WorldWideScience

Sample records for oklahoma community networks

  1. ("un")Doing the Next Generation Science Standards: Climate Change Education Actor-Networks in Oklahoma

    Science.gov (United States)

    Colston, Nicole M.; Ivey, Toni A.

    2015-01-01

    This exploratory research investigated how science education communities of practice in Oklahoma engage in translations of climate change education (CCE). Applications of actor-network theory to educational policymaking facilitate this analysis of the spaces of prescription and spaces of negotiation that characterize CCE in Oklahoma. Informed by…

  2. Networked Observation of Precipitating Cloud Systems in Oklahoma

    Science.gov (United States)

    Collis, S. M.; Giangrande, S. E.; Bharadwaj, N.

    2012-12-01

    Radars are inherently limited in their ability to resolve fine structure of cloud systems and completely image a volume of space. Both the radial nature of sampling and the issues of beam width mean that upper level features are often missed or poorly resolved. While constant azimuth scans (RHIs) give amazing insight into the vertical structure they are not capable of sampling full storm structure in within a time commensurate with the evolution of the storm system. This presentation will show results from the ARM multi-scale remote sensing facility in Lamont, Oklahoma where there is a network of three X-Band and a C-Band radar deployed. Taking care in quality control and using a flexible mapping methodology enables the combining of information from multiple sources. We will showcase some sample storm reconstructions highlighting the advantages of using the full capabilities of the observing system.

  3. Infant Toddler Services through Community Collaboration: Oklahoma's Early Childhood Initiatives

    Science.gov (United States)

    Goble, Carla B.; Horm, Diane M.

    2009-01-01

    Comprehensive, integrated services for infants, toddlers, and families are essential for optimal child development, and collaboration across systems is increasingly important to maximize limited resources. The authors describe three successful initiatives in Oklahoma that use a collaborative systems approach to providing direct services to young…

  4. Journalism in the Community Classroom: A Curriculum Model for Cultural Journalism in Oklahoma.

    Science.gov (United States)

    Howard, Linda C.

    This paper introduces the medium of cultural journalism as an effective means of intensified basic communication training and community involvement. Part one contains a report of a needs assessment and a subsequent pilot project on cultural journalism that was conducted at an Oklahoma high school. The needs assessment also reports on similar…

  5. Financial Aid and Persistence in Community Colleges: Assessing the Effectiveness of Federal and State Financial Aid Programs in Oklahoma

    Science.gov (United States)

    Mendoza, Pilar; Mendez, Jesse P.; Malcolm, Zaria

    2009-01-01

    Using a longitudinal, state-wide dataset, this study assessed the effect of financial aid on the persistence of full-time students in associate's degree programs at the Oklahoma community colleges. Three financial-aid sources were examined: the Oklahoma Higher Learning Access Program (OHLAP), Pell grants, and Stafford loans. Results indicate that…

  6. The Community Networking Handbook.

    Science.gov (United States)

    Bajjaly, Stephen T.

    This publication outlines the complete community networking process: planning, developing partnerships, funding, marketing, content, public access, and evaluation, and discusses the variety of roles that the local public library can play in this process. Chapter One, "The Importance of Community Networking," describes the importance of community…

  7. 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.

  8. Predictability of littoral-zone fish communities through ontogeny in Lake Texoma, Oklahoma-Texas, USA

    Science.gov (United States)

    Eggleton, M.A.; Ramirez, R.; Hargrave, C.W.; Gido, K.B.; Masoner, J.R.; Schnell, G.D.; Matthews, W.J.

    2005-01-01

    We sampled larval, juvenile and adult fishes from littoral-zone areas of a large reservoir (Lake Texoma, Oklahoma-Texas) (1) to characterize environmental factors that influenced fish community structure, (2) to examine how consistent fish-environment relationships were through ontogeny (i.e., larval vs. juvenile and adult), and (3) to measure the concordance of larval communities sampled during spring to juvenile and adult communities sampled at the same sites later in the year. Larval, juvenile and adult fish communities were dominated by Atherinidae (mainly inland silverside, Menidia beryllina) and Moronidae (mainly juvenile striped bass, Morone saxatilis) and were consistently structured along a gradient of site exposure to prevailing winds and waves. Larval, juvenile and adult communities along this gradient varied from atherinids and moronids at highly exposed sites to mostly centrarchids (primarily Lepomis and Micropterus spp.) at protected sites. Secondarily, zooplankton densities, water clarity, and land-use characteristics were related to fish community structure. Rank correlation analyses and Mantel tests indicated that the spatial consistency and predictability of fish communities was high as larval fishes sampled during spring were concordant with juvenile and adult fishes sampled at the same sites during summer and fall in terms of abundance, richness, and community structure. We propose that the high predictability and spatial consistency of littoral-zone fishes in Lake Texoma was a function of relatively simple communities (dominated by 1-2 species) that were structured by factors, such as site exposure to winds and waves, that varied little through time. ?? Springer 2005.

  9. Multiple Inclusion and Community Networks

    NARCIS (Netherlands)

    I.M. Bogenrieder (Irma); P.J. van Baalen (Peter)

    2004-01-01

    textabstractCommunity membership has changed over the last decades. Most people participate in different communities simultaneously in order to satisfy different individual interests. This network individualism might threaten the sustainability of modern communities, like communities of practice (Co

  10. Community extraction for social networks

    CERN Document Server

    Zhao, Yunpeng; Zhu, Ji

    2010-01-01

    Analysis of networks and in particular discovering communities within networks has been a focus of recent work in several fields, with applications ranging from citation and friendship networks to food webs and gene regulatory networks. Most of the existing community detection methods focus on partitioning the entire network into communities, with the expectation of many ties within communities and few ties between. However, many networks contain nodes that do not fit in with any of the communities, and forcing every node into a community can distort results. Here we propose a new framework that focuses on community extraction instead of partition, extracting one community at a time. The main idea behind extraction is that the strength of a community should not depend on ties between members of other communities, but only on ties within that community and its ties to the outside world. We show that the new extraction criterion performs well on simulated and real networks, and establish asymptotic consistency ...

  11. Networks and the fiscal performance of rural hospitals in Oklahoma: are they associated?

    Science.gov (United States)

    Broyles, R W; Brandt, E N; Biard-Holmes, D

    1998-01-01

    This paper uses regression analysis to explore the relation of network membership to the financial performance of rural hospitals in Oklahoma during fiscal year 1995. After adjusting for the scope of service, as measured by the number of facilities or services offered by the hospital, indicators of fiscal status are (1) the cash receipts derived from net patient revenue; (2) the cash disbursements related to operating costs, net of interest and depreciation expense, labor costs and nonlabor costs; and (3) net cash flow, defined as the difference between cash receipts and disbursements. Controlling for the effects of the hospital's structural attributes, operating characteristics and market conditions, the results indicate that members of a network reported lower net operating costs, labor costs and nonlabor expenses per service than nonmembers. Hence, the analysis seems to suggest that the membership of rural hospitals in a network is associated with lower cash disbursements and an improved net cash flow, outcomes that may preserve their fiscal viability and the access of the population at risk to service.

  12. Communities unfolding in multislice networks

    CERN Document Server

    Carchiolo, Vincenza; Malgeri, Michele; Mangioni, Giuseppe

    2016-01-01

    Discovering communities in complex networks helps to understand the behaviour of the network. Some works in this promising research area exist, but communities uncovering in time-dependent and/or multiplex networks has not deeply investigated yet. In this paper, we propose a communities detection approach for multislice networks based on modularity optimization. We first present a method to reduce the network size that still preserves modularity. Then we introduce an algorithm that approximates modularity optimization (as usually adopted) for multislice networks, thus finding communities. The network size reduction allows us to maintain acceptable performances without affecting the effectiveness of the proposed approach.

  13. Full Wavefield Recordings of Oklahoma Seismicity from an IRIS-led Community Experiment

    Science.gov (United States)

    Anderson, K. R.; Woodward, R.; Sweet, J. R.; Bilek, S. L.; Brudzinski, M.; Chen, X.; DeShon, H. R.; Karplus, M. S.; Keranen, K. M.; Langston, C. A.; Lin, F. C.; Magnani, M. B.; Stump, B. W.

