Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868-0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community.

  18. Back propagation artificial neural network for community Alzheimer's disease screening in China★

    Science.gov (United States)

    Tang, Jun; Wu, Lei; Huang, Helang; Feng, Jiang; Yuan, Yefeng; Zhou, Yueping; Huang, Peng; Xu, Yan; Yu, Chao

    2013-01-01

    Alzheimer's disease patients diagnosed with the Chinese Classification of Mental Disorders diagnostic criteria were selected from the community through on-site sampling. Levels of macro and trace elements were measured in blood samples using an atomic absorption method, and neurotransmitters were measured using a radioimmunoassay method. SPSS 13.0 was used to establish a database, and a back propagation artificial neural network for Alzheimer's disease prediction was simulated using Clementine 12.0 software. With scores of activities of daily living, creatinine, 5-hydroxytryptamine, age, dopamine and aluminum as input variables, the results revealed that the area under the curve in our back propagation artificial neural network was 0.929 (95% confidence interval: 0.868–0.968), sensitivity was 90.00%, specificity was 95.00%, and accuracy was 92.50%. The findings indicated that the results of back propagation artificial neural network established based on the above six variables were satisfactory for screening and diagnosis of Alzheimer's disease in patients selected from the community. PMID:25206598

  19. US LHCNet: Transatlantic Networking for the LHC and the U.S. HEP Community

    Energy Technology Data Exchange (ETDEWEB)

    Newman, Harvey B; Barczyk, Artur J

    2013-04-05

    US LHCNet provides the transatlantic connectivity between the Tier1 computing facilities at the Fermilab and Brookhaven National Labs and the Tier0 and Tier1 facilities at CERN, as well as Tier1s elsewhere in Europe and Asia. Together with ESnet, Internet2, and other R&E Networks participating in the LHCONE initiative, US LHCNet also supports transatlantic connections between the Tier2 centers (where most of the data analysis is taking place) and the Tier1s as needed. Given the key roles of the US and European Tier1 centers as well as Tier2 centers on both continents, the largest data flows are across the Atlantic, where US LHCNet has the major role. US LHCNet manages and operates the transatlantic network infrastructure including four Points of Presence (PoPs) and currently six transatlantic OC-192 (10Gbps) leased links. Operating at the optical layer, the network provides a highly resilient fabric for data movement, with a target service availability level in excess of 99.95%. This level of resilience and seamless operation is achieved through careful design including path diversity on both submarine and terrestrial segments, use of carrier-grade equipment with built-in high-availability and redundancy features, deployment of robust failover mechanisms based on SONET protection schemes, as well as the design of facility-diverse paths between the LHC computing sites. The US LHCNet network provides services at Layer 1(optical), Layer 2 (Ethernet) and Layer 3 (IPv4 and IPv6). The flexible design of the network, including modular equipment, a talented and agile team, and flexible circuit lease management, allows US LHCNet to react quickly to changing requirements form the LHC community. Network capacity is provisioned just-in-time to meet the needs, as demonstrated in the past years during the changing LHC start-up plans.

  20. Disaster preparedness networks in rural Midwest communities: Organizational roles, collaborations, and support for older residents.

    Science.gov (United States)

    Ashida, Sato; Zhu, Xi; Robinson, Erin L; Schroer, Audrey

    2018-05-17

    This study investigated the roles and interconnections among community organizations belonging to local disaster coalitions in Midwest in supporting older residents. Representatives from 44 organizations participated in one-time survey. Most were non-profit (68%) or federal/state/local government agencies (23%). The analyses of 761 relationships showed stronger collaborations in assessment (average strength=2.88 on a 5-point scale), emergency response (2.72), and planning (2.61); and weaker collaborations in co-sponsoring programs (1.71) and supporting older residents (2.03). The extent of collaboration (network density) to support older adults was also low. Coalitions may enhance network density and centralization by developing sub-committee structure and strengthening existing collaborations.

  1. Social Network Community Detection for DMA Creation: Criteria Analysis through Multilevel Optimization

    Directory of Open Access Journals (Sweden)

    Bruno M. Brentan

    2017-01-01

    Full Text Available Management of large water distribution systems can be improved by dividing their networks into so-called district metered areas (DMAs. However, such divisions must be based on appropriated technical criteria. Considering the importance of deeply understanding the relationship between DMA creation and these criteria, this work proposes a performance analysis of DMA generation that takes into account such indicators as resilience index, demand similarity, pressure uniformity, water age (and thus water quality, solution implantation costs, and electrical consumption. To cope with the complexity of the problem, suitable mathematical techniques are proposed in this paper. We use a social community detection technique to define the sectors, and then a multilevel particle swarm optimization approach is applied to find the optimal placement and operating point of the necessary devices. The results obtained by implementing the methodology in a real water supply network show its validity and the meaningful influence on the final result of, especially, elevation and pipe length.

  2. A social network analysis of Twitter: Mapping the digital humanities community

    Directory of Open Access Journals (Sweden)

    Martin Grandjean

    2016-12-01

    Full Text Available Defining digital humanities might be an endless debate if we stick to the discussion about the boundaries of this concept as an academic “discipline”. In an attempt to concretely identify this field and its actors, this paper shows that it is possible to analyse them through Twitter, a social media widely used by this “community of practice”. Based on a network analysis of 2,500 users identified as members of this movement, the visualisation of the “who’s following who?” graph allows us to highlight the structure of the network’s relationships, and identify users whose position is particular. Specifically, we show that linguistic groups are key factors to explain clustering within a network whose characteristics look similar to a small world.

  3. Novel approaches to develop community-built biological network models for potential drug discovery.

    Science.gov (United States)

    Talikka, Marja; Bukharov, Natalia; Hayes, William S; Hofmann-Apitius, Martin; Alexopoulos, Leonidas; Peitsch, Manuel C; Hoeng, Julia

    2017-08-01

    Hundreds of thousands of data points are now routinely generated in clinical trials by molecular profiling and NGS technologies. A true translation of this data into knowledge is not possible without analysis and interpretation in a well-defined biology context. Currently, there are many public and commercial pathway tools and network models that can facilitate such analysis. At the same time, insights and knowledge that can be gained is highly dependent on the underlying biological content of these resources. Crowdsourcing can be employed to guarantee the accuracy and transparency of the biological content underlining the tools used to interpret rich molecular data. Areas covered: In this review, the authors describe crowdsourcing in drug discovery. The focal point is the efforts that have successfully used the crowdsourcing approach to verify and augment pathway tools and biological network models. Technologies that enable the building of biological networks with the community are also described. Expert opinion: A crowd of experts can be leveraged for the entire development process of biological network models, from ontologies to the evaluation of their mechanistic completeness. The ultimate goal is to facilitate biomarker discovery and personalized medicine by mechanistically explaining patients' differences with respect to disease prevention, diagnosis, and therapy outcome.

  4. 77 FR 15273 - Oklahoma: Final Authorization of State Hazardous Waste Management Program Revision

    Science.gov (United States)

    2012-03-15

    ...: Final Authorization of State Hazardous Waste Management Program Revision AGENCY: Environmental... hazardous waste management program. We authorized the following revisions: Oklahoma received authorization... its program revision in accordance with 40 CFR 271.21. The Oklahoma Hazardous Waste Management Act...

  5. Cost-Effectiveness Analysis of the Residential Provisions of the 2015 IECC for Oklahoma

    Energy Technology Data Exchange (ETDEWEB)

    Mendon, Vrushali V. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Zhao, Mingjie [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Taylor, Zachary T. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States); Poehlman, Eric A. [Pacific Northwest National Lab. (PNNL), Richland, WA (United States)

    2016-02-15

    The 2015 IECC provides cost-effective savings for residential buildings in Oklahoma. Moving to the 2015 IECC from the 2009 IECC base code is cost-effective for residential buildings in all climate zones in Oklahoma.

  6. Exponential cluster synchronization in directed community networks via adaptive nonperiodically intermittent pinning control

    Science.gov (United States)

    Zhou, Peipei; Cai, Shuiming; Jiang, Shengqin; Liu, Zengrong

    2018-02-01

    In this paper, the problem of exponential cluster synchronization for a class of directed community networks is investigated via adaptive nonperiodically intermittent pinning control. By constructing a novel piecewise continuous Lyapunov function, some sufficient conditions to guarantee globally exponential cluster synchronization are derived. It is noted that the derived cluster synchronization criteria rely on the control rates, but not the control widths or the control periods, which facilitates the choice of the control periods in practical applications. A numerical example is finally presented to show the effectiveness of the obtained theoretical results.

  7. Virtual Community, social network and media environment of Canary Isands regional digital newspapers

    Directory of Open Access Journals (Sweden)

    Dr. Francisco Manuel Mateos Rodríguez

    2008-01-01

    Full Text Available The impact of the new communication and information technologies has favoured the creation of multiple local newspaper websites in the Canary Islands, thus making the regional press emerge as an alternative on the rise. This tendency affects significantly both traditional and new editions of the different regional and local newspapers from the Canaries and motivates a different distribution, positioning and development within the local media environment in which these media share a novel dimension of communication with a specific virtual community and social network within the World Wide Web.

  8. The Worldviews Network: Innovative Strategies for Increasing Climate and Ecological Literacy in Your Community

    Science.gov (United States)

    Connolly, R.; Yu, K.; McConville, D.; Sickler, J.; "Irving, Lindsay", L. S.; Gardiner, N.; Hamilton, H.

    2011-12-01

    Informal science Institutions (ISI) are in the unique position to convene and support community dialogues surrounding local ecological impacts of global change. The Worldviews Network-a collaboration between museums, scientists, and community-based organizations-is developing and testing innovative approaches for promoting and encouraging ecological literacy with the American public. In this session, we will share strategies for sparking and sustaining dialogue and action in local communities through high-impact visual presentations and real-world examples of successful projects that are increasing the healthy functioning of regional and global ecosystems. Educating the public about interconnected global change issues can be a daunting task. ISIs can help communities by facilitating dialogues about realistic and regionally relevant approaches for systemically addressing global challenges. Managing the complexity of these challenges requires going far beyond the standard prescriptions for behavior change; it requires inspiring participants with positive examples of system-wide solutions as well as actively involving the audience in scientifically informed design processes. This session will demonstrate how you can implement and sustain these community dialogues, using real-world examples from our partners' national events. We present visualization story templates and a model for facilitating dialogues that can be adapted at your institution. Based on video and written assessment feedback from visitors of our first Worldviews events, we will present initial evaluation findings about the impact that these strategies are having on our audiences and ISI partners. These findings show that engaging the public and NGO partners in sustainability and design dialogues is a powerful way to maintain the relevance of ISIs within their communities.

  9. Dynamics of bacterial communities before and after distribution in a full-scale drinking water network.

    Science.gov (United States)

    El-Chakhtoura, Joline; Prest, Emmanuelle; Saikaly, Pascal; van Loosdrecht, Mark; Hammes, Frederik; Vrouwenvelder, Hans

    2015-05-01

    Understanding the biological stability of drinking water distribution systems is imperative in the framework of process control and risk management. The objective of this research was to examine the dynamics of the bacterial community during drinking water distribution at high temporal resolution. Water samples (156 in total) were collected over short time-scales (minutes/hours/days) from the outlet of a treatment plant and a location in its corresponding distribution network. The drinking water is treated by biofiltration and disinfectant residuals are absent during distribution. The community was analyzed by 16S rRNA gene pyrosequencing and flow cytometry as well as conventional, culture-based methods. Despite a random dramatic event (detected with pyrosequencing and flow cytometry but not with plate counts), the bacterial community profile at the two locations did not vary significantly over time. A diverse core microbiome was shared between the two locations (58-65% of the taxa and 86-91% of the sequences) and found to be dependent on the treatment strategy. The bacterial community structure changed during distribution, with greater richness detected in the network and phyla such as Acidobacteria and Gemmatimonadetes becoming abundant. The rare taxa displayed the highest dynamicity, causing the major change during water distribution. This change did not have hygienic implications and is contingent on the sensitivity of the applied methods. The concept of biological stability therefore needs to be revised. Biostability is generally desired in drinking water guidelines but may be difficult to achieve in large-scale complex distribution systems that are inherently dynamic. Copyright © 2015 Elsevier Ltd. All rights reserved.