    2016-12-01

    In June 2016, a field crew of students, faculty, industry personnel and IRIS staff deployed several hundred stations above an active seismic lineament in north-central Oklahoma, with the goal to advance our understanding of general seismicity and earthquake source processes using arrays designed to capture full wavefield seismic data. In addition, we used this as an educational opportunity to extend the experience with nodal type experiment planning and execution. IRIS selected 30 graduate students from 18 different US and foreign based institutions to participate in the deployment. In addition, IRIS was pleased to have the assistance of several individuals from the Oklahoma Geological Survey. The crew deployed 363 3C 5Hz Generation 2 Fairfield Z-Land nodes along three seismic lines and in a seven-layer nested gradiometer array. The seismic lines spanned a region 13 km long by 5 km wide. The nested gradiometer was designed to measure the full seismic wavefield using standard frequency-wavenumber techniques and spatial wave gradients. A broadband, 18 station "Golay 3x6" array was deployed around the gradiometer and seismic lines with an aperture of approximately 5 km to collect waveform data from local and regional events. In addition, 9 infrasound stations were deployed in order to capture and identify acoustic events that might be recorded by the seismic arrays and to quantify the wind acoustic noise effect on co-located broadband stations. The variety of instrumentation used in this deployment was chosen to capture the full seismic wavefield generated by the local and regional seismicity beneath the array and the surrounding region. A demobilization team returned to the sites in mid-July to recover the nodes, after a full month of deployment. The broadband and infrasound stations will remain in place through September to capture any additional local and regional seismicity. This experiment was designed by and for the seismological community. The experiment was

  14. REMARKS ON NETWORK COMMUNITY PROPERTIES

    Institute of Scientific and Technical Information of China (English)

    2008-01-01

    This paper discusses a popular community definition in complex network research in terms of the conditions under which a community is minimal,that is,the community cannot be split into several smaller communities or split and reorganized with other network elements into new communities.The result provides a base on which further optimization computation of the quantitative measure for community identification can be realized.

  15. Finding communities in sparse networks

    CERN Document Server

    Singh, Abhinav

    2015-01-01

    Spectral algorithms based on matrix representations of networks are often used to detect communities but classic spectral methods based on the adjacency matrix and its variants fail to detect communities in sparse networks. New spectral methods based on non-backtracking random walks have recently been introduced that successfully detect communities in many sparse networks. However, the spectrum of non-backtracking random walks ignores hanging trees in networks that can contain information about the community structure of networks. We introduce the reluctant backtracking operators that explicitly account for hanging trees as they admit a small probability of returning to the immediately previous node unlike the non-backtracking operators that forbid an immediate return. We show that the reluctant backtracking operators can detect communities in certain sparse networks where the non-backtracking operators cannot while performing comparably on benchmark stochastic block model networks and real world networks. We...

  16. 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

  17. Dynamical detection of network communities

    Science.gov (United States)

    Quiles, Marcos G.; Macau, Elbert E. N.; Rubido, Nicolás

    2016-05-01

    A prominent feature of complex networks is the appearance of communities, also known as modular structures. Specifically, communities are groups of nodes that are densely connected among each other but connect sparsely with others. However, detecting communities in networks is so far a major challenge, in particular, when networks evolve in time. Here, we propose a change in the community detection approach. It underlies in defining an intrinsic dynamic for the nodes of the network as interacting particles (based on diffusive equations of motion and on the topological properties of the network) that results in a fast convergence of the particle system into clustered patterns. The resulting patterns correspond to the communities of the network. Since our detection of communities is constructed from a dynamical process, it is able to analyse time-varying networks straightforwardly. Moreover, for static networks, our numerical experiments show that our approach achieves similar results as the methodologies currently recognized as the most efficient ones. Also, since our approach defines an N-body problem, it allows for efficient numerical implementations using parallel computations that increase its speed performance.

  18. Community Detection in Complex Networks

    Institute of Scientific and Technical Information of China (English)

    Nan Du; Bai Wang; Bin Wu

    2008-01-01

    With the rapidly growing evidence that various systems in nature and society can be modeled as complex networks, community detection in networks becomes a hot research topic in physics, sociology, computer society, etc. Although this investigation of community structures has motivated many diverse algorithms, most of them are unsuitable when dealing with large networks due to their computational cost. In this paper, we present a faster algorithm ComTeetor,which is more efficient for the community detection in large complex networks based on the nature of overlapping cliques.This algorithm does not require any priori knowledge about the number or the original division of the communities. With respect to practical applications, ComTector is challenging with five different types of networks including the classic Zachary Karate Club, Scientific Collaboration Network, South Florida Free Word Association Network, Urban Traffic Network, North America Power Grid and the Telecomnmnication Call Network. Experimental results show that our algorithm can discover meaningful communities that meet both the objective basis and our intuitions.

  19. Tracing the flow: Climate change actor-networks in Oklahoma secondary science education

    Science.gov (United States)

    Colston, Nicole Marie

    This dissertation reports research about the translation of climate change in science education. Public controversies about climate change education raises questions about the lived experiences of teachers in Oklahoma and the role of science education in increasing public understanding. A mixed methods research design included rhetorical analysis of climate change denial media, key informant interviews with science education stakeholders, and a survey questionnaire of secondary science teachers. Final analysis was further informed by archival research and supplemented by participant observation in state-wide meetings and science teacher workshops. The results are organized into three distinct manuscripts intended for publication across the fields of communication, science education, and climate science. As a whole the dissertation answers the research question, how does manufactured scientific controversy about climate change present specific challenges and characterize negotiations in secondary science education in Oklahoma? Taken together, the findings suggest that manufactured controversy about climate change introduces a logic of non-problematicity, challenges science education policy making, and undermines scientific consensus about global warming.

  20. Discovering Network Structure Beyond Communities

    CERN Document Server

    Nishikawa, Takashi; 10.1038/srep00151

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  1. Discovering network structure beyond communities.

    Science.gov (United States)

    Nishikawa, Takashi; Motter, Adilson E

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes characterized by common network properties, including but not limited to communities of densely connected nodes. Without any prior information about the nature of the groups, the method simultaneously identifies the number of groups, the group assignment, and the properties that define these groups. The results of applying our method to real networks suggest the possibility that most group structures lurk undiscovered in the fast-growing inventory of social, biological, and technological networks of scientific interest.

  2. Bayesian Overlapping Community Detection in Dynamic Networks

    CERN Document Server

    Ghorbani, Mahsa; Khodadadi, Ali

    2016-01-01

    Detecting community structures in social networks has gained considerable attention in recent years. However, lack of prior knowledge about the number of communities, and their overlapping nature have made community detection a challenging problem. Moreover, many of the existing methods only consider static networks, while most of real world networks are dynamic and evolve over time. Hence, finding consistent overlapping communities in dynamic networks without any prior knowledge about the number of communities is still an interesting open research problem. In this paper, we present an overlapping community detection method for dynamic networks called Dynamic Bayesian Overlapping Community Detector (DBOCD). DBOCD assumes that in every snapshot of network, overlapping parts of communities are dense areas and utilizes link communities instead of common node communities. Using Recurrent Chinese Restaurant Process and community structure of the network in the last snapshot, DBOCD simultaneously extracts the numbe...

  3. Industrial extension, the Oklahoma way

    Science.gov (United States)

    Farrell, Edmund J.

    1994-03-01

    Oklahoma has established a customer-driven industrial extension system. A publicly-chartered, private non-profit corporation, the Oklahoma Alliance for Manufacturing Excellence, Inc. (`the Alliance') coordinates the system. The system incorporates principles that Oklahoma manufacturers value: (1) decentralization and local accessibility; (2) coordinated existing resources; (3) comprehensive help; (4) interfirm cooperation; (5) pro-active outreach; (6) self- help and commitment from firms; (7) customer governance; and (8) performance accountability. The Oklahoma system consists of: (1) a network of locally-based broker/agents who work directly with manufacturers to diagnose problems and find appropriate assistance; (2) a group of industry sector specialists who collect and disseminate sector specific technological and market intelligence to the broker/agents and their clients; (3) all the specialized public and private sector resources coordinated by the system; and (4) a customer- driven coordination and evaluation mechanism, the Alliance.

  4. Significant communities in large sparse networks

    CERN Document Server

    Mirshahvalad, Atieh; Derlen, Mattias; Rosvall, Martin

    2011-01-01

    Researchers use community-detection algorithms to reveal large-scale organization in biological and social networks, but community detection is useful only if the communities are significant and not a result of noisy data. To assess the statistical significance of the network communities, or the robustness of the detected structure, one approach is to perturb the network structure by removing links and measure how much the communities change. However, perturbing sparse networks is challenging because they are inherently sensitive; they shatter easily if links are removed. Here we propose a simple method to perturb sparse networks and assess the significance of their communities. We generate resampled networks by adding extra links based on local information, then we aggregate the information from multiple resampled networks to find a coarse-grained description of significant clusters. In addition to testing our method on benchmark networks, we use our method on the sparse network of the European Court of Just...