  10. Dynamics of bacterial communities before and after distribution in a full-scale drinking water network

    KAUST Repository

    El Chakhtoura, Joline

    2015-05-01

    Understanding the biological stability of drinking water distribution systems is imperative in the framework of process control and risk management. The objective of this research was to examine the dynamics of the bacterial community during drinking water distribution at high temporal resolution. Water samples (156 in total) were collected over short time-scales (minutes/hours/days) from the outlet of a treatment plant and a location in its corresponding distribution network. The drinking water is treated by biofiltration and disinfectant residuals are absent during distribution. The community was analyzed by 16S rRNA gene pyrosequencing and flow cytometry as well as conventional, culture-based methods. Despite a random dramatic event (detected with pyrosequencing and flow cytometry but not with plate counts), the bacterial community profile at the two locations did not vary significantly over time. A diverse core microbiome was shared between the two locations (58-65% of the taxa and 86-91% of the sequences) and found to be dependent on the treatment strategy. The bacterial community structure changed during distribution, with greater richness detected in the network and phyla such as Acidobacteria and Gemmatimonadetes becoming abundant. The rare taxa displayed the highest dynamicity, causing the major change during water distribution. This change did not have hygienic implications and is contingent on the sensitivity of the applied methods. The concept of biological stability therefore needs to be revised. Biostability is generally desired in drinking water guidelines but may be difficult to achieve in large-scale complex distribution systems that are inherently dynamic.

  11. Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils

    Science.gov (United States)

    Álvarez-Yela, Astrid Catalina; Gómez-Cano, Fabio; Zambrano, María Mercedes; Husserl, Johana; Danies, Giovanna; Restrepo, Silvia; González-Barrios, Andrés Fernando

    2017-01-01

    Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA) were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community. PMID:28767679

  12. The Community-based Organizations Working Group of the Space Science Education Support Network

    Science.gov (United States)

    Lutz, J. H.; Lowes, L. L.; Asplund, S.

    2004-12-01

    The NASA Space Science Support Network Community-based Organizations Working Group (CBOWG) has been working for the past two years on issues surrounding afterschool programs and programs for youth (e.g., Girl Scouts, Boy Scouts, Boys and Girls Clubs, 4-H, summer camps, afterschool and weekend programs for various ages, programs with emphases on minority youth). In this session the co-leaders of the CBOWG will discuss the challenges of working with community-based organizations on a regional or national level. We will highlight some ties that we have forged with the National Institute for Out of School Time (NIOST) and the National Afterschool Association (NAA). We will also talk about efforts to coordinate how various entities within NASA cooperate with community-based organizations to serve the best interests of these groups. We will give a couple of examples of how NASA space science organizations have partnered with community-based organizations. The session will include some handouts of information and resources that the CBOWG has found useful in developing an understanding of this segment of informal education groups. We would like to thank NASA for providing resources to support the work of the CBOWG.

  13. Compartmentalized metabolic network reconstruction of microbial communities to determine the effect of agricultural intervention on soils.

    Directory of Open Access Journals (Sweden)

    María Camila Alvarez-Silva

    Full Text Available Soil microbial communities are responsible for a wide range of ecological processes and have an important economic impact in agriculture. Determining the metabolic processes performed by microbial communities is crucial for understanding and managing ecosystem properties. Metagenomic approaches allow the elucidation of the main metabolic processes that determine the performance of microbial communities under different environmental conditions and perturbations. Here we present the first compartmentalized metabolic reconstruction at a metagenomics scale of a microbial ecosystem. This systematic approach conceives a meta-organism without boundaries between individual organisms and allows the in silico evaluation of the effect of agricultural intervention on soils at a metagenomics level. To characterize the microbial ecosystems, topological properties, taxonomic and metabolic profiles, as well as a Flux Balance Analysis (FBA were considered. Furthermore, topological and optimization algorithms were implemented to carry out the curation of the models, to ensure the continuity of the fluxes between the metabolic pathways, and to confirm the metabolite exchange between subcellular compartments. The proposed models provide specific information about ecosystems that are generally overlooked in non-compartmentalized or non-curated networks, like the influence of transport reactions in the metabolic processes, especially the important effect on mitochondrial processes, as well as provide more accurate results of the fluxes used to optimize the metabolic processes within the microbial community.

  14. The Evaluation of a Regional Faith Community Network's Million Hearts Program.

    Science.gov (United States)

    Cooper, Jennifer; Zimmerman, Wendy

    2016-01-01

    The goal of the Million Hearts initiative is to prevent one million heart attacks and strokes by 2017. Maryland was one state in the Association of State and Territorial Health Official's Million Hearts State Learning Collaborative. Washington County, Maryland formed a collaboration between the County Health Department, Meritus Health System, and the Meritus Health Parish Nurse Coordinator to address hypertension in the county. Within a regional network of 52 faith communities, the Parish Nurse Coordinator recruited 25 faith community nurses to participate in a three-month program. Nurses were trained on proper blood pressure measurement and 22 nurses identified 58 participants engaged in blood pressure self-monitoring and coaching for lifestyle changes. Additionally, nurses took 1,729 blood pressures and provided health education to individuals within their congregations. Fifty-one participants participated in blood pressure self-monitoring and lifestyle coaching with faith community nurses. There was improvement in six out of seven lifestyle areas. Eight-two percent of participants (N = 42) decreased their systolic and/or diastolic blood pressure over three months. Coaching provided by faith community nurses can create an environment of sustained support to promote improved lifestyle and blood pressure changes over time. © 2015 Wiley Periodicals, Inc.

  15. Environmental Assessment KC-46A Depot Maintenance Activation, Tinker Air Force Base, Oklahoma. Volume 1

    Science.gov (United States)

    2014-03-01

    Cultural Resources Tinker AFB is located in the south central Oklahoma archaeological region. Important research into the prehistory of central...No. 5. Oklahoma River Basin Survey Project. University of Oklahoma Research Institute. Norman. Bell, R. E. (editor). 1984. Prehistory of Oklahoma...February 2012. Galm, J.R. and P. Flynn. 1978. The Cultural Sequences of the Scott (34LF11) and Wann (34LF27) Sites and Prehistory of the Wister Valley

  16. Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami

    Science.gov (United States)

    Lu, Xin; Brelsford, Christa

    2014-10-01

    To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the 2011 earthquake and tsunami in Japan. We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average. By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their current community. While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content. This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

  17. Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase

    Directory of Open Access Journals (Sweden)

    Nudelman Irina

    2010-10-01

    Full Text Available Abstract Background Dendritic cells are antigen-presenting cells that play an essential role in linking the innate and adaptive immune systems. Much research has focused on the signaling pathways triggered upon infection of dendritic cells by various pathogens. The high level of activity in the field makes it desirable to have a pathway-based resource to access the information in the literature. Current pathway diagrams lack either comprehensiveness, or an open-access editorial interface. Hence, there is a need for a dependable, expertly curated knowledgebase that integrates this information into a map of signaling networks. Description We have built a detailed diagram of the dendritic cell signaling network, with the goal of providing researchers with a valuable resource and a facile method for community input. Network construction has relied on comprehensive review of the literature and regular updates. The diagram includes detailed depictions of pathways activated downstream of different pathogen recognition receptors such as Toll-like receptors, retinoic acid-inducible gene-I-like receptors, C-type lectin receptors and nucleotide-binding oligomerization domain-like receptors. Initially assembled using CellDesigner software, it provides an annotated graphical representation of interactions stored in Systems Biology Mark-up Language. The network, which comprises 249 nodes and 213 edges, has been web-published through the Biological Pathway Publisher software suite. Nodes are annotated with PubMed references and gene-related information, and linked to a public wiki, providing a discussion forum for updates and corrections. To gain more insight into regulatory patterns of dendritic cell signaling, we analyzed the network using graph-theory methods: bifan, feedforward and multi-input convergence motifs were enriched. This emphasis on activating control mechanisms is consonant with a network that subserves persistent and coordinated responses to

  18. Signaling network of dendritic cells in response to pathogens: a community-input supported knowledgebase.

    Science.gov (United States)

    Patil, Sonali; Pincas, Hanna; Seto, Jeremy; Nudelman, German; Nudelman, Irina; Sealfon, Stuart C

    2010-10-07

    Dendritic cells are antigen-presenting cells that play an essential role in linking the innate and adaptive immune systems. Much research has focused on the signaling pathways triggered upon infection of dendritic cells by various pathogens. The high level of activity in the field makes it desirable to have a pathway-based resource to access the information in the literature. Current pathway diagrams lack either comprehensiveness, or an open-access editorial interface. Hence, there is a need for a dependable, expertly curated knowledgebase that integrates this information into a map of signaling networks. We have built a detailed diagram of the dendritic cell signaling network, with the goal of providing researchers with a valuable resource and a facile method for community input. Network construction has relied on comprehensive review of the literature and regular updates. The diagram includes detailed depictions of pathways activated downstream of different pathogen recognition receptors such as Toll-like receptors, retinoic acid-inducible gene-I-like receptors, C-type lectin receptors and nucleotide-binding oligomerization domain-like receptors. Initially assembled using CellDesigner software, it provides an annotated graphical representation of interactions stored in Systems Biology Mark-up Language. The network, which comprises 249 nodes and 213 edges, has been web-published through the Biological Pathway Publisher software suite. Nodes are annotated with PubMed references and gene-related information, and linked to a public wiki, providing a discussion forum for updates and corrections. To gain more insight into regulatory patterns of dendritic cell signaling, we analyzed the network using graph-theory methods: bifan, feedforward and multi-input convergence motifs were enriched. This emphasis on activating control mechanisms is consonant with a network that subserves persistent and coordinated responses to pathogen detection. This map represents a navigable

  19. Social capital in a lower socioeconomic palliative care population: a qualitative investigation of individual, community and civic networks and relations.

    Science.gov (United States)

    Lewis, Joanne M; DiGiacomo, Michelle; Currow, David C; Davidson, Patricia M

    2014-01-01

    Lower socioeconomic populations live and die in contexts that render them vulnerable to poorer health and wellbeing. Contexts of care at the end of life are overwhelmingly determined by the capacity and nature of formal and informal networks and relations to support care. To date, studies exploring the nature of networks and relations of support in lower socioeconomic populations at the end of life are absent. This qualitative study sought to identify the nature of individual, community and civic networks and relations that defined the contexts of care for this group. Semi-structured qualitative interviews were conducted with 16 patients and 6 informal carers who identified that they had social and economic needs and were from a lower socioeconomic area. A social capital questionnaire identifying individual, community and civic networks and relations formed the interview guide. Interviews were audio-taped, transcribed and analysed using framework analysis. Participants identified that individual and community networks and relations of support were mainly inadequate to meet care needs. Specifically, data revealed: (1) individual (informal caregivers) networks and relations were small and fragile due to the nature of conflict and crisis; (2) community trust and engagement was limited and shifted by illness and caregiving; (3) and formal care services were inconsistent and provided limited practical support. Some transitions in community relations for support were noted. Levels of civic and government engagement and support were overall positive and enabled access to welfare resources. Networks and relations of support are essential for ensuring quality end of life care is achieved. Lower socioeconomic groups are at a distinct disadvantage where these networks and relations are limited, as they lack the resources necessary to augment these gaps. Understanding of the nature of assets and limitations, in networks and relations of support, is necessary to inform

  20. The relation between social network site usage and loneliness and mental health in community-dwelling older adults.

    Science.gov (United States)

    Aarts, S; Peek, S T M; Wouters, E J M

    2015-09-01

    Loneliness is expected to become an even bigger social problem in the upcoming decades, because of the growing number of older adults. It has been argued that the use of social network sites can aid in decreasing loneliness and improving mental health. The purpose of this study was to examine whether and how social network sites usage is related to loneliness and mental health in community-dwelling older adults. The study population included community-dwelling older adults aged 60 and over residing in the Netherlands (n = 626) collected through the LISS panel (www.lissdata.nl). Univariate and multivariate linear regression analyses, adjusted for potentially important confounders, were conducted in order to investigate the relation between social network sites usage and (emotional and social) loneliness and mental health. More than half of the individuals (56.2%) reported to use social network sites at least several times per week. Social network sites usage appeared unrelated to loneliness in general, and to emotional and social loneliness in particular. Social network sites usage also appeared unrelated to mental health. Several significant associations between related factors and the outcomes at hand were detected. In this sample, which was representative for the Dutch population, social network sites usage was unrelated to loneliness and/or mental health. The results indicate that a simple association between social network site usage and loneliness and mental health as such, cannot automatically be assumed in community-dwelling older adults. Copyright © 2014 John Wiley & Sons, Ltd.

  1. Innovation in Indigenous Health and Medical Education: The Leaders in Indigenous Medical Education (LIME) Network as a Community of Practice.

    Science.gov (United States)

    Mazel, Odette; Ewen, Shaun

    2015-01-01

    The Leaders in Indigenous Medical Education (LIME) Network aims to improve the quality and effectiveness of Indigenous health in medical education as well as best practice in the recruitment, retention, and graduation of Indigenous medical students. In this article we explore the utility of Etienne Wenger's "communities of practice" (CoP) concept in providing a theoretical framework to better understand the LIME Network as a form of social infrastructure to further knowledge and innovation in this important area of health care education reform. The Network operates across all medical schools in Australia and New Zealand. Utilizing a model of evaluation of communities of practice developed by Fung-Kee-Fung et al., we seek to analyze the outcomes of the LIME Network as a CoP and assess its approach and contribution to improving the implementation of Indigenous health in the medical curriculum and the graduation of Indigenous medical students. By reflecting on the Network through a community of practice lens, this article highlights the synthesis between the LIME Network and Wenger's theory and provides a framework with which to measure Network outputs. It also posits an opportunity to better capture the impact of Network activities into the future to ensure that it remains a relevant and sustainable entity.

  2. Content-specific network analysis of peer-to-peer communication in an online community for smoking cessation.

    Science.gov (United States)

    Myneni, Sahiti; Cobb, Nathan K; Cohen, Trevor

    2016-01-01

    Analysis of user interactions in online communities could improve our understanding of health-related behaviors and inform the design of technological solutions that support behavior change. However, to achieve this we would need methods that provide granular perspective, yet are scalable. In this paper, we present a methodology for high-throughput semantic and network analysis of large social media datasets, combining semi-automated text categorization with social network analytics. We apply this method to derive content-specific network visualizations of 16,492 user interactions in an online community for smoking cessation. Performance of the categorization system was reasonable (average F-measure of 0.74, with system-rater reliability approaching rater-rater reliability). The resulting semantically specific network analysis of user interactions reveals content- and behavior-specific network topologies. Implications for socio-behavioral health and wellness platforms are also discussed.

  3. Aftershock Forecasting: Recent Developments and Lessons from the 2016 M5.8 Pawnee, Oklahoma, Earthquake

    Science.gov (United States)

    Michael, A. J.; Field, E. H.; Hardebeck, J.; Llenos, A. L.; Milner, K. R.; Page, M. T.; Perry, S. C.; van der Elst, N.; Wein, A. M.

    2016-12-01

    After the Mw 5.8 Pawnee, Oklahoma, earthquake of September 3, 2016 the USGS issued a series of aftershock forecasts for the next month and year. These forecasts were aimed at the emergency response community, those making decisions about well operations in the affected region, and the general public. The forecasts were generated manually using methods planned for automatically released Operational Aftershock Forecasts. The underlying method is from Reasenberg and Jones (Science, 1989) with improvements recently published in Page et al. (BSSA, 2016), implemented in a JAVA Graphical User Interface and presented in a template that is under development. The methodological improvements include initial models based on the tectonic regime as defined by Garcia et al. (BSSA, 2012) and the inclusion of both uncertainty in the clustering parameters and natural random variability. We did not utilize the time-dependent magnitude of completeness model from Page et al. because it applies only to teleseismic events recorded by NEIC. The parameters for Garcia's Generic Active Continental Region underestimated the modified-Omori decay parameter and underestimated the aftershock rate by a factor of 2. And the sequence following the Mw 5.7 Prague, Oklahoma, earthquake of November 6, 2011 was about 3 to 4 times more productive than the Pawnee sequence. The high productivity for these potentially induced sequences is consistent with an increase in productivity in Oklahoma since 2009 (Llenos and Michael, BSSA, 2013) and makes a general tectonic model inapplicable to sequences in this region. Soon after the mainshock occurred, the forecasts relied on the sequence specific parameters. After one month, the Omori decay parameter p is less than one, implying a very long-lived sequence. However, the decay parameter is known to be biased low at early times due to secondary aftershock triggering, and the p-value determined early in the sequence may be inaccurate for long-term forecasting.

  4. 40 CFR 81.47 - Central Oklahoma Intrastate Air Quality Control Region.

    Science.gov (United States)

    2010-07-01

    ... Quality Control Region. 81.47 Section 81.47 Protection of Environment ENVIRONMENTAL PROTECTION AGENCY... Air Quality Control Regions § 81.47 Central Oklahoma Intrastate Air Quality Control Region. The Metropolitan Oklahoma Intrastate Air Quality Control Region has been renamed the Central Oklahoma Intrastate...

  5. Tallinna Ülikool ja Kesk-Oklahoma allkirjastasid koostööleppe

    Index Scriptorium Estoniae

    2011-01-01

    Septembris külastas Tallinna Ülikooli Kesk-Oklahoma Ülikooli (University of Central Oklahoma) ajalooprofessor ja rahvusvaheliste õpingute dekaan Richard M. Bernard. Külastuse jätkuna allkirjastasid Tallinna Ülikool ning Kesk-Oklahoma Ülikool koostöö memorandumi

  6. Inter-subject FDG PET Brain Networks Exhibit Multi-scale Community Structure with Different Normalization Techniques.

    Science.gov (United States)

    Sperry, Megan M; Kartha, Sonia; Granquist, Eric J; Winkelstein, Beth A

    2018-07-01

    Inter-subject networks are used to model correlations between brain regions and are particularly useful for metabolic imaging techniques, like 18F-2-deoxy-2-(18F)fluoro-D-glucose (FDG) positron emission tomography (PET). Since FDG PET typically produces a single image, correlations cannot be calculated over time. Little focus has been placed on the basic properties of inter-subject networks and if they are affected by group size and image normalization. FDG PET images were acquired from rats (n = 18), normalized by whole brain, visual cortex, or cerebellar FDG uptake, and used to construct correlation matrices. Group size effects on network stability were investigated by systematically adding rats and evaluating local network connectivity (node strength and clustering coefficient). Modularity and community structure were also evaluated in the differently normalized networks to assess meso-scale network relationships. Local network properties are stable regardless of normalization region for groups of at least 10. Whole brain-normalized networks are more modular than visual cortex- or cerebellum-normalized network (p network resolutions where modularity differs most between brain and randomized networks. Hierarchical analysis reveals consistent modules at different scales and clustering of spatially-proximate brain regions. Findings suggest inter-subject FDG PET networks are stable for reasonable group sizes and exhibit multi-scale modularity.

  7. Field assessment of bacterial communities and total trihalomethanes: Implications for drinking water networks.

    Science.gov (United States)

    Montoya-Pachongo, Carolina; Douterelo, Isabel; Noakes, Catherine; Camargo-Valero, Miller Alonso; Sleigh, Andrew; Escobar-Rivera, Juan-Carlos; Torres-Lozada, Patricia

    2018-03-01

    Operation and maintenance (O&M) of drinking water distribution networks (DWDNs) in tropical countries simultaneously face the control of acute and chronic risks due to the presence of microorganisms and disinfection by-products, respectively. In this study, results from a detailed field characterization of microbiological, chemical and infrastructural parameters of a tropical-climate DWDN are presented. Water physicochemical parameters and the characteristics of the network were assessed to evaluate the relationship between abiotic and microbiological factors and their association with the presence of total trihalomethanes (TTHMs). Illumina sequencing of the bacterial 16s rRNA gene revealed significant differences in the composition of biofilm and planktonic communities. The highly diverse biofilm communities showed the presence of methylotrophic bacteria, which suggest the presence of methyl radicals such as THMs within this habitat. Microbiological parameters correlated with water age, pH, temperature and free residual chlorine. The results from this study are necessary to increase the awareness of O&M practices in DWDNs required to reduce biofilm formation and maintain appropriate microbiological and chemical water quality, in relation to biofilm detachment and DBP formation. Copyright © 2017 Elsevier B.V. All rights reserved.

  8. Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling

    Science.gov (United States)

    Mitrović, Marija; Tadić, Bosiljka

    2012-11-01

    We present an analysis of the empirical data and the agent-based modeling of the emotional behavior of users on the Web portals where the user interaction is mediated by posted comments, like Blogs and Diggs. We consider the dataset of discussion-driven popular Diggs, in which all comments are screened by machine-learning emotion detection in the text, to determine positive and negative valence (attractiveness and aversiveness) of each comment. By mapping the data onto a suitable bipartite network, we perform an analysis of the network topology and the related time-series of the emotional comments. The agent-based model is then introduced to simulate the dynamics and to capture the emergence of the emotional behaviors and communities. The agents are linked to posts on a bipartite network, whose structure evolves through their actions on the posts. The emotional states (arousal and valence) of each agent fluctuate in time, subject to the current contents of the posts to which the agent is exposed. By an agent’s action on a post its current emotions are transferred to the post. The model rules and the key parameters are inferred from the considered empirical data to ensure their realistic values and mutual consistency. The model assumes that the emotional arousal over posts drives the agent’s action. The simulations are preformed for the case of constant flux of agents and the results are analyzed in full analogy with the empirical data. The main conclusions are that the emotion-driven dynamics leads to long-range temporal correlations and emergent networks with community structure, that are comparable with the ones in the empirical system of popular posts. In view of pure emotion-driven agents actions, this type of comparisons provide a quantitative measure for the role of emotions in the dynamics on real blogs. Furthermore, the model reveals the underlying mechanisms which relate the post popularity with the emotion dynamics and the prevalence of negative

  9. The LTER Network Information System: Improving Data Quality and Synthesis through Community Collaboration

    Science.gov (United States)

    Servilla, M.; Brunt, J.