  5. Identifying Community Structures in Dynamic Networks

    CERN Document Server

    Alvari, Hamidreza; Sukthankar, Gita; Lakkaraju, Kiran

    2016-01-01

    Most real-world social networks are inherently dynamic, composed of communities that are constantly changing in membership. To track these evolving communities, we need dynamic community detection techniques. This article evaluates the performance of a set of game theoretic approaches for identifying communities in dynamic networks. Our method, D-GT (Dynamic Game Theoretic community detection), models each network node as a rational agent who periodically plays a community membership game with its neighbors. During game play, nodes seek to maximize their local utility by joining or leaving the communities of network neighbors. The community structure emerges after the game reaches a Nash equilibrium. Compared to the benchmark community detection methods, D-GT more accurately predicts the number of communities and finds community assignments with a higher normalized mutual information, while retaining a good modularity.

  6. Matching Community Structure Across Online Social Networks

    CERN Document Server

    Li, Lin

    2016-01-01

    The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across these networks. However, in reality, users typically identify themselves with different usernames across social media sites. This creates a great difficulty in detecting the community structure. In this paper, we explore several approaches for community detection across online social networks with limited knowledge of username alignment across the networks. We refer to the known alignment of usernames as seeds. We investigate strategies for seed selection and its impact on networks with a different fraction of overlapping vertices. The goal is to study the interplay between network topologies and seed selection strategies, and to understand how it affects the detected community structu...

  7. Influence of Inter-Community Link Strength on Community Networks

    Science.gov (United States)

    Peng, Qian-Jin; Zhao, Ming; Zhang, Hai-Feng

    2013-09-01

    This paper mainly discusses how the strength of inter-community links affects the synchronization state of the whole network and the individual community. It is found, when the inter-community link number is neither too small nor too large and the overall coupling strength takes right values, there will be an optimal inter-community link strength value, in which the community networks are in a balance region where the individual community is maximally independent, while the information transmission remains effective among different communities. Combining the result of this paper and that of the number of inter-community links, it is easy to help us find the most effective community networks.

  8. Community structure of complex networks based on continuous neural network

    Science.gov (United States)

    Dai, Ting-ting; Shan, Chang-ji; Dong, Yan-shou

    2017-09-01

    As a new subject, the research of complex networks has attracted the attention of researchers from different disciplines. Community structure is one of the key structures of complex networks, so it is a very important task to analyze the community structure of complex networks accurately. In this paper, we study the problem of extracting the community structure of complex networks, and propose a continuous neural network (CNN) algorithm. It is proved that for any given initial value, the continuous neural network algorithm converges to the eigenvector of the maximum eigenvalue of the network modularity matrix. Therefore, according to the stability of the evolution of the network symbol will be able to get two community structure.

  9. Virtual communities, social networks and collaboration

    CERN Document Server

    Lazakidou, Athina A

    2012-01-01

    Social networks and virtual communities are often in the news, either being censored or facilitating academic cooperation. Here, leading researchers cover cutting-edge topics such as the requirements for effective collaboration in on-line communities.

  10. Community-enhanced Network Representation Learning for Network Analysis

    CERN Document Server

    Tu, Cunchao; Zeng, Xiangkai; Liu, Zhiyuan; Sun, Maosong

    2016-01-01

    Network representation learning (NRL) aims to build low-dimensional vectors for vertices in a network. Most existing NRL methods focus on learning representations from local context of vertices (such as their neighbors). Nevertheless, vertices in many complex networks also exhibit significant global patterns widely known as communities. It's a common sense that vertices in the same community tend to connect densely, and usually share common attributes. These patterns are expected to improve NRL and benefit relevant evaluation tasks, such as link prediction and vertex classification. In this work, we propose a novel NRL model by introducing community information of vertices to learn more discriminative network representations, named as Community-enhanced Network Representation Learning (CNRL). CNRL simultaneously detects community distribution of each vertex and learns embeddings of both vertices and communities. In this way, we can obtain more informative representation of a vertex accompanying with its commu...

  11. Community Extraction in Multilayer Networks with Heterogeneous Community Structure

    CERN Document Server

    Wilson, James D; Bhamidi, Shankar; Nobel, Andrew B

    2016-01-01

    Multilayer networks are a useful way to capture and model multiple, binary relationships among a fixed group of objects. While community detection has proven to be a useful exploratory technique for the analysis of single-layer networks, the development of community detection methods for multilayer networks is still in its infancy. We propose and investigate a procedure, called Multilayer Extraction, that identifies densely connected vertex-layer sets in multilayer networks. Multilayer Extraction makes use of a significance based score that quantifies the connectivity of an observed vertex-layer set by comparison with a multilayer fixed degree random graph model. Unlike existing detection methods, Multilayer Extraction handles networks with heterogeneous layers where community structure may be different from layer to layer. The procedure is able to capture overlapping communities, and it identifies background vertex-layer pairs that do not belong to any community. We establish large-graph consistency of the v...

  12. 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…

  13. 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;…

  14. 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...... is hinged on a research aimed at understanding how and 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 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....

  15. Matching Community Structure Across Online Social Networks

    OpenAIRE

    Lin LI; Campbell, W. M.

    2016-01-01

    The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across these networks. However, in reality, users typically identify themselves with different usernames across social media sites. This creates a great difficulty in detecti...

  16. The Community Science Workshop Network Story: Becoming a Networked Organization

    Science.gov (United States)

    St. John, Mark

    2014-01-01

    The Community Science Workshops (CSWs)--with funding from the S.D. Bechtel, Jr. Foundation, and the Gordon and Betty Moore Foundation--created a network among the CSW sites in California. The goals of the CSW Network project have been to improve programs, build capacity throughout the Network, and establish new sites. Inverness Research has been…

  17. Online community detection for large complex networks.

    Directory of Open Access Journals (Sweden)

    Gang Pan

    Full Text Available Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information. The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance.

  18. Overlapping Community Detection based on Network Decomposition

    Science.gov (United States)

    Ding, Zhuanlian; Zhang, Xingyi; Sun, Dengdi; Luo, Bin

    2016-04-01

    Community detection in complex network has become a vital step to understand the structure and dynamics of networks in various fields. However, traditional node clustering and relatively new proposed link clustering methods have inherent drawbacks to discover overlapping communities. Node clustering is inadequate to capture the pervasive overlaps, while link clustering is often criticized due to the high computational cost and ambiguous definition of communities. So, overlapping community detection is still a formidable challenge. In this work, we propose a new overlapping community detection algorithm based on network decomposition, called NDOCD. Specifically, NDOCD iteratively splits the network by removing all links in derived link communities, which are identified by utilizing node clustering technique. The network decomposition contributes to reducing the computation time and noise link elimination conduces to improving the quality of obtained communities. Besides, we employ node clustering technique rather than link similarity measure to discover link communities, thus NDOCD avoids an ambiguous definition of community and becomes less time-consuming. We test our approach on both synthetic and real-world networks. Results demonstrate the superior performance of our approach both in computation time and accuracy compared to state-of-the-art algorithms.

  19. Preserving Communities in Anonymized Social Networks

    Directory of Open Access Journals (Sweden)

    Alina Campan

    2015-04-01

    Full Text Available Social media and social networks are embedded in our society to a point that could not have been imagined only ten years ago. Facebook, LinkedIn, and Twitter are already well known social networks that have a large audience in all age groups. The amount of data that those social sites gather from their users is continually increasing and this data is very valuable for marketing, research, and various other purposes. At the same time, this data usually contain a significant amount of sensitive information which should be protected against unauthorized disclosure. To protect the privacy of individuals, this data must be anonymized such that the risk of re-identification of specific individuals is very low. In this paper we study if anonymized social networks preserve existing communities from the original social networks. To perform this study, we introduce two approaches to measure the community preservation between the initial network and its anonymized version. In the first approach we simply count how many nodes from the original communities remained in the same community after the processes of anonymization and de-anonymization. In the second approach we consider the community preservation for each node individually. Specifically, for each node, we compare the original and final communities to which the node belongs. To anonymize social networks we use two models, namely, k-anonymity for social networks and k-degree anonymity. To determine communities in social networks we use an existing community detection algorithm based on modularity quality function. Our experiments on publically available datasets show that anonymized social networks satisfactorily preserve the community structure of their original networks.