    2011-12-01

    Emerging in the 1980's as a U.S. National Science Foundation funded research network, the Long Term Ecological Research (LTER) Network began with six sites and with the goal of performing comparative data collection and analysis of major biotic regions of North America. Today, the LTER Network includes 26 sites located in North America, Antarctica, Puerto Rico, and French Polynesia and has contributed a corpus of over 7,000 data sets to the public domain. The diversity of LTER research has led to a wealth of scientific data derived from atmospheric to terrestrial to oceanographic to anthropogenic studies. Such diversity, however, is a contributing factor to data being published with poor or inconsistent quality or to data lacking descriptive documentation sufficient for understanding their origin or performing derivative studies. It is for these reasons that the LTER community, in collaboration with the LTER Network Office, have embarked on the development of the LTER Network Information System (NIS) - an integrative data management approach to improve the process by which quality LTER data and metadata are assembled into a central archive, thereby enabling better discovery, analysis, and synthesis of derived data products. The mission of the LTER NIS is to promote advances in collaborative and synthetic ecological science at multiple temporal and spatial scales by providing the information management and technology infrastructure to increase: ? availability and quality of data from LTER sites - by the use and support of standardized approaches to metadata management and access to data; ? timeliness and number of LTER derived data products - by creating a suite of middleware programs and workflows that make it easy to create and maintain integrated data sets derived from LTER data; and ? knowledge generated from the synthesis of LTER data - by creating standardized access and easy to use applications to discover, access, and use LTER data. The LTER NIS will utilize

  10. Uranium favorability of southwestern Oklahoma and north-central Texas

    International Nuclear Information System (INIS)

    Stanton, G.D.; Brogdon, L.D.; Quick, J.V.; Thomas, N.G.; Martin, T.S.

    1977-10-01

    Results are presented of a project to identify and delineate units and (or) facies that are favorable for uranium in the Upper Pennsylvanian and Lower Permian strata of north-central Texas and southwestern Oklahoma. To aid in this evaluation, an assessment of the probable uranium rocks (Wichita and Arbuckle Mountains) was necessary. Surface samples were collected from igneous and sedimentary rocks. Stream-sediment samples were also collected. However, the main emphasis of the investigation of the sedimentary units was on the identification of sedimentary facies trends in the subsurface and an evaluation of the uranium favorability within units studied. The area investigated centers along the Red River, the boundary between Texas and Oklahoma. The project area encompasses approximately 17,000 sq. mi. It includes all or parts of Cooke, Montague, Clay, Wichita, Wilbarger, Hardeman, Baylor, Knox, and Archer Counties in Texas and Love, Jefferson, Cotton, Tillman, Jackson, Stephens, Carter, Comanche, Harmon, and Greer Counties in Oklahoma

  11. Urban Ecological Stewardship: Understanding the Structure, Function and Network of Community-based Urban Land Management

    Directory of Open Access Journals (Sweden)

    Lindsay K. Campbell

    2008-01-01

    Full Text Available Urban environmental stewardship activities are on the rise in cities throughout the Northeast. Groups participating in stewardship activities range in age, size, and geography and represent an increasingly complex and dynamic arrangement of civil society, government and business sectors. To better understand the structure, function and network of these community-based urban land managers, an assessment was conducted in 2004 by the research subcommittee of the Urban Ecology Collaborative. The goal of the assessment was to better understand the role of stewardship organizations engaged in urban ecology initiatives in selected major cities in the Northeastern U.S.: Boston, New Haven, New York City, Pittsburgh, Baltimore, and Washington, D.C. A total of 135 active organizations participated in this assessment. Findings include the discovery of a dynamic social network operating within cities, and a reserve of social capital and expertise that could be better utilized. Although often not the primary land owner, stewardship groups take an increasingly significant responsibility for a wide range of land use types including street and riparian corridors, vacant lots, public parks and gardens, green roofs, etc. Responsibilities include the delivery of public programs as well as daily maintenance and fundraising support. While most of the environmental stewardship organizations operate on staffs of zero or fewer than ten, with small cohorts of community volunteers, there is a significant difference in the total amount of program funding. Nearly all respondents agree that committed resources are scarce and insufficient with stewards relying upon and potentially competing for individual donations, local foundations, and municipal support. This makes it a challenge for the groups to grow beyond their current capacity and to develop long-term programs critical to resource management and education. It also fragments groups, making it difficult for planners and

  12. Depression and social networks in community dwelling elders: a descriptive study.

    Science.gov (United States)

    Wilby, Frances

    2011-04-01

    Social isolation and inadequate social support have been identified as correlates of depression in older adults, although the relationship between depression and social isolation is not entirely understood (Dorfman et al., 1995). This study was conducted to describe the social networks of depressed older adults living in the community and to compare the social networks of depressed and nondepressed individuals, thus adding to the body of knowledge regarding social networks, older adults, and depression. The sample consisted of 91 respondents aged 65 and older who were randomly selected using the voter registry. About 27% (25) respondents reported significant levels of depressive symptomology as measured by the Center for Epidemiological Studies-Depression Scale (CES-D). All respondents completed semistructured interviews that included questions about social contacts with family and others during the prior week. All participants reported social contact with family and friends during this period. In this sample, depressed elders were not socially isolated. They were more likely to report contacts with friends than those who were not depressed, and equally likely to report involvement in volunteer activities. Their likelihood of seeking social support was also comparable. Results emphasize the importance of peer relationships and suggest that, in some groups of older adults, social isolation may not be a hallmark of depressive symptoms.

  13. Benefits of Enterprise Social Networking Systems for High Energy Physics community

    Science.gov (United States)

    Silva de Sousa, B.; Wagner, A.; Ormancey, E.; Grzywaczewski, P.

    2015-12-01

    The emergence of social media platforms in the consumer space unlocked new ways of interaction between individuals on the Web. People develop now their social networks and relations based on common interests and activities with the choice to opt-in or opt-out on content of their interest. This kind of platforms have also an important place to fill inside large organizations and enterprises where communication and collaborators interaction are keys for development. Enterprise Social Networking Systems (ESN) add value to an organization by encouraging information sharing, capturing knowledge, enabling action and empowering people. CERN is currently rolling out an ESN which aims to unify and provide a single point of access to the multitude of information sources in the organization. It also implements social features that can be added on top of existing communication channels. While the deployment of this kind of platforms is not without risks we firmly believe that they are of the best interest for our community, opening the opportunity to evaluate a global social network for High Energy Physics (HEP).

  14. Integration of available regenerative energy sources in community networks for both electricity and heating

    Energy Technology Data Exchange (ETDEWEB)

    Alcalde Melo, Henrique

    2013-03-06

    . Water can be heated up to 70 C at least once a week preventing the bacterium Legionella to grow. The community is able to supply 99% of the uncontrollable load group demand and 97% of the controllable load group demand. There is enough energy available to heat space during the cold months, if heat pumps with a coefficient of performance greater than two are used. The electric vehicles can be charged using the energy generated in the community via grid or extra battery banks. If energy prices continue to increase, German households will try to find solutions to reduce their energy bills. The integration of several households forming a community network is a solution that optimizes the energy use and space (especially taking wind turbines in consideration), and reduces investments. However, the implementation of such a community still depends on the availability of space, improvement and price reduction of energy storage systems, regulations for energy exchange as well as willingness of the people living in such a community to adapt their daily routine according to the availability of energy.

  15. Integration of available regenerative energy sources in community networks for both electricity and heating

    Energy Technology Data Exchange (ETDEWEB)

    Alcalde Melo, Henrique

    2013-03-06

    heated up to 70 C at least once a week preventing the bacterium Legionella to grow. The community is able to supply 99% of the uncontrollable load group demand and 97% of the controllable load group demand. There is enough energy available to heat space during the cold months, if heat pumps with a coefficient of performance greater than two are used. The electric vehicles can be charged using the energy generated in the community via grid or extra battery banks. If energy prices continue to increase, German households will try to find solutions to reduce their energy bills. The integration of several households forming a community network is a solution that optimizes the energy use and space (especially taking wind turbines in consideration), and reduces investments. However, the implementation of such a community still depends on the availability of space, improvement and price reduction of energy storage systems, regulations for energy exchange as well as willingness of the people living in such a community to adapt their daily routine according to the availability of energy.

  16. Last Glacial Maximum Development of Parna Dunes in Panhandle Oklahoma, USA

    Science.gov (United States)

    Johnson, W. C.; Halfen, A. F.; McGowen, S.; Carter, B.; Fine, S.; Bement, L. C.; Simms, A. R.

    2012-12-01

    Though dunefields are a ubiquitous feature of the North American Great Plains, those studied to date have consisted primarily of sand grains. In Beaver County of the Oklahoma panhandle, however, upland dune forms consist of sand-sized aggregates of silt and clay. These aptly named parna dunes occur in two swarms, range in height from 10-15 m, and have asymmetrical dome morphologies with approximate north-south dune orientations. Despite their morphological similarities to sand dunes of the region, their origin and evolution is unknown. Documenting parna dune formation in the Oklahoma panhandle will help improve our understanding of prehistoric landscape instability and climate change, particularly in the central Great Plains where such records are limited. Panhandle parna dunes are typified by Blue Mound, our best documented parna dune thus far. Coring has documented a basal paleosol buried at a depth equivalent to the surrounding landscape—14C ages from this soil indicate its formation about 25-21 ka. The paleosol is a hydric Mollisol with a pronounced C3 isotopic signature reflecting hydric plant communities, rather than the regionally dominated C4 prairie vegetation. Hydric soils are associated with many of the playas on the surrounding landscape today, which suggests that they may have been more prevalent during the LGM. The overlying 8-10 m of parna is low in organic C and high in calcite, with indications of up to ten major episodes of sediment flux, which are documented with magnetic, isotope, soil-stratigraphic, particle-size, and color data. Near-surface luminescence (OSL) ages from Blue Mound are similar to the 14C ages from the basal paleosol, indicating rapid dune construction, with little or no Holocene accumulation of sediment. Marine isotope stage (MIS) 3 loess records indicate that upland areas of the region were relatively stable with attendant widespread pedogenesis prior to development of the parna dunes. At the onset of the LGM, however, the

  17. Mapping quorum sensing onto neural networks to understand collective decision making in heterogeneous microbial communities

    Science.gov (United States)

    Yusufaly, Tahir I.; Boedicker, James Q.

    2017-08-01

    Microbial communities frequently communicate via quorum sensing (QS), where cells produce, secrete, and respond to a threshold level of an autoinducer (AI) molecule, thereby modulating gene expression. However, the biology of QS remains incompletely understood in heterogeneous communities, where variant bacterial strains possess distinct QS systems that produce chemically unique AIs. AI molecules bind to ‘cognate’ receptors, but also to ‘non-cognate’ receptors found in other strains, resulting in inter-strain crosstalk. Understanding these interactions is a prerequisite for deciphering the consequences of crosstalk in real ecosystems, where multiple AIs are regularly present in the same environment. As a step towards this goal, we map crosstalk in a heterogeneous community of variant QS strains onto an artificial neural network model. This formulation allows us to systematically analyze how crosstalk regulates the community’s capacity for flexible decision making, as quantified by the Boltzmann entropy of all QS gene expression states of the system. In a mean-field limit of complete cross-inhibition between variant strains, the model is exactly solvable, allowing for an analytical formula for the number of variants that maximize capacity as a function of signal kinetics and activation parameters. An analysis of previous experimental results on the Staphylococcus aureus two-component Agr system indicates that the observed combination of variant numbers, gene expression rates and threshold concentrations lies near this critical regime of parameter space where capacity peaks. The results are suggestive of a potential evolutionary driving force for diversification in certain QS systems.

  18. Linking macroecology and community ecology: refining predictions of species distributions using biotic interaction networks.

    Science.gov (United States)

    Staniczenko, Phillip P A; Sivasubramaniam, Prabu; Suttle, K Blake; Pearson, Richard G

    2017-06-01

    Macroecological models for predicting species distributions usually only include abiotic environmental conditions as explanatory variables, despite knowledge from community ecology that all species are linked to other species through biotic interactions. This disconnect is largely due to the different spatial scales considered by the two sub-disciplines: macroecologists study patterns at large extents and coarse resolutions, while community ecologists focus on small extents and fine resolutions. A general framework for including biotic interactions in macroecological models would help bridge this divide, as it would allow for rigorous testing of the role that biotic interactions play in determining species ranges. Here, we present an approach that combines species distribution models with Bayesian networks, which enables the direct and indirect effects of biotic interactions to be modelled as propagating conditional dependencies among species' presences. We show that including biotic interactions in distribution models for species from a California grassland community results in better range predictions across the western USA. This new approach will be important for improving estimates of species distributions and their dynamics under environmental change. © 2017 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

  19. Oklahoma State University proposed Advanced Technology Research Center. Environmental Assessment

    Energy Technology Data Exchange (ETDEWEB)

    NONE

    1995-06-01

    The Department of Energy (DOE) has prepared an Environmental Assessment (EA) evaluating the construction and equipping of the proposed Advanced Technology Research Center (ATRC) at Oklahoma State University (OSU) in Stillwater, Oklahoma. Based on the analysis in the EA, the DOE has determined that the proposed action does not constitute a major federal action significantly affecting the quality of the human environment within the meaning of the National Environmental Policy Act (NEPA) of 1969. Therefore, the preparation of an Environmental Impact Statement is not required.