  20. Permanence and Community Structure in Complex Networks

    CERN Document Server

    Chakraborty, Tanmoy; Ganguly, Niloy; Mukherjee, Animesh; Bhowmick, Sanjukta

    2016-01-01

    The goal of community detection algorithms is to identify densely-connected units within large networks. An implicit assumption is that all the constituent nodes belong equally to their associated community. However, some nodes are more important in the community than others. To date, efforts have been primarily driven to identify communities as a whole, rather than understanding to what extent an individual node belongs to its community. Therefore, most metrics for evaluating communities, for example modularity, are global. These metrics produce a score for each community, not for each individual node. In this paper, we argue that the belongingness of nodes in a community is not uniform. The central idea of permanence is based on the observation that the strength of membership of a vertex to a community depends upon two factors: (i) the the extent of connections of the vertex within its community versus outside its community, and (ii) how tightly the vertex is connected internally. We discuss how permanence ...

  1. Networks communities within and across borders

    CERN Document Server

    Cerina, Federica; Pammolli, Fabio; Riccaboni, Massimo

    2013-01-01

    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 and highlights the interplay of the internationalization pressure toward a global innovation system against the administrative borders imposed by the national and continental 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 geog...

  2. Finding network communities using modularity density

    Science.gov (United States)

    Botta, Federico; del Genio, Charo I.

    2016-12-01

    Many real-world complex networks exhibit a community structure, in which the modules correspond to actual functional units. Identifying these communities is a key challenge for scientists. A common approach is to search for the network partition that maximizes a quality function. Here, we present a detailed analysis of a recently proposed function, namely modularity density. We show that it does not incur in the drawbacks suffered by traditional modularity, and that it can identify networks without ground-truth community structure, deriving its analytical dependence on link density in generic random graphs. In addition, we show that modularity density allows an easy comparison between networks of different sizes, and we also present some limitations that methods based on modularity density may suffer from. Finally, we introduce an efficient, quadratic community detection algorithm based on modularity density maximization, validating its accuracy against theoretical predictions and on a set of benchmark networks.

  3. Community Detection in Quantum Complex Networks

    CERN Document Server

    Faccin, Mauro; Johnson, Tomi; Biamonte, Jacob; Bergholm, Ville

    2013-01-01

    Determining community structure in interacting systems, ranging from technological to social, from biological to chemical, is a topic of central importance in the study of networks. Extending this concept to apply to quantum systems represents an open challenge and a crucial missing component towards a theory of complex networks based on quantum mechanics. Here we accomplish this goal by introducing methods for identifying the community structure of a network governed by quantum dynamics. To illustrate our approach we turn to a host of examples, including a naturally occurring light-harvesting network, where from first principles we determine a consistent community structure. In certain regimes the communities we determine agree with a partitioning currently done by hand in the quantum chemistry literature. In other regimes, we uncover a new community structure. The difference stems from defining measures to determine distances between nodes in quantum systems, and then determining optimal modularity. Merging...

  4. A Social Network Model Exhibiting Tunable Overlapping Community Structure

    NARCIS (Netherlands)

    Liu, D.; Blenn, N.; Van Mieghem, P.F.A.

    2012-01-01

    Social networks, as well as many other real-world networks, exhibit overlapping community structure. In this paper, we present formulas which facilitate the computation for characterizing the overlapping community structure of networks. A hypergraph representation of networks with overlapping

  5. 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…

  6. Community detection in networks: Structural communities versus ground truth

    Science.gov (United States)

    Hric, Darko; Darst, Richard K.; Fortunato, Santo

    2014-12-01

    Algorithms to find communities in networks rely just on structural information and search for cohesive subsets of nodes. On the other hand, most scholars implicitly or explicitly assume that structural communities represent groups of nodes with similar (nontopological) properties or functions. This hypothesis could not be verified, so far, because of the lack of network datasets with information on the classification of the nodes. We show that traditional community detection methods fail to find the metadata groups in many large networks. Our results show that there is a marked separation between structural communities and metadata groups, in line with recent findings. That means that either our current modeling of community structure has to be substantially modified, or that metadata groups may not be recoverable from topology alone.

  7. 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....

  8. 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.

  9. Taxonomies of networks from community structure.

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J; Reid, Stephen; Porter, Mason A; Mucha, Peter J; Fricker, Mark D; Jones, Nick S

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  10. Taxonomies of networks from community structure

    Science.gov (United States)

    Onnela, Jukka-Pekka; Fenn, Daniel J.; Reid, Stephen; Porter, Mason A.; Mucha, Peter J.; Fricker, Mark D.; Jones, Nick S.

    2012-09-01

    The study of networks has become a substantial interdisciplinary endeavor that encompasses myriad disciplines in the natural, social, and information sciences. Here we introduce a framework for constructing taxonomies of networks based on their structural similarities. These networks can arise from any of numerous sources: They can be empirical or synthetic, they can arise from multiple realizations of a single process (either empirical or synthetic), they can represent entirely different systems in different disciplines, etc. Because mesoscopic properties of networks are hypothesized to be important for network function, we base our comparisons on summaries of network community structures. Although we use a specific method for uncovering network communities, much of the introduced framework is independent of that choice. After introducing the framework, we apply it to construct a taxonomy for 746 networks and demonstrate that our approach usefully identifies similar networks. We also construct taxonomies within individual categories of networks, and we thereby expose nontrivial structure. For example, we create taxonomies for similarity networks constructed from both political voting data and financial data. We also construct network taxonomies to compare the social structures of 100 Facebook networks and the growth structures produced by different types of fungi.

  11. Sadness, tragedy and mass disaster in Oklahoma City: providing critical incident stress debriefings to a community in crisis.

    Science.gov (United States)

    Davis, J A

    1996-04-01

    Shortly after 09:00 h on 19 April 1995, the Alfred P. Murrah Federal Building, located in downtown Oklahoma City, was devastated with a bomb blast of such gigantic proportions that it was heard 60 miles away in neighbouring Norman, Oklahoma. Oklahomans routinely commuting to work on that sunny Wednesday morning went about their business as usual. A crude bomb chemically comprised of various organic compounds, chemical fertilizer, ammonium nitrate and diesel fuel, weighing an estimated 4800 pounds or more, was transported in a vehicle the size of a truck. It blew open a crater 6-8 ft deep in the street floor. The Murrah Federal Building was impacted immediately; floors, windows, communication equipment and almost all the innocent victims inside were razed to the ground. Outside the building, as far as 10 blocks away or more, hundreds of victims lay hurt, seriously injured or dead from shards of glass that flew from office windows hundreds of feet above the street floor. Without warning, the initial impact of the bomb immediately devastated the entire city. People were in a state of shock, disbelief and denial; acute symptoms of post traumatic stress disorder (PTSD) were commonplace. Oklahomans, 'numb' from the impact of the critical incident and ill-equipped to handle the chaos of such catastrophic proportions, struggled to regain control of their lives as friends, family and loved ones went unaccounted for or were found critically injured, dying or already dead. The critical incident on 19 April demanded the immediate attention of the nation, to come to the aid of the Oklahomans who were in desperate need. By 1 June, the exhaustive investigations revealed that 30 office buildings in downtown Oklahoma City had to be condemned, and as many as 300 others were damaged. In addition, 168 people had been found dead including 19 children and one nurse working as an emergency services rescue worker. Approximately 490 other victims had been reported injured from the blast

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

    African Journals Online (AJOL)

    WimHugo

    project and institutional websites, funder databases, community of practice portals, and social networks. .... World Data Centre for Biodiversity and Human Health in Africa. In some ... but typically linked into the portal environment manually.

  13. Hierarchical community structure in complex (social) networks

    CERN Document Server

    Massaro, Emanuele

    2014-01-01

    The investigation of community structure in networks is a task of great importance in many disciplines, namely physics, sociology, biology and computer science where systems are often represented as graphs. One of the challenges is to find local communities from a local viewpoint in a graph without global information in order to reproduce the subjective hierarchical vision for each vertex. In this paper we present the improvement of an information dynamics algorithm in which the label propagation of nodes is based on the Markovian flow of information in the network under cognitive-inspired constraints \\cite{Massaro2012}. In this framework we have introduced two more complex heuristics that allow the algorithm to detect the multi-resolution hierarchical community structure of networks from a source vertex or communities adopting fixed values of model's parameters. Experimental results show that the proposed methods are efficient and well-behaved in both real-world and synthetic networks.