  20. The Indigenous Observation Network: Collaborative, Community-Based Monitoring in the Yukon River Basin

    Science.gov (United States)

    Herman-Mercer, N. M.; Mutter, E. A.; Wilson, N. J.; Toohey, R.; Schuster, P. F.

    2017-12-01

    The Indigenous Observation Network (ION) is a collaborative Community-Based Monitoring (CBM) program with both permafrost and water-quality monitoring components operating in the Yukon River Basin (YRB) of Alaska and Canada. ION is jointly facilitated by the Yukon River Inter-Tribal Watershed Council (YRITWC), an indigenous non-profit organization, and the US Geological Survey (USGS), a federal agency. The YRB is the fourth largest drainage basin in North America encompassing 855,000 square kilometers in northwestern Canada and central Alaska and is essential to the ecosystems of the Bering and Chuckchi Seas. Water is also fundamental to the subsistence and culture of the 76 Tribes and First Nations that live in the YRB providing sustenance in the form of drinking water, fish, wildlife, and vegetation. Despite the ecological and cultural significance of the YRB, the remote geography of sub-Arctic and Arctic Alaska and Canada make it difficult to collect scientific data in these locations and led to a lack of baseline data characterizing this system until recently. In response to community concerns about the quality of the YR and a desire by USGS scientists to create a long term water-quality database, the USGS and YRITWC collaborated to create ION in 2005. Surface water samples are collected by trained community technicians from Tribal Environmental Programs or First Nation Lands and Resources staff from over 35 Alaska Native Tribes and First Nations that reside along the YR and/or one of the major tributaries. Samples are analyzed at USGS laboratories in Boulder, CO and results are disseminated to participating YRB communities and the general public. This presentation will focus on the factors that have enabled the longevity and success of this program over the last decade, as well as the strategies ION uses to ensure the credibility of the data collected by community members and best practices that have facilitated the collection of surface water data in remote

  1. Building a Science Community of Effective Advocates: The Case of the Union of Concerned Scientists Science Network

    Science.gov (United States)

    Varga, M.; Worcester, J.

    2017-12-01

    The Union of Concerned Scientists (UCS) Science Network is a community of over 20,000 scientists, engineers, economists, public health specialists, and technical experts that inform and advocate for science-based solutions to some of our nation's most pressing problems. The role of the community manager here is to train and prepare Science Network members to be effective advocates for science-based decision making, and also to identify opportunities for them to put their skills and expertise into action on science and public health issues. As an organizational asset, but also an important resource to its members, it is crucial that the Science Network demonstrate its impact. But measuring impact when it comes to engagement and advocacy can be difficult. Here we will define a glossary of terms relating to community management and scientist engagement, delve into tracking and measurement of actions taken within a community, and connect the dots between tracking metrics and measuring impact. Measuring impact in community management is a growing field, and here we will also suggest future research that will help standardize impact measurement, as well as bring attention to the growing and unique role that scientist communities can have on policy and public engagement goals. This work has been informed by the American Association for the Advancement of Science's inaugural cohort of the Community Engagement Fellows Program.

  2. Double-difference tomography velocity structure in Northern Oklahoma: Evidence for reduced basement velocity in the Nemaha Uplift

    Science.gov (United States)

    Stevens, N. T.; Keranen, K. M.; Lambert, C.

    2016-12-01

    Induced seismicity in northern Oklahoma presents risk for infrastructure, but also an opportunity to gain new insights to earthquake processes [Petersen et al., 2016]. Here we present a double-difference tomographic study using TomoDD [Zhang and Thurber, 2003] in northern Oklahoma utilizing records from a dense broadband network over a 1-year period, constituting a catalog of over 10,000 local seismic events. We image a shallow (depth 4 km). We suggest that this low velocity anomaly stems from enhanced fracturing and/or weathering of the basement in the Nemaha uplift in northern Oklahoma. This velocity anomaly is not observed in basement off the shoulders of the structure, particularly to the southeast of the Nemaha bounding fault. Enhanced fracturing, and related increases to permeability, would ease pressure migration from injection wells linked to increased seismicity in the region, and may explain the relative absence of seismicity coincident with this structure compared to it periphery. References Gay, S. Parker, J. (2003), The Nemaha Trend-A System of Compressional Thrust-Fold, Strike-Slilp Structural Features in Kansas and Oklahoma, Part 1, Shale Shak., 9-49. Petersen, M. D., C. S. Mueller, M. P. Moschetti, S. M. Hoover, A. L. Llenos, W. L. Ellsworth, A. J. Michael, J. L. Rubinstein, A. F. McGarr, and K. S. Rukstales (2016), 2016 One-Year Seismic Hazard Forecast for the Central and Eastern United States from Induced and Natural Earthquakes, Open-File Rep., doi:10.3133/OFR20161035. Zhang, H., and C. H. Thurber (2003), Double-difference tomography: The method and its application to the Hayward Fault, California, Bull. Seismol. Soc. Am., 93(5), 1875-1889, doi:10.1785/0120020190.

  3. Building a Community of Practice for Researchers: The International Network for Simulation-Based Pediatric Innovation, Research and Education.

    Science.gov (United States)

    Cheng, Adam; Auerbach, Marc; Calhoun, Aaron; Mackinnon, Ralph; Chang, Todd P; Nadkarni, Vinay; Hunt, Elizabeth A; Duval-Arnould, Jordan; Peiris, Nicola; Kessler, David

    2018-06-01

    The scope and breadth of simulation-based research is growing rapidly; however, few mechanisms exist for conducting multicenter, collaborative research. Failure to foster collaborative research efforts is a critical gap that lies in the path of advancing healthcare simulation. The 2017 Research Summit hosted by the Society for Simulation in Healthcare highlighted how simulation-based research networks can produce studies that positively impact the delivery of healthcare. In 2011, the International Network for Simulation-based Pediatric Innovation, Research and Education (INSPIRE) was formed to facilitate multicenter, collaborative simulation-based research with the aim of developing a community of practice for simulation researchers. Since its formation, the network has successfully completed and published numerous collaborative research projects. In this article, we describe INSPIRE's history, structure, and internal processes with the goal of highlighting the community of practice model for other groups seeking to form a simulation-based research network.

  4. Synthesizing community wisdom: A model for sharing cancer-related resources through social networking and collaborative partnerships.

    Science.gov (United States)

    Weiss, Jacob B; Lorenzi, Nancy M; Lorenzi, Nancy

    2008-11-06

    Despite the availability of community-based support services, cancer patients and survivors are not aware of many of these resources. Without access to community programs, cancer survivors are at risk for lower quality of care and lower quality of life. At the same time, non-profit community organizations lack access to advanced consumer informatics applications to effectively promote awareness of their services. In addition to the current models of print and online resource guides, new community-driven informatics approaches are needed to achieve the goal of comprehensive care for cancer survivors. We present the formulation of a novel model for synthesizing a local communitys collective wisdom of cancer-related resources through a combination of online social networking technologies and real-world collaborative partnerships. This approach can improve awareness of essential, but underutilized community resources.

  5. Investigating the Associations between Ethnic Networks, Community Social Capital, and Physical Health among Marriage Migrants in Korea.

    Science.gov (United States)

    Kim, Harris Hyun-Soo

    2018-01-17

    This study examines factors associated with the physical health of Korea's growing immigrant population. Specifically, it focuses on the associations between ethnic networks, community social capital, and self-rated health (SRH) among female marriage migrants. For empirical testing, secondary analysis of a large nationally representative sample (NSMF 2009) is conducted. Given the clustered data structure (individuals nested in communities), a series of two-level random intercepts and slopes models are fitted to probe the relationships between SRH and interpersonal (bonding and bridging) networks among foreign-born wives in Korea. In addition to direct effects, cross-level interaction effects are investigated using hierarchical linear modeling. While adjusting for confounders, bridging (inter-ethnic) networks are significantly linked with better health. Bonding (co-ethnic) networks, to the contrary, are negatively associated with immigrant health. Net of individual-level covariates, living in a commuijnity with more aggregate bridging social capital is positively linked with health. Community-level bonding social capital, however, is not a significant predictor. Lastly, two cross-level interaction terms are found. First, the positive relationship between bridging network and health is stronger in residential contexts with more aggregate bridging social capital. Second, it is weaker in communities with more aggregate bonding social capital.

  6. Community structure in real-world networks from a non-parametrical synchronization-based dynamical approach

    International Nuclear Information System (INIS)

    Moujahid, Abdelmalik; D’Anjou, Alicia; Cases, Blanca

    2012-01-01

    Highlights: ► A synchronization-based algorithm for community structure detection is proposed. ► We model a complex network based on coupled nonidentical chaotic Rössler oscillators. ► The interaction scheme contemplates an uniformly increasing coupling force. ► The frequencies of oscillators are adapted according to a parameterless mechanism. ► The adaptation mechanism reveals the community structure present in the network. - Abstract: This work analyzes the problem of community structure in real-world networks based on the synchronization of nonidentical coupled chaotic Rössler oscillators each one characterized by a defined natural frequency, and coupled according to a predefined network topology. The interaction scheme contemplates an uniformly increasing coupling force to simulate a society in which the association between the agents grows in time. To enhance the stability of the correlated states that could emerge from the synchronization process, we propose a parameterless mechanism that adapts the characteristic frequencies of coupled oscillators according to a dynamic connectivity matrix deduced from correlated data. We show that the characteristic frequency vector that results from the adaptation mechanism reveals the underlying community structure present in the network.

  7. Environmental factors shaping cultured free-living amoebae and their associated bacterial community within drinking water network.

    Science.gov (United States)

    Delafont, Vincent; Bouchon, Didier; Héchard, Yann; Moulin, Laurent

    2016-09-01

    Free-living amoebae (FLA) constitute an important part of eukaryotic populations colonising drinking water networks. However, little is known about the factors influencing their ecology in such environments. Because of their status as reservoir of potentially pathogenic bacteria, understanding environmental factors impacting FLA populations and their associated bacterial community is crucial. Through sampling of a large drinking water network, the diversity of cultivable FLA and their bacterial community were investigated by an amplicon sequencing approach, and their correlation with physicochemical parameters was studied. While FLA ubiquitously colonised the water network all year long, significant changes in population composition were observed. These changes were partially explained by several environmental parameters, namely water origin, temperature, pH and chlorine concentration. The characterisation of FLA associated bacterial community reflected a diverse but rather stable consortium composed of nearly 1400 OTUs. The definition of a core community highlighted the predominance of only few genera, majorly dominated by Pseudomonas and Stenotrophomonas. Co-occurrence analysis also showed significant patterns of FLA-bacteria association, and allowed uncovering potentially new FLA - bacteria interactions. From our knowledge, this study is the first that combines a large sampling scheme with high-throughput identification of FLA together with associated bacteria, along with their influencing environmental parameters. Our results demonstrate the importance of physicochemical parameters in the ecology of FLA and their bacterial community in water networks. Copyright © 2016 Elsevier Ltd. All rights reserved.

  8. FoodMicrobionet: A database for the visualisation and exploration of food bacterial communities based on network analysis.