  14. Deciphering network community structure by surprise

    National Research Council Canada - National Science Library

    Aldecoa, Rodrigo; Marín, Ignacio

    2011-01-01

    .... A fundamental, unsolved problem is how to characterize the community structure of a network. Here, using both standard and novel benchmarks, we show that maximization of a simple global parameter, which we call Surprise...

  15. Stochastic blockmodels and community structure in networks

    CERN Document Server

    Karrer, Brian

    2010-01-01

    Stochastic blockmodels have been proposed as a tool for detecting community structure in networks as well as for generating synthetic networks for use as benchmarks. Most blockmodels, however, ignore variation in vertex degree, making them unsuitable for applications to real-world networks, which typically display broad degree distributions that can significantly distort the results. Here we demonstrate how the generalization of blockmodels to incorporate this missing element leads to an improved objective function for community detection in complex networks. We also propose a heuristic algorithm for community detection using this objective function or its non-degree-corrected counterpart and show that the degree-corrected version dramatically outperforms the uncorrected one in both real-world and synthetic networks.

  16. 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.

  17. Community Detection in Networks with Node Features

    CERN Document Server

    Zhang, Yuan; Zhu, Ji

    2015-01-01

    Many methods have been proposed for community detection in networks, but most of them do not take into account additional information on the nodes that is often available in practice. In this paper, we propose a new joint community detection criterion that uses both the network edge information and the node features to detect community structures. One advantage our method has over existing joint detection approaches is the flexibility of learning the impact of different features which may differ across communities. Another advantage is the flexibility of choosing the amount of influence the feature information has on communities. The method is asymptotically consistent under the block model with additional assumptions on the feature distributions, and performs well on simulated and real networks.

  18. Civil Society as a Network of Communities

    OpenAIRE

    Mendo Castro Henriques

    2007-01-01

    According to a political philosophy approach, civil society may be defined as the network of institutions of private origin and a public purpose in which communities share goods, meanings and values, significantly contributing to the progress or decline of governance. Its empowering role is growing as cooperation networks spread across the local, national, supranational and global levels, strengthening both the communities they serve and the governance procedures they legitimize. Those instit...

  19. 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

  20. Netgram: Visualizing Communities in Evolving Networks.

    Directory of Open Access Journals (Sweden)

    Raghvendra Mall

    Full Text Available Real-world complex networks are dynamic in nature and change over time. The change is usually observed in the interactions within the network over time. Complex networks exhibit community like structures. A key feature of the dynamics of complex networks is the evolution of communities over time. Several methods have been proposed to detect and track the evolution of these groups over time. However, there is no generic tool which visualizes all the aspects of group evolution in dynamic networks including birth, death, splitting, merging, expansion, shrinkage and continuation of groups. In this paper, we propose Netgram: a tool for visualizing evolution of communities in time-evolving graphs. Netgram maintains evolution of communities over 2 consecutive time-stamps in tables which are used to create a query database using the sql outer-join operation. It uses a line-based visualization technique which adheres to certain design principles and aesthetic guidelines. Netgram uses a greedy solution to order the initial community information provided by the evolutionary clustering technique such that we have fewer line cross-overs in the visualization. This makes it easier to track the progress of individual communities in time evolving graphs. Netgram is a generic toolkit which can be used with any evolutionary community detection algorithm as illustrated in our experiments. We use Netgram for visualization of topic evolution in the NIPS conference over a period of 11 years and observe the emergence and merging of several disciplines in the field of information processing systems.

  1. Discovering Typed Communities in Mobile Social Networks

    Institute of Scientific and Technical Information of China (English)

    Huai-Yu Wan; You-Fang Lin; Zhi-Hao Wu; Hou-Kuan Huang

    2012-01-01

    Mobile social networks,which consist of mobile users who communicate with each other using cell phones,are reflections of people's interactions in social lives.Discovering typed communities (e.g.,family communities or corporate communities) in mobile social networks is a very promising problem.For example,it can help mobile operators to determine the target users for precision marketing.In this paper we propose discovering typed communities in mobile social networks by utilizing the labels of relationships between users.We use the user logs stored by mobile operators,including communication and user movement records,to collectively label all the relationships in a network,by employing an undirected probabilistic graphical model,i.e.,conditional random fields.Then we use two methods to discover typed communities based on the results of relationship labeling:one is simply retaining or cutting relationships according to their labels,and the other is using sophisticated weighted community detection algorithms.The experimental results show that our proposed framework performs well in terms of the accuracy of typed community detection in mobile social networks.

  2. Discovering Network Structure Beyond Communities

    OpenAIRE

    Nishikawa, Takashi; Adilson E Motter

    2011-01-01

    To understand the formation, evolution, and function of complex systems, it is crucial to understand the internal organization of their interaction networks. Partly due to the impossibility of visualizing large complex networks, resolving network structure remains a challenging problem. Here we overcome this difficulty by combining the visual pattern recognition ability of humans with the high processing speed of computers to develop an exploratory method for discovering groups of nodes chara...

  3. Community detection by signaling on complex networks

    Science.gov (United States)

    Hu, Yanqing; Li, Menghui; Zhang, Peng; Fan, Ying; di, Zengru

    2008-07-01

    Based on a signaling process of complex networks, a method for identification of community structure is proposed. For a network with n nodes, every node is assumed to be a system which can send, receive, and record signals. Each node is taken as the initial signal source to excite the whole network one time. Then the source node is associated with an n -dimensional vector which records the effects of the signaling process. By this process, the topological relationship of nodes on the network could be transferred into a geometrical structure of vectors in n -dimensional Euclidean space. Then the best partition of groups is determined by F statistics and the final community structure is given by the K -means clustering method. This method can detect community structure both in unweighted and weighted networks. It has been applied to ad hoc networks and some real networks such as the Zachary karate club network and football team network. The results indicate that the algorithm based on the signaling process works well.

  4. Identifying communities from multiplex biological networks

    Directory of Open Access Journals (Sweden)

    Gilles Didier

    2015-12-01

    Full Text Available Various biological networks can be constructed, each featuring gene/protein relationships of different meanings (e.g., protein interactions or gene co-expression. However, this diversity is classically not considered and the different interaction categories are usually aggregated in a single network. The multiplex framework, where biological relationships are represented by different network layers reflecting the various nature of interactions, is expected to retain more information. Here we assessed aggregation, consensus and multiplex-modularity approaches to detect communities from multiple network sources. By simulating random networks, we demonstrated that the multiplex-modularity method outperforms the aggregation and consensus approaches when network layers are incomplete or heterogeneous in density. Application to a multiplex biological network containing 4 layers of physical or functional interactions allowed recovering communities more accurately annotated than their aggregated counterparts. Overall, taking into account the multiplexity of biological networks leads to better-defined functional modules. A user-friendly graphical software to detect communities from multiplex networks, and corresponding C source codes, are available at GitHub (https://github.com/gilles-didier/MolTi.

  5. Sharing cost in social community networks

    DEFF Research Database (Denmark)

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

    2012-01-01

    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...... to gain attention amongst the civilian Internet users. By using special WiFi routers that are provided by a social community network provider (SCNP), users can effectively share their connection with the neighborhood in return for some monthly monetary benefits. However, deployment maps of existing WSCNs...

  6. Sharing cost in social community networks

    DEFF Research Database (Denmark)

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

    2012-01-01

    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...... to gain attention amongst the civilian Internet users. By using special WiFi routers that are provided by a social community network provider (SCNP), users can effectively share their connection with the neighborhood in return for some monthly monetary benefits. However, deployment maps of existing WSCNs...

  7. 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.

  8. 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.

  9. Deep community detection in topologically incomplete networks

    Science.gov (United States)

    Xin, Xin; Wang, Chaokun; Ying, Xiang; Wang, Boyang

    2017-03-01

    In this paper, we consider the problem of detecting communities in topologically incomplete networks (TIN), which are usually observed from real-world networks and where some edges are missing. Existing approaches to community detection always consider the input network as connected. However, more or less, even nearly all, edges are missing in real-world applications, e.g. the protein-protein interaction networks. Clearly, it is a big challenge to effectively detect communities in these observed TIN. At first, we bring forward a simple but useful method to address the problem. Then, we design a structured deep convolutional neural network (CNN) model to better detect communities in TIN. By gradually removing edges of the real-world networks, we show the effectiveness and robustness of our structured deep model on a variety of real-world networks. Moreover, we find that the appropriate choice of hop counts can improve the performance of our deep model in some degree. Finally, experimental results conducted on synthetic data sets also show the good performance of our proposed deep CNN model.