    Science.gov (United States)

    Parente, Eugenio; Cocolin, Luca; De Filippis, Francesca; Zotta, Teresa; Ferrocino, Ilario; O'Sullivan, Orla; Neviani, Erasmo; De Angelis, Maria; Cotter, Paul D; Ercolini, Danilo

    2016-02-16

    Amplicon targeted high-throughput sequencing has become a popular tool for the culture-independent analysis of microbial communities. Although the data obtained with this approach are portable and the number of sequences available in public databases is increasing, no tool has been developed yet for the analysis and presentation of data obtained in different studies. This work describes an approach for the development of a database for the rapid exploration and analysis of data on food microbial communities. Data from seventeen studies investigating the structure of bacterial communities in dairy, meat, sourdough and fermented vegetable products, obtained by 16S rRNA gene targeted high-throughput sequencing, were collated and analysed using Gephi, a network analysis software. The resulting database, which we named FoodMicrobionet, was used to analyse nodes and network properties and to build an interactive web-based visualisation. The latter allows the visual exploration of the relationships between Operational Taxonomic Units (OTUs) and samples and the identification of core- and sample-specific bacterial communities. It also provides additional search tools and hyperlinks for the rapid selection of food groups and OTUs and for rapid access to external resources (NCBI taxonomy, digital versions of the original articles). Microbial interaction network analysis was carried out using CoNet on datasets extracted from FoodMicrobionet: the complexity of interaction networks was much lower than that found for other bacterial communities (human microbiome, soil and other environments). This may reflect both a bias in the dataset (which was dominated by fermented foods and starter cultures) and the lower complexity of food bacterial communities. Although some technical challenges exist, and are discussed here, the net result is a valuable tool for the exploration of food bacterial communities by the scientific community and food industry. Copyright © 2015. Published by

  9. A parameter-free community detection method based on centrality and dispersion of nodes in complex networks

    Science.gov (United States)

    Li, Yafang; Jia, Caiyan; Yu, Jian

    2015-11-01

    K-means is a simple and efficient clustering algorithm to detect communities in networks. However, it may suffer from a bad choice of initial seeds (also called centers) that seriously affect the clustering accuracy and the convergence rate. Additionally, in K-means, the number of communities should be specified in advance. Till now, it is still an open problem on how to select initial seeds and how to determine the number of communities. In this study, a new parameter-free community detection method (named K-rank-D) was proposed. First, based on the fact that good initial seeds usually have high importance and are dispersedly located in a network, we proposed a modified PageRank centrality to evaluate the importance of a node, and drew a decision graph to depict the importance and the dispersion of nodes. Then, the initial seeds and the number of communities were selected from the decision graph actively and intuitively as the 'start' parameter of K-means. Experimental results on synthetic and real-world networks demonstrate the superior performance of our approach over competing methods for community detection.

  10. Interactive network configuration maintains bacterioplankton community structure under elevated CO2 in a eutrophic coastal mesocosm experiment

    Science.gov (United States)

    Lin, Xin; Huang, Ruiping; Li, Yan; Li, Futian; Wu, Yaping; Hutchins, David A.; Dai, Minhan; Gao, Kunshan

    2018-01-01

    There is increasing concern about the effects of ocean acidification on marine biogeochemical and ecological processes and the organisms that drive them, including marine bacteria. Here, we examine the effects of elevated CO2 on the bacterioplankton community during a mesocosm experiment using an artificial phytoplankton community in subtropical, eutrophic coastal waters of Xiamen, southern China. Through sequencing the bacterial 16S rRNA gene V3-V4 region, we found that the bacterioplankton community in this high-nutrient coastal environment was relatively resilient to changes in seawater carbonate chemistry. Based on comparative ecological network analysis, we found that elevated CO2 hardly altered the network structure of high-abundance bacterioplankton taxa but appeared to reassemble the community network of low abundance taxa. This led to relatively high resilience of the whole bacterioplankton community to the elevated CO2 level and associated chemical changes. We also observed that the Flavobacteria group, which plays an important role in the microbial carbon pump, showed higher relative abundance under the elevated CO2 condition during the early stage of the phytoplankton bloom in the mesocosms. Our results provide new insights into how elevated CO2 may influence bacterioplankton community structure.

  11. Semi-supervised spectral algorithms for community detection in complex networks based on equivalence of clustering methods

    Science.gov (United States)

    Ma, Xiaoke; Wang, Bingbo; Yu, Liang

    2018-01-01

    Community detection is fundamental for revealing the structure-functionality relationship in complex networks, which involves two issues-the quantitative function for community as well as algorithms to discover communities. Despite significant research on either of them, few attempt has been made to establish the connection between the two issues. To attack this problem, a generalized quantification function is proposed for community in weighted networks, which provides a framework that unifies several well-known measures. Then, we prove that the trace optimization of the proposed measure is equivalent with the objective functions of algorithms such as nonnegative matrix factorization, kernel K-means as well as spectral clustering. It serves as the theoretical foundation for designing algorithms for community detection. On the second issue, a semi-supervised spectral clustering algorithm is developed by exploring the equivalence relation via combining the nonnegative matrix factorization and spectral clustering. Different from the traditional semi-supervised algorithms, the partial supervision is integrated into the objective of the spectral algorithm. Finally, through extensive experiments on both artificial and real world networks, we demonstrate that the proposed method improves the accuracy of the traditional spectral algorithms in community detection.

  12. Making the Invisible Visible: The Oklahoma Science Project.

    Science.gov (United States)

    McCarty, Robbie; Pedersen, Jon E.

    2002-01-01

    Reports that teachers in preservice education programs still view the teaching of science much in the same traditional ways as our predecessors. "The Oklahoma Science Project (OSP) Model for Professional Development: Practicing Science Across Contexts" will build discourses and relationships that can be extended across contexts to establish…

  13. Forest statistics for east Oklahoma counties - l993

    Science.gov (United States)

    Patrick E. Miller; Andrew J. Hartsell; Jack D. London

    1993-01-01

    This report contains the statistical tables and figures derived from data obtained during a recent inventory of east Oklahoma. The multiresource inventory included 18 counties and two survey regions. Data on forest acreage and timber volume involved a three-step procedure. First, estimate of forest acreage were made for each county using aerial photographs....

  14. The Native American Studies Program at the University of Oklahoma.

    Science.gov (United States)

    Kidwell, Clara Sue

    2001-01-01

    Begun in 1994, the Native American Studies program at the University of Oklahoma is an interdisciplinary B.A. program with a liberal arts orientation and strong emphasis on contemporary American Indian policy. Program strengths include the number and diversity of the faculty involved, the four Native languages taught, connections to tribal…

  15. Digital Learning Compass: Distance Education State Almanac 2017. Oklahoma

    Science.gov (United States)

    Seaman, Julia E.; Seaman, Jeff

    2017-01-01

    This brief report uses data collected under the U.S. Department of Education's National Center for Educational Statistics (NCES) Integrated Postsecondary Education Data System (IPEDS) Fall Enrollment survey to highlight distance education data in the state of Oklahoma. The sample for this analysis is comprised of all active, degree-granting…

  16. 75 FR 23280 - Choctaw Nation of Oklahoma Alcohol Control Ordinance

    Science.gov (United States)

    2010-05-03

    .... Application of Federal Law. Federal law forbids the introduction, possession, and sale of liquor in Indian... State of Oklahoma. D. ``Applicant'' means any person who submits an application to the Alcohol... adjacent thereto; public restaurants, buildings, meeting halls, hotels, theaters, retail stores, and...

  17. Data Privacy Laws Follow Lead of Oklahoma and California

    Science.gov (United States)

    Vance, Amelia

    2016-01-01

    Oklahoma's Student Data Accessibility, Transparency, and Accountability Act (known as the Student DATA Act) arose just as privacy concerns about student data were beginning to surface. According to Linnette Attai, founder of education technology compliance consultancy PlayWell LLC, "When this climate of data privacy first emerged in its…

  18. 75 FR 5015 - Television Broadcasting Services; Oklahoma City, OK

    Science.gov (United States)

    2010-02-01

    ...] Television Broadcasting Services; Oklahoma City, OK AGENCY: Federal Communications Commission. ACTION... procedures for comments, see 47 CFR 1.415 and 1.420. List of Subjects in 47 CFR Part 73 Television, Television broadcasting. For the reasons discussed in the preamble, the Federal Communications Commission...

  19. 75 FR 13236 - Television Broadcasting Services; Oklahoma City, OK

    Science.gov (United States)

    2010-03-19

    ... FEDERAL COMMUNICATIONS COMMISSION 47 CFR Part 73 [DA 10-395; MB Docket No. 10-19; RM-11589] Television Broadcasting Services; Oklahoma City, OK AGENCY: Federal Communications Commission. ACTION: Final... review Act, see 5 U.S.C. 801(a)(1)(A). List of Subjects in 47 CFR Part 73 Television, Television...

  20. Ground-water conditions in the vicinity of Enid, Oklahoma

    Science.gov (United States)

    Schoff, Stuart L.

    1948-01-01

    This memorandum summaries matter discussed at a meeting of the City Commission of Enid, Oklahoma, on Thursday, January 15, 1948, at which the write presented a brief analysis of the ground-water resources available to the City of Enid and answered questions brought up by the commissioners.

  1. Ecoregions and stream morphology in eastern Oklahoma

    Science.gov (United States)

    Splinter, D.K.; Dauwalter, D.C.; Marston, R.A.; Fisher, W.L.

    2010-01-01

    Broad-scale variables (i.e., geology, topography, climate, land use, vegetation, and soils) influence channel morphology. How and to what extent the longitudinal pattern of channel morphology is influenced by broad-scale variables is important to fluvial geomorphologists and stream ecologists. In the last couple of decades, there has been an increase in the amount of interdisciplinary research between fluvial geomorphologists and stream ecologists. In a historical context, fluvial geomorphologists are more apt to use physiographic regions to distinguish broad-scale variables, while stream ecologists are more apt to use the concept of an ecosystem to address the broad-scale variables that influence stream habitat. For this reason, we designed a study using ecoregions, which uses physical and biological variables to understand how landscapes influence channel processes. Ecoregions are delineated by similarities in geology, climate, soils, land use, and potential natural vegetation. In the fluvial system, stream form and function are dictated by processes observed throughout the fluvial hierarchy. Recognizing that stream form and function should differ by ecoregion, a study was designed to evaluate how the characteristics of stream channels differed longitudinally among three ecoregions in eastern Oklahoma, USA: Boston Mountains, Ozark Highlands, and Ouachita Mountains. Channel morphology of 149 stream reaches was surveyed in 1st- through 4th-order streams, and effects of drainage area and ecoregion on channel morphology was evaluated using multiple regressions. Differences existed (?????0.05) among ecoregions for particle size, bankfull width, and width/depth ratio. No differences existed among ecoregions for gradient or sinuosity. Particle size was smallest in the Ozark Highlands and largest in the Ouachita Mountains. Bankfull width was larger in the Ozark Highlands than in the Boston Mountains and Ouachita Mountains in larger streams. Width/depth ratios of the

  2. University of Oklahoma - High Energy Physics

    Energy Technology Data Exchange (ETDEWEB)