  10. 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.

  11. Alternative approach to community detection in networks

    Science.gov (United States)

    Medus, A. D.; Dorso, C. O.

    2009-06-01

    The problem of community detection is relevant in many disciplines of science and modularity optimization is the widely accepted method for this purpose. It has recently been shown that this approach presents a resolution limit by which it is not possible to detect communities with sizes smaller than a threshold, which depends on the network size. Moreover, it might happen that the communities resulting from such an approach do not satisfy the usual qualitative definition of commune; i.e., nodes in a commune are more connected among themselves than to nodes outside the commune. In this paper we present a different method for community detection in complex networks. We define merit factors based on the weak and strong community definitions formulated by Radicchi [Proc. Natl. Acad. Sci. U.S.A. 101, 2658 (2004)] and we show that these local definitions avoid the resolution limit problem found in the modularity optimization approach.

  12. Community detection based on network communicability

    Science.gov (United States)

    Estrada, Ernesto

    2011-03-01

    We propose a new method for detecting communities based on the concept of communicability between nodes in a complex network. This method, designated as N-ComBa K-means, uses a normalized version of the adjacency matrix to build the communicability matrix and then applies K-means clustering to find the communities in a graph. We analyze how this method performs for some pathological cases found in the analysis of the detection limit of communities and propose some possible solutions on the basis of the analysis of the ratio of local to global densities in graphs. We use four different quality criteria for detecting the best clustering and compare the new approach with the Girvan-Newman algorithm for the analysis of two "classical" networks: karate club and bottlenose dolphins. Finally, we analyze the more challenging case of homogeneous networks with community structure, for which the Girvan-Newman completely fails in detecting any clustering. The N-ComBa K-means approach performs very well in these situations and we applied it to detect the community structure in an international trade network of miscellaneous manufactures of metal having these characteristics. Some final remarks about the general philosophy of community detection are also discussed.

  13. Defining and identifying communities in networks

    Science.gov (United States)

    Radicchi, Filippo; Castellano, Claudio; Cecconi, Federico; Loreto, Vittorio; Parisi, Domenico

    2004-03-01

    The investigation of community structures in networks is an important issue in many domains and disciplines. This problem is relevant for social tasks (objective analysis of relationships on the web), biological inquiries (functional studies in metabolic and protein networks), or technological problems (optimization of large infrastructures). Several types of algorithms exist for revealing the community structure in networks, but a general and quantitative definition of community is not implemented in the algorithms, leading to an intrinsic difficulty in the interpretation of the results without any additional nontopological information. In this article we deal with this problem by showing how quantitative definitions of community are implemented in practice in the existing algorithms. In this way the algorithms for the identification of the community structure become fully self-contained. Furthermore, we propose a local algorithm to detect communities which outperforms the existing algorithms with respect to computational cost, keeping the same level of reliability. The algorithm is tested on artificial and real-world graphs. In particular, we show how the algorithm applies to a network of scientific collaborations, which, for its size, cannot be attacked with the usual methods. This type of local algorithm could open the way to applications to large-scale technological and biological systems.

  14. 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.

  15. Topics in networks: Community detection, random graphs, and network epidemiology

    Science.gov (United States)

    Karrer, Brian C.

    In this dissertation, we present research on several topics in networks including community detection, random graphs, and network epidemiology. Traditional stochastic blockmodels may produce inaccurate fits to complex networks with heterogeneous degree distributions and we devise a degree-corrected block-model that alleviates this problematic behavior. The resulting objective function for community detection using the degree-corrected version outperforms the traditional model at finding communities on a variety of real-world and synthetic tests. Then we study a different generative model that associates communities to the edges of the network and naturally includes overlapping vertex communities. We create a fast and accurate algorithm to fit this model to empirical networks and show that it can be used to quickly find non-overlapping communities as well. We also develop random graph models for directed acyclic graphs, a class of networks including family trees and citation networks. We argue that the lack of cycles comes from an ordering constraint and then generalize the configuration model to incorporate this constraint. We calculate many properties of these models and demonstrate that sonic of the model predictions agree quite well with real-world networks, emphasizing the importance of vertex ordering to generating directed acyclic networks with realistic properties. Finally, we examine the spread of disease over networks, starting with a simple model of two diseases spreading with cross-immunity, where infection by one disease makes an individual immune to the other disease and vice versa. Utilizing a timescale separation argument, we map the system to consecutive bond percolation, one disease spreading after the other. The resulting phase diagram includes discontinuous and continuous phase transitions and a coexistence region where both diseases can spread to a substantial fraction of the population. Then we analyze a flexible susceptible

  16. Information transfer in community structured multiplex networks

    CERN Document Server

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

    2015-01-01

    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 ...

  17. Emergence of multiplex communities in collaboration networks

    CERN Document Server

    Battiston, Federico; Nicosia, Vincenzo; Bianconi, Ginestra; Latora, Vito

    2015-01-01

    Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes in which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce ...

  18. Community detection in networks: A user guide

    CERN Document Server

    Fortunato, Santo

    2016-01-01

    Community detection in networks is one of the most popular topics of modern network science. Communities, or clusters, are usually groups of vertices having higher probability of being connected to each other than to members of other groups, though other patterns are possible. Identifying communities is an ill-defined problem. There are no universal protocols on the fundamental ingredients, like the definition of community itself, nor on other crucial issues, like the validation of algorithms and the comparison of their performances. This has generated a number of confusions and misconceptions, which undermine the progress in the field. We offer a guided tour through the main aspects of the problem. We also point out strengths and weaknesses of popular methods, and give directions to their use.

  19. Properties of asymmetrically evolved community networks

    Institute of Scientific and Technical Information of China (English)

    Cui Di; Gao Zi-You; Zheng Jian-Feng

    2009-01-01

    This paper studies a simple asymmetrically evolved community network with a combination of preferential at-tachment and random properties. An important issue about community networks is to discover the different utility increments of two nodes, where the utility is introduced to investigate the asymmetrical effect of connecting two nodes. On the other hand, the connection of two nodes in community networks can be classified as two nodes belonging to the same or to different communities. The simulation results show that the model can reproduce a power-law utility distribution P(u)~ u-σ,σ=2+ 1/p, which can be obtained by using mean-field approximation methods. Furthermore, the model exhibits exponential behaviour with respect to small values of a parameter denoting the random effect in our model at the low-utility region and a power-law feature with respect to big values of this parameter at the high-utility region, which is in good agreement with theoretical analysis. This kind of community network can reproduce a unique utility distribution by theoretical and numerical analysis.

  20. 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

  1. Cataloguing the bacterial community of the Great Salt Plains, Oklahoma using 16S rRNA based metagenomics pyrosequencing

    Directory of Open Access Journals (Sweden)

    Ahmed H. Gad

    2017-06-01

    Full Text Available The Great Salt Plains of Oklahoma (GSP is an extreme region, a hypersaline environment from marine origin and a unique area of the Salt National Wild Refuge in the north-central region of Oklahoma. In this study we analyzed the diversity and distribution of bacteria in two habitats; vegetated areas (GAB and salt flat areas (GAS in the sediments of GSP using the high-throughput techniques of 16S rRNA gene amplicon (V1-V2 regions metagenomics-454 pyrosequencing. The filtered sequences resulted to a total of 303,723 paired end reads were generated, assigned into 1646 numbers of OTUs and 56.4% G + C content for GAB, and a total of 144,496 paired end reads were generated, assigned into 785 numbers of OTUs and 56.7% G+ C content for GAS. All the resulting 16S rRNA was of an average length ~ 187 bp, assigned to 37 bacterial phyla and candidate divisions. The abundant OTUs were affiliated with Proteobacteria (36.2% in GAB and 31.5% in GAS, Alphaproteobacteria (13.3% in GAB and 8.7% in GAS, Gammaproteobacteria (13% in GAB and 14.2% in GAS, Deltaproteobacteria (6.5% in GAB and 6.1% in GAS, Betaproteobacteria (2.6% in GAB and 1.14% in GAS, Bacteroidetes (16.8% in GAB and 24.3% in GAS, Chloroflexi (8.7% in GAB and 6% in GAS, Actinobacteria (8.5% in GAB and 5.8% in GAS and Firmicutes (6.5% in GAB and 6.6% in GAS. This is the first study of a high resolution microbial phylogenetic profile of the GSP and the findings stipulate evidence of the bacterial heterogeneity that might be originated by surface and subsurface environments and better understanding of the ecosystem dynamics of GSP. Metagenome sequence data are available at NCBI with accession numbers; LT699840-LT700186.