    Skubic, Patrick L. [University of Oklahoma

    2013-07-31

    The High Energy Physics program at the University of Oklahoma, Pat Skubic, Principal Investigator, is attempting to understand nature at the deepest level using the most advanced experimental and theoretical tools. The four experimental faculty, Brad Abbott, Phil Gutierrez, Pat Skubic, and Mike Strauss, together with post-doctoral associates and graduate students, are finishing their work as part of the D0 collaboration at Fermilab, and increasingly focusing their investigations at the Large Hadron Collidor (LHC) as part of the ATLAS Collaboration. Work at the LHC has become even more exciting with the recent discovery by ATLAS and the other collaboration, CMS, of the long-sought Higgs boson, which plays a key role in generating masses for the elementary constituents of matter. Work of the OUHEP group has been in the three areas of hardware, software, and analysis. Now that the Higgs boson has been discovered, completing the Standard Model of fundamental physics, new efforts will focus on finding hints of physics beyond the standard model, such as supersymmetry. The OUHEP theory group (Kim Milton, PI) also consists of four faculty members, Howie Baer, Chung Kao, Kim Milton, and Yun Wang, and associated students and postdocs. They are involved in understanding fundamental issues in formulating theories of the microworld, and in proposing models that carry us past the Standard Model, which is an incomplete description of nature. They therefore work in close concert with their experimental colleagues. One also can study fundamental physics by looking at the large scale structure of the universe; in particular the ``dark energy'' that seems to be causing the universe to expand at an accelerating rate, effectively makes up about 3/4 of the energy in the universe, and yet is totally unidentified. Dark energy and dark matter, which together account for nearly all of the energy in the universe, are an important probe of fundamental physics at the very shortest

  3. Promoting and developing a trail network across suburban, rural, and urban communities.

    Science.gov (United States)

    Schasberger, Michele G; Hussa, Carol S; Polgar, Michael F; McMonagle, Julie A; Burke, Sharon J; Gegaris, Andrew J

    2009-12-01

    The Wyoming Valley Wellness Trails Partnership received an Active Living by Design grant late in 2003 for a project centered on a growing trail network linking urban, suburban, and rural communities in northeast Pennsylvania, a former coal region, in order to increase physical activity among residents. The partnership conducted research, collected information, created promotional documents, worked with partners on events and programs, and participated in trail planning. Local trail organizations continued planning and construction toward developing a trail network. Other partners spearheaded policy change in schools and worksites and worked toward downtown revitalization. The partnership assisted these efforts by providing a forum in which organizations could meet. The partnership became a central resource for information about local parks, trails, and outdoor recreational activities. The partnership increased awareness and use of recreational facilities. Trail partners constructed 22 miles of walking and biking trails. The partnership took advantage of an allied effort that created organizational capacity for wellness in schools and worksites. Messages promoting social and entertainment benefits of physical activity were more successful than those promoting health benefits. The existence of multiple small, independent trail organizations can help advance trail development through concurrent development efforts. Urban, suburban, and rural residents' conceptions of walkability may differ. Trails provide options for recreational and transportation-related physical activity across urban, suburban, and rural landscapes that are supported by all constituents. Trail builders can be strong allies in bringing active living to suburban and rural places.

  4. Serving our communities better. Guidelines for planning and developing integrated delivery networks.

    Science.gov (United States)

    Prybil, L; Golden, P; Ballance, X

    1995-04-01

    In 1994 the Daughters of Charity National Health System-East Central (DCNHS-East Central) adopted 11 guidelines to help corporate staff and local leaders plan and develop integrated networks. Guideline 1 emphasizes needs-based strategic planning. Guideline 2 focuses on the community-based network planning process, recommending a team approach and ongoing communication with the local ordinary. In guidelines 3 through 5, the DCNHS-East Central Board of Directors spells out key issues that must be covered in proposals ultimately presented for governance action. Guideline 6 presents three core elements that should characterize all CBNs in which DCNHS-East Central institutions participate. Guideline 7 emphasizes that all CBN proposals and agreements must be clear with respect to the Catholic identity of DCNHS-East Central institutions. Guidelines 8 and 9 require that proposed changes to traditional policies and management practices be explicit in CBN proposals. The tenth guideline requires that all CBN proposals indicate an explicit evaluation function. The final guideline underscores that regardless of the strategic fit or how well a CBN is designed, it is unlikely to succeed unless both internal and external relationships are based on a solid foundation of honesty, mutual respect, and trust.

  5. Microbial network, phylogenetic diversity and community membership in the active layer across a permafrost thaw gradient.

    Science.gov (United States)

    Mondav, Rhiannon; McCalley, Carmody K; Hodgkins, Suzanne B; Frolking, Steve; Saleska, Scott R; Rich, Virginia I; Chanton, Jeff P; Crill, Patrick M

    2017-08-01

    Biogenic production and release of methane (CH 4 ) from thawing permafrost has the potential to be a strong source of radiative forcing. We investigated changes in the active layer microbial community of three sites representative of distinct permafrost thaw stages at a palsa mire in northern Sweden. The palsa site (intact permafrost and low radiative forcing signature) had a phylogenetically clustered community dominated by Acidobacteria and Proteobacteria. The bog (thawing permafrost and low radiative forcing signature) had lower alpha diversity and midrange phylogenetic clustering, characteristic of ecosystem disturbance affecting habitat filtering. Hydrogenotrophic methanogens and Acidobacteria dominated the bog shifting from palsa-like to fen-like at the waterline. The fen (no underlying permafrost, high radiative forcing signature) had the highest alpha, beta and phylogenetic diversity, was dominated by Proteobacteria and Euryarchaeota and was significantly enriched in methanogens. The Mire microbial network was modular with module cores consisting of clusters of Acidobacteria, Euryarchaeota or Xanthomonodales. Loss of underlying permafrost with associated hydrological shifts correlated to changes in microbial composition, alpha, beta and phylogenetic diversity associated with a higher radiative forcing signature. These results support the complex role of microbial interactions in mediating carbon budget changes and climate feedback in response to climate forcing. © 2017 Society for Applied Microbiology and John Wiley & Sons Ltd.

  6. Energy Management for Community Energy Network with CHP Based on Cooperative Game

    Directory of Open Access Journals (Sweden)

    Xiaofeng Liu

    2018-04-01

    Full Text Available Integrated energy system (IES has received increasing attention in micro grid due to the high energy efficiency and low emission of carbon dioxide. Based on the technology of combined heat and power (CHP, this paper develops a novel operation mechanism with community micro turbine and shared energy storage system (ESS for energy management of prosumers. In the proposed framework, micro-grid operator (MGO equipped with micro turbine and ESS provides energy selling business and ESS leasing business for prosumers. Prosumers can make energy trading with public grid and MGO, and ESS will be shared among prosumers when they pay for the rent to MGO. Based on such framework, we adopt a cooperative game for prosumers to determine optimal energy trading strategies from MGO and public grid for the next day. Concretely, a cooperative game model is formulated to search the optimal strategies aiming at minimizing the daily cost of coalition, and then a bilateral Shapley value (BSV is proposed to solve the allocation problem of coalition’s cost among prosumers. To verify the effectiveness of proposed energy management framework, a practical example is conducted with a community energy network containing MGO and 10 residential buildings. Simulation results show that the proposed scheme is able to provide financial benefits to all prosumers, while providing peak load leveling for the grid.

  7. Structure analysis and core community detection of embodied resources networks among regional industries

    Science.gov (United States)

    He, Xijun; Dong, Yanbo; Wu, Yuying; Wei, Guodan; Xing, Lizhi; Yan, Jia

    2017-08-01

    To address the double pressure of scarce resources and regional industrial isomorphism, this paper applied the concepts of exergy and embodied resources based on economic input-output (I-O) data. We constructed the embodied resources networks among the regional industries of Beijing-Tianjin-Hebei (also known as Jing-Jin-Ji) in China. We analyzed the rules of embodied resources consumption in the area's industries, identified the core community structures, and studied the characteristics of industrial homogeneity through regional comparisons. The results showed that the dependence on scarce resources of industrial operations in Beijing was less than in Jin-Ji, while the dependence on finance, technology, information, and other service resources in Beijing was higher than in Jin-Ji. The I-O efficiency of embodied resources among industries and the agglomeration of correlation relationships in industries with large embodied resources were higher than in Jin-Ji. The industrial coincidence degree in the ;bridge; industries and in the core community in Jin-Ji was higher than in Jing-Jin and Jing-Ji, which means the industrial homogeneous competition of Jin-Ji was higher, too. This study makes a significant contribution toward promoting the dislocation development of regional industries, accelerating the coordination of resources, and reducing homogeneity competition.

  8. Detecting the Community Structure and Activity Patterns of Temporal Networks: A Non-Negative Tensor Factorization Approach

    Science.gov (United States)

    Gauvin, Laetitia; Panisson, André; Cattuto, Ciro

    2014-01-01

    The increasing availability of temporal network data is calling for more research on extracting and characterizing mesoscopic structures in temporal networks and on relating such structure to specific functions or properties of the system. An outstanding challenge is the extension of the results achieved for static networks to time-varying networks, where the topological structure of the system and the temporal activity patterns of its components are intertwined. Here we investigate the use of a latent factor decomposition technique, non-negative tensor factorization, to extract the community-activity structure of temporal networks. The method is intrinsically temporal and allows to simultaneously identify communities and to track their activity over time. We represent the time-varying adjacency matrix of a temporal network as a three-way tensor and approximate this tensor as a sum of terms that can be interpreted as communities of nodes with an associated activity time series. We summarize known computational techniques for tensor decomposition and discuss some quality metrics that can be used to tune the complexity of the factorized representation. We subsequently apply tensor factorization to a temporal network for which a ground truth is available for both the community structure and the temporal activity patterns. The data we use describe the social interactions of students in a school, the associations between students and school classes, and the spatio-temporal trajectories of students over time. We show that non-negative tensor factorization is capable of recovering the class structure with high accuracy. In particular, the extracted tensor components can be validated either as known school classes, or in terms of correlated activity patterns, i.e., of spatial and temporal coincidences that are determined by the known school activity schedule. PMID:24497935

  9. Metacommunity theory as a multispecies, multiscale framework for studying the influence of river network structure on riverine communities and ecosystems

    Science.gov (United States)

    Brown, B.L.; Swan, C.M.; Auerbach, D.A.; Campbell, Grant E.H.; Hitt, N.P.; Maloney, K.O.; Patrick, C.

    2011-01-01

    Explaining the mechanisms underlying patterns of species diversity and composition in riverine networks is challenging. Historically, community ecologists have conceived of communities as largely isolated entities and have focused on local environmental factors and interspecific interactions as the major forces determining species composition. However, stream ecologists have long embraced a multiscale approach to studying riverine ecosystems and have studied both local factors and larger-scale regional factors, such as dispersal and disturbance. River networks exhibit a dendritic spatial structure that can constrain aquatic organisms when their dispersal is influenced by or confined to the river network. We contend that the principles of metacommunity theory would help stream ecologists to understand how the complex spatial structure of river networks mediates the relative influences of local and regional control on species composition. From a basic ecological perspective, the concept is attractive because new evidence suggests that the importance of regional processes (dispersal) depends on spatial structure of habitat and on connection to the regional species pool. The role of local factors relative to regional factors will vary with spatial position in a river network. From an applied perspective, the long-standing view in ecology that local community composition is an indicator of habitat quality may not be uniformly applicable across a river network, but the strength of such bioassessment approaches probably will depend on spatial position in the network. The principles of metacommunity theory are broadly applicable across taxa and systems but seem of particular consequence to stream ecology given the unique spatial structure of riverine systems. By explicitly embracing processes at multiple spatial scales, metacommunity theory provides a foundation on which to build a richer understanding of stream communities.

  10. Local Observations, Global Connections: An Educational Program Using Ocean Networks Canada's Community-Based Observatories

    Science.gov (United States)

    Pelz, M.; Hoeberechts, M.; Ewing, N.; Davidson, E.; Riddell, D. J.