  2. 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.

  3. Overlapping Communities Detection Based on Link Partition in Directed Networks

    Directory of Open Access Journals (Sweden)

    Qingyu Zou

    2013-09-01

    Full Text Available Many complex systems can be described as networks to comprehend both the structure and the function. Community structure is one of the most important properties of complex networks. Detecting overlapping communities in networks have been more attention in recent years, but the most of approaches to this problem have been applied to the undirected networks. This paper presents a novel approach based on link partition to detect overlapping communities structure in directed networks. In contrast to previous researches focused on grouping nodes, our algorithm defines communities as groups of directed links rather than nodes with the purpose of nodes naturally belong to more than one community. This approach can identify a suitable number of overlapping communities without any prior knowledge about the community in directed networks. We evaluate our algorithm on a simple artificial network and several real-networks. Experimental results demonstrate that the algorithm proposed is efficient for detecting overlapping communities in directed networks.  

  4. Compassionate community networks: supporting home dying.

    Science.gov (United States)

    Abel, Julian; Bowra, Jon; Walter, Tony; Howarth, Glennys

    2011-09-01

    How may communities be mobilised to help someone dying at home? This conceptual article outlines the thinking behind an innovative compassionate community project being developed at Weston-super-Mare, UK. In this project, a health professional mentors the dying person and their carer to identify and match: (a) the tasks that need to be done and (b) the members of their social network who might help with these tasks. Network members may subsequently join a local volunteer force to assist others who are network poor. Performing practical tasks may be more acceptable to some family, friends and neighbours than having to engage in a conversation about dying, and provides a familiarity with dying that is often lacking in modern societies, so in this model, behavioural change precedes attitudinal change. The scheme rejects a service delivery model of care in favour of a community development model, but differs from community development schemes in which the mentor is a volunteer rather than a health professional, and also from those approaches that strive to build community capacity before any one individual dying person is helped. The pros and cons of each approach are discussed. There is a need for evaluation of this and similar schemes, and for basic research into naturally occurring resource mobilisation at the end of life.

  5. Walk modularity and community structure in networks

    CERN Document Server

    Mehrle, David; Harkin, Anthony

    2014-01-01

    Modularity maximization has been one of the most widely used approaches in the last decade for discovering community structure in networks of practical interest in biology, computing, social science, statistical mechanics, and more. Modularity is a quality function that measures the difference between the number of edges found within clusters minus the number of edges one would statistically expect to find based on random chance. We present a natural generalization of modularity based on the difference between the actual and expected number of walks within clusters, which we call walk-modularity. Walk-modularity can be expressed in matrix form, and community detection can be performed by finding leading eigenvectors of the walk-modularity matrix. We demonstrate community detection on both synthetic and real-world networks and find that walk-modularity maximization returns significantly improved results compared to traditional modularity maximization.

  6. How to Measure Significance of Community Structure in Complex Networks

    CERN Document Server

    Hu, Yanqing; Fan, Ying; Di, Zengru

    2010-01-01

    Community structure analysis is a powerful tool for complex networks, which can simplify their functional analysis considerably. Recently, many approaches were proposed to community structure detection, but few works were focused on the significance of community structure. Since real networks obtained from complex systems always contain error links, and most of the community detection algorithms have random factors, evaluate the significance of community structure is important and urgent. In this paper, we use the eigenvectors' stability to characterize the significance of community structures. By employing the eigenvalues of Laplacian matrix of a given network, we can evaluate the significance of its community structure and obtain the optimal number of communities, which are always hard for community detection algorithms. We apply our method to many real networks. We find that significant community structures exist in many social networks and C.elegans neural network, and that less significant community stru...

  7. 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.

  8. Community detection for networks with unipartite and bipartite structure

    OpenAIRE

    Chang, Chang; Tang, Chao

    2013-01-01

    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 net...

  9. 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.

  10. Emergence of Multiplex Communities in Collaboration Networks.

    Directory of Open Access Journals (Sweden)

    Federico Battiston

    Full Text Available Community structures in collaboration networks reflect the natural tendency of individuals to organize their work in groups in order to better achieve common goals. In most of the cases, individuals exploit their connections to introduce themselves to new areas of interests, giving rise to multifaceted collaborations which span different fields. In this paper, we analyse collaborations in science and among movie actors as multiplex networks, where the layers represent respectively research topics and movie genres, and we show that communities indeed coexist and overlap at the different layers of such systems. We then propose a model to grow multiplex networks based on two mechanisms of intra and inter-layer triadic closure which mimic the real processes by which collaborations evolve. We show that our model is able to explain the multiplex community structure observed empirically, and we infer the strength of the two underlying social mechanisms from real-world systems. Being also able to correctly reproduce the values of intra-layer and inter-layer assortativity correlations, the model contributes to a better understanding of the principles driving the evolution of social networks.

  11. Community Detection in Quantum Complex Networks

    Science.gov (United States)

    Faccin, Mauro; Migdał, Piotr; Johnson, Tomi H.; Bergholm, Ville; Biamonte, Jacob D.

    2014-10-01

    Determining community structure is a central topic in the study of complex networks, be it technological, social, biological or chemical, static or in interacting systems. In this paper, we extend the concept of community detection from classical to quantum systems—a crucial missing component of a theory of complex networks based on quantum mechanics. We demonstrate that certain quantum mechanical effects cannot be captured using current classical complex network tools and provide new methods that overcome these problems. Our approaches are based on defining closeness measures between nodes, and then maximizing modularity with hierarchical clustering. Our closeness functions are based on quantum transport probability and state fidelity, two important quantities in quantum information theory. To illustrate the effectiveness of our approach in detecting community structure in quantum systems, we provide several examples, including a naturally occurring light-harvesting complex, LHCII. The prediction of our simplest algorithm, semiclassical in nature, mostly agrees with a proposed partitioning for the LHCII found in quantum chemistry literature, whereas our fully quantum treatment of the problem uncovers a new, consistent, and appropriately quantum community structure.

  12. Spectral community detection in sparse networks

    CERN Document Server

    Newman, M E J

    2013-01-01

    Spectral methods based on the eigenvectors of matrices are widely used in the analysis of network data, particularly for community detection and graph partitioning. Standard methods based on the adjacency matrix and related matrices, however, break down for very sparse networks, which includes many networks of practical interest. As a solution to this problem it has been recently proposed that we focus instead on the spectrum of the non-backtracking matrix, an alternative matrix representation of a network that shows better behavior in the sparse limit. Inspired by this suggestion, we here make use of a relaxation method to derive a spectral community detection algorithm that works well even in the sparse regime where other methods break down. Interestingly, however, the matrix at the heart of the method, it turns out, is not exactly the non-backtracking matrix, but a variant of it with a somewhat different definition. We study the behavior of this variant matrix for both artificial and real-world networks an...

  13. Fast network community detection by SCORE

    CERN Document Server

    Jin, Jiashun

    2012-01-01

    Consider a network where the nodes split into K different communities. The community labels for the nodes are unknown and it is of major interest to estimate them (i.e., community detection). Degree Corrected Block Model (DCBM) is a popular network model. How to detect communities with the DCBM is an interesting problem, where the main challenge lies in the degree heterogeneity. We propose a new approach to community detection which we call the Spectral Clustering On Ratios-of-Eigenvectors (SCORE). Compared to classical spectral methods, the main innovation is to use the entry-wise ratios between the first leading eigenvector and each of the other leading eigenvectors for clustering. The central surprise is, the effect of degree heterogeneity is largely ancillary, and can be effectively removed by taking entry-wise ratios between the leading eigenvectors. The method is successfully applied to the web blogs data and the karate club data, with error rates of 58/1222 and 1/34, respectively. These results are muc...

  14. 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.

  15. Community overlays upon real-world complex networks

    NARCIS (Netherlands)

    Ge, X.; Wang, H.

    2012-01-01

    Many networks are characterized by the presence of communities, densely intra-connected groups with sparser inter-connections between groups. We propose a community overlay network representation to capture large-scale properties of communities. A community overlay Go can be constructed upon a netwo

  16. Community overlays upon real-world complex networks

    NARCIS (Netherlands)

    Ge, X.; Wang, H.