    2014-12-01

    Schools on Canada's west coast and in the Canadian Arctic are participating in the pilot year of a novel educational program based on analyzing, understanding and sharing ocean data collected by cabled observatories. The core of the program is "local observations, global connections." First, students develop an understanding of ocean conditions at their doorstep through the analysis of community-based observatory data. Then, they connect that knowledge with the health of the global ocean by engaging with students at other schools participating in the educational program and through supplemental educational resources. Ocean Networks Canada (ONC), an initiative of the University of Victoria, operates cabled ocean observatories which supply continuous power and Internet connectivity to a broad suite of subsea instruments from the coast to the deep sea. This Internet connectivity permits researchers, students and members of the public to download freely available data on their computers anywhere around the globe, in near real-time. In addition to the large NEPTUNE and VENUS cabled observatories off the coast of Vancouver Island, British Columbia, ONC has been installing smaller, community-based cabled observatories. Currently two are installed: one in Cambridge Bay, Nunavut and one at Brentwood College School, on Mill Bay in Saanich Inlet, BC. Several more community-based observatories are scheduled for installation within the next year. The observatories support a variety of subsea instruments, such as a video camera, hydrophone and water quality monitor and shore-based equipment including a weather station and a video camera. Schools in communities hosting an observatory are invited to participate in the program, alongside schools located in other coastal and inland communities. Students and teachers access educational material and data through a web portal, and use video conferencing and social media tools to communicate their findings. A series of lesson plans

  11. Community pharmacist participation in a practice-based research network: a report from the Medication Safety Research Network of Indiana (Rx-SafeNet).

    Science.gov (United States)

    Patel, Puja; Hemmeger, Heather; Kozak, Mary Ann; Gernant, Stephanie A; Snyder, Margie E

    2015-01-01

    To describe the experiences and opinions of pharmacists serving as site coordinators for the Medication Safety Research Network of Indiana (Rx-SafeNet). Retail chain, independent, and hospital/health system outpatient community pharmacies throughout Indiana, with a total of 127 pharmacy members represented by 26 site coordinators. Rx-SafeNet, a statewide practice-based research network (PBRN) formed in 2010 and administered by the Purdue University College of Pharmacy. Barriers and facilitators to participation in available research studies, confidence participating in research, and satisfaction with overall network communication. 22 of 26 site coordinators participated, resulting in an 85% response rate. Most (72.2%) of the respondents had received a doctor of pharmacy degree, and 13.6% had postgraduate year (PGY)1 residency training. The highest reported benefits of PBRN membership were an enhanced relationship with the Purdue University College of Pharmacy (81% agreed or strongly agreed) and enhanced professional development (80% agreed or strongly agreed). Time constraints were identified as the greatest potential barrier to network participation, reported by 62% of respondents. In addition, the majority (59%) of survey respondents identified no prior research experience. Last, respondents' confidence in performing research appeared to increase substantially after becoming network members, with 43% reporting a lack of confidence in engaging in research before joining the network compared with 90% reporting confidence after joining the network. In general, Rx-SafeNet site coordinators appeared to experience increased confidence in research engagement after joining the network. While respondents identified a number of benefits associated with network participation, concerns about potential time constraints remained a key barrier to participation. These findings will assist network leadership in identifying opportunities to positively increase member participation

  12. The relations between network-operation and topological-property in a scale-free and small-world network with community structure

    Science.gov (United States)

    Ma, Fei; Yao, Bing

    2017-10-01

    It is always an open, demanding and difficult task for generating available model to simulate dynamical functions and reveal inner principles from complex systems and networks. In this article, due to lots of real-life and artificial networks are built from series of simple and small groups (components), we discuss some interesting and helpful network-operation to generate more realistic network models. In view of community structure (modular topology), we present a class of sparse network models N(t , m) . At the moment, we capture the fact the N(t , 4) has not only scale-free feature, which means that the probability that a randomly selected vertex with degree k decays as a power-law, following P(k) ∼k-γ, where γ is the degree exponent, but also small-world property, which indicates that the typical distance between two uniform randomly chosen vertices grows proportionally to logarithm of the order of N(t , 4) , namely, relatively shorter diameter and lower average path length, simultaneously displays higher clustering coefficient. Next, as a new topological parameter correlating to reliability, synchronization capability and diffusion properties of networks, the number of spanning trees over a network is studied in more detail, an exact analytical solution for the number of spanning trees of the N(t , 4) is obtained. Based on the network-operation, part hub-vertex linking with each other will be helpful for structuring various network models and investigating the rules related with real-life networks.

  13. [Community mental health networks and doors at a Health and Community Center (CeSAC No 24) in the south of the Autonomous City of Buenos Aires (CABA)].

    Science.gov (United States)

    Corin, Marcela

    2013-01-01

    The history of the Health and Community Center (CeSAC) No 24 began 21 years ago in the Ramón Carrillo district, in Villa Soldati neighborhood. This is a marginal urban area in the south of the CABA with a population with urgent needs. The work started with a group of professionals dealing with primary care practices, and it continues up to the present. The goal of our community mental health project is the creation of spaces for prevention and well-being including art in all its expressions, with the active participation of neighbors in the coordination of many activities. Through the symbolic opening of doors and making networks visible, we opened the CeSAC to universities, scientific societies, foundations, NGOs, neighbor committees, neighborhood boards, human rights organizations, formal and informal education centers, cultural centers, churches, community radios, in order to show the vital movements of the community, as we -as a team- are an integral part of it. Our spaces, workshops and programs are a collective socio-historical construction that achieves milestones such as thinking, thinking of us, getting together and achieving the participation from the community and the continuous training for everybody working and living in Soldati. The decentralization and democratization trend, and the heterarchical management of our organization favors the possibility of catalyzing the wishes, motivations and efforts of the healthcare team, neighbors and network nodes, thus benefiting the creative work that turns a dream into a hopeful reality.

  14. Measuring the impact of spatial network layout on community social cohesion: a cross-sectional study.

    Science.gov (United States)

    Cooper, Crispin H V; Fone, David L; Chiaradia, Alain J F

    2014-04-11

    There is now a substantial body of research suggesting that social cohesion, a collective characteristic measured by the levels of trust, reciprocity and formation of strong social bonds within communities, is an important factor in determining health. Of particular interest is the extent to which factors in the built environment facilitate, or impede, the development of social bonds. Severance is a characteristic of physical environments which is hypothesized to inhibit cohesion. In the current study we test a number of characteristics of spatial networks which could be hypothesized to relate either to severance, or directly to community cohesion. Particular focus is given to our most promising variable for further analysis (Convex Hull Maximum Radius 600 m). In the current study we analysed social cohesion as measured at Enumeration District level, aggregated from a survey of 10,892 individuals aged 18 to 74 years in the Caerphilly Health and Social Needs Cohort Study, 2001. In a data mining process we test 16 network variables on multiple scales. The variable showing the most promise is validated in a test on an independent data set. We then conduct a multivariate regression also including Townsend deprivation scores and urban/rural status as predictor variables for social cohesion. We find convex hull maximum radius at a 600 m scale to have a small but highly significant correlation with social cohesion on both data sets. Deprivation has a stronger effect. Splitting the analysis by tertile of deprivation, we find that the effect of severance as measured by this variable is strongest in the most deprived areas. A range of spatial scales are tested, with the strongest effects being observed at scales that match typical walking distances. We conclude that physical connectivity as measured in this paper has a significant effect on social cohesion, and that our measure is unlikely to proxy either deprivation or the urban/rural status of communities. Possible

  15. Efficacy of community-based physiotherapy networks for patients with Parkinson's disease: a cluster-randomised trial.

    NARCIS (Netherlands)

    Munneke, M.; Nijkrake, M.J.; Keus, S.H.J.; Kwakkel, G.; Berendse, H.W.; Roos, R.A.; Borm, G.F.; Adang, E.M.M.; Overeem, S.; Bloem, B.R.

    2010-01-01

    BACKGROUND: Many patients with Parkinson's disease are treated with physiotherapy. We have developed a community-based professional network (ParkinsonNet) that involves training of a selected number of expert physiotherapists to work according to evidence-based recommendations, and structured

  16. Efficacy of community-based physiotherapy networks for patients with Parkinson's disease: a cluster-randomised trial

    NARCIS (Netherlands)

    Munneke, M.; Nijkrake, M.J.; Keus, S.H.; Kwakkel, G.; Berendse, H.W.; Roos, R.A.; Borm, G.F.; Adang, E.M.; Overeem, S.; Bloem, B.R.

    2010-01-01

    Background: Many patients with Parkinson's disease are treated with physiotherapy. We have developed a community-based professional network (ParkinsonNet) that involves training of a selected number of expert physiotherapists to work according to evidence-based recommendations, and structured

  17. Network Correlates of Sexual Health Advice Seeking and Substance Use among Members of the Los Angeles House and Ball Communities

    Science.gov (United States)

    Holloway, Ian W.; Schrager, Sheree M.; Wong, Carolyn F.; Dunlap, Shannon L.; Kipke, Michele D.

    2014-01-01

    House and Ball communities (HBCs), represent a prime context for human immunodeficiency virus prevention with African American young men who have sex with men and transgender persons. This study sought to understand the composition and function of social support and sexual networks of HBC members in Los Angeles, California (N = 263). Participants…

  18. Ordination of self-organizing feature map neural networks and its application to the study of plant communities

    Institute of Scientific and Technical Information of China (English)

    Jintun ZHANG; Dongping MENG; Yuexiang XI

    2009-01-01

    A self-organizing feature map (SOFM) neural network is a powerful tool in analyzing and solving complex, non-linear problems. According to its features, a SOFM is entirely compatible with ordination studies of plant communities. In our present work, mathematical principles, and ordination techniques and procedures are introduced. A SOFM ordination was applied to the study of plant communities in the middle of the Taihang mountains. The ordination was carried out by using the NNTool box in MATLAB. The results of 68 quadrats of plant communities were distributed in SOFM space. The ordination axes showed the ecological gradients clearly and provided the relationships between communities with ecological meaning. The results are consistent with the reality of vegetation in the study area. This suggests that SOFM ordination is an effective technique in plant ecology. During ordination procedures, it is easy to carry out clustering of communities and so it is beneficial for combining classification and ordination in vegetation studies.

  19. Communication settings for the empowerment of communities: international comparison between networks of solidarity economy in Brazil and Italy

    Directory of Open Access Journals (Sweden)

    Carmela Guarascio

    2016-12-01

    Full Text Available This article presents a qualitative comparison between two communities in Brazil and Italy, which host a solidarity organization. These communities promote sustainable and local development, empowering their residents, building an alternative way of producing, in a form of plural economy. Financial sustainability comes from public and private aid, and this can affect the durability of communities by making them dependent on funds. This condition weakens the possibility of local development. To develop this research we used the qualitative approach, and data were collected through structured and unstructured interviews, in addition to the participatory observation. The results demonstrate that the method of communication between solidarity economy networks and investors is relevant, because sometimes those who offer funds can contribute to building development conditions that are not truly empowering for the community. It is concluded that an adequate method of communicating the information necessary for community development can contribute to local empowerment, including possible problems of financial sustainability.

  20. Building ties: social capital network analysis of a forest community in a biosphere reserve in Chiapas, Mexico

    Directory of Open Access Journals (Sweden)

    Luis Rico García-Amado

    2012-09-01

    Full Text Available Governance of the commons depends on the capacity to generate collective action. Networks and rules that foster that collective action have been defined as social capital. However, their causal link is still not fully understood. We use social network analysis to assess social capital, decision-making, and collective action in a forest-based common pool resource management in La Sepultura Biosphere Reserve (Chiapas, Mexico. Our research analyzes the productive networks and the evolution of coffee groups in one community. The network shows some centrality, with richer landholders tending to occupy core positions and poorer landless peasants occupying peripheral ones. This has fostered the community's environmentally oriented development but has also caused internal conflicts. Market requirements have shaped different but complementary productive networks, where organic coffee commercialization is the main source of bridging ties, which has resulted in more connectivity and resilience. Conservation attitudes, along with the institutional setting of the community, have promoted collective action. The unresolved conflicts, however, still leave some concerns about governance in the future.