    2012-01-01

    Many networks are characterized by the presence of communities, densely intra-connected groups with sparser inter-connections between groups. We propose a community overlay network representation to capture large-scale properties of communities. A community overlay Go can be constructed upon a

  17. Implementation of Network Community Profile using Local Spectral algorithm and its application in Community Networking

    Directory of Open Access Journals (Sweden)

    Vaibhav VPrakash

    2012-12-01

    Full Text Available The problem that is addressed here and being investigated is to empirically review the paper entitled"Empirical comparison of algorithms for network community detection" - Jure Leskovec, Kevin J Langand Michael W Mahoney”wherein we look at the characteristics and specific properties of varioussocial networks used in the public and private domain. The objective of the investigation is to understandcompletely the network community detection using Local Spectral and Metis+MQI algorithms and toanalyse how communities are created and ranked on specific metrics. Five communities have beencompared using the same heuristics of the established functions in the entitled paper and an inference isdrawn based on the graph generated by the same.

  18. 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...... 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...... 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...

  19. Epidemic spreading on complex networks with community structures

    CERN Document Server

    Stegehuis, Clara; van Leeuwaarden, Johan 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 and the vertex degrees. These models show that community structure is an important determinant of the behavior of percolation processes on networks, such as information diffusion or virus spreading: the community structure can both \\textit{enforce} as well as \\textit{inhibit} diffusion processes. Our models further show that it is the mesoscopic set of communities that matters. The exact internal structures of communities barely influence the behavior of percolation processes across networks. This insensitivity is likely due to the relative denseness of the communities.

  20. Microbial Community Metabolic Modeling: A Community Data-Driven Network Reconstruction: COMMUNITY DATA-DRIVEN METABOLIC NETWORK MODELING

    Energy Technology Data Exchange (ETDEWEB)

    Henry, Christopher S. [Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Computation Institute, University of Chicago, Chicago Illinois; Bernstein, Hans C. [Biodetection Sciences, National Security Directorate, Pacific Northwest National Laboratory Richland Washington; Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; The Gene and Linda Voiland School of Chemical Engineering and Bioengineering, Washington State University, Pullman Washington; Weisenhorn, Pamela [Division of Mathematics and Computer Science, Argonne National Laboratory, Argonne Illinois; Division of Biosciences, Argonne National Laboratory, Argonne Illinois; Taylor, Ronald C. [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Lee, Joon-Yong [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Zucker, Jeremy [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington; Song, Hyun-Seob [Biological Sciences Division, Earth and Biological Sciences Directorate, Pacific Northwest National Laboratory, Richland Washington

    2016-06-02

    Metabolic network modeling of microbial communities provides an in-depth understanding of community-wide metabolic and regulatory processes. Compared to single organism analyses, community metabolic network modeling is more complex because it needs to account for interspecies interactions. To date, most approaches focus on reconstruction of high-quality individual networks so that, when combined, they can predict community behaviors as a result of interspecies interactions. However, this conventional method becomes ineffective for communities whose members are not well characterized and cannot be experimentally interrogated in isolation. Here, we tested a new approach that uses community-level data as a critical input for the network reconstruction process. This method focuses on directly predicting interspecies metabolic interactions in a community, when axenic information is insufficient. We validated our method through the case study of a bacterial photoautotroph-heterotroph consortium that was used to provide data needed for a community-level metabolic network reconstruction. Resulting simulations provided experimentally validated predictions of how a photoautotrophic cyanobacterium supports the growth of an obligate heterotrophic species by providing organic carbon and nitrogen sources.

  1. 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.

  2. Temporal occurrence and community structure of helminth parasites in southern leopard frogs, Rana sphenocephala, from north central Oklahoma.

    Science.gov (United States)

    Vhora, M Suhail; Bolek, Matthew G

    2015-03-01

    Currently, little information is available about the temporal recruitment of helminth communities in amphibian hosts. We examined the helminth community structure and temporal recruitment of helminth parasites in southern leopard frogs, Rana sphenocephala. Specifically, we were interested in how host life history such as habitat, age and/or size, diet, sex, and temporal variation in abiotic factors (precipitation and temperature) were important in determining monthly infection patterns of helminth populations and communities in southern leopard frogs. From May to September 2011, 74 southern leopard frogs were collected from Teal Ridge in Stillwater Payne County, OK, USA. Sixty-nine (93 %) of 74 frogs were infected with 1 or more helminth species. During our collecting period, the average monthly temperature was lowest in May and highest in July, and monthly precipitation was highest in May and lowest during the first week of September. The component community consisted of 11 species of helminth, including 1 larval and 1 adult cestode, 2 larval and 3 adult trematodes, and 1 juvenile and 3 adult nematodes. Of the 1790 helminths recovered, 51 % (911) were nematodes, 47 % (842) were cestodes, and 2 % (37) were trematodes. There were significant differences in the total abundance and mean species richness of helminths acquired by skin contact or through frog diet in monthly component communities of southern leopard frogs. A positive correlation existed for percentage of all helminths acquired by skin contact and monthly precipitation (r = 0.94, P < 0.01). Conversely, a negative correlation existed for monthly precipitation and percentage of helminths acquired by diet (r = -0.94, P < 0.01). Our results indicate that abiotic conditions such as precipitation have a major influence on the avenues for and constraints on the transmission of helminths with life cycles associated with water/moisture or terrestrial intermediate/paratenic hosts and are important in structuring

  3. Identifying communities by influence dynamics in social networks

    CERN Document Server

    Stanoev, Angel; Kocarev, Ljupco

    2011-01-01

    Communities are not static; they evolve, split and merge, appear and disappear, i.e. they are product of dynamical processes that govern the evolution of the network. A good algorithm for community detection should not only quantify the topology of the network, but incorporate the dynamical processes that take place on the network. We present a novel algorithm for community detection that combines network structure with processes that support creation and/or evolution of communities. The algorithm does not embrace the universal approach but instead tries to focus on social networks and model dynamic social interactions that occur on those networks. It identifies leaders, and communities that form around those leaders. It naturally supports overlapping communities by associating each node with a membership vector that describes node's involvement in each community. This way, in addition to overlapping communities, we can identify nodes that are good followers to their leader, and also nodes with no clear commu...

  4. Community structure in time-dependent, multiscale, and multiplex networks.

    Science.gov (United States)

    Mucha, Peter J; Richardson, Thomas; Macon, Kevin; Porter, Mason A; Onnela, Jukka-Pekka

    2010-05-14

    Network science is an interdisciplinary endeavor, with methods and applications drawn from across the natural, social, and information sciences. A prominent problem in network science is the algorithmic detection of tightly connected groups of nodes known as communities. We developed a generalized framework of network quality functions that allowed us to study the community structure of arbitrary multislice networks, which are combinations of individual networks coupled through links that connect each node in one network slice to itself in other slices. This framework allows studies of community structure in a general setting encompassing networks that evolve over time, have multiple types of links (multiplexity), and have multiple scales.

  5. Dynamics and control of diseases in networks with community structure.

    Directory of Open Access Journals (Sweden)

    Marcel Salathé

    2010-04-01

    Full Text Available The dynamics of infectious diseases spread via direct person-to-person transmission (such as influenza, smallpox, HIV/AIDS, etc. depends on the underlying host contact network. Human contact networks exhibit strong community structure. Understanding how such community structure affects epidemics may provide insights for preventing the spread of disease between communities by changing the structure of the contact network through pharmaceutical or non-pharmaceutical interventions. We use empirical and simulated networks to investigate the spread of disease in networks with community structure. We find that community structure has a major impact on disease dynamics, and we show that in networks with strong community structure, immunization interventions targeted at individuals bridging communities are more effective than those simply targeting highly connected individuals. Because the structure of relevant contact networks is generally not known, and vaccine supply is often limited, there is great need for efficient vaccination algorithms that do not require full knowledge of the network. We developed an algorithm that acts only on locally available network information and is able to quickly identify targets for successful immunization intervention. The algorithm generally outperforms existing algorithms when vaccine supply is limited, particularly in networks with strong community structure. Understanding the spread of infectious diseases and designing optimal control strategies is a major goal of public health. Social networks show marked patterns of community structure, and our results, based on empirical and simulated data, demonstrate that community structure strongly affects disease dynamics. These results have implications for the design of control strategies.

  6. Cost Sharing in Social Community Networks

    CERN Document Server

    Pal, Ranjan

    2011-01-01

    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 to gain attention amongst the civilian Internet users. By using \\emph{special} WiFi routers that are provided by a social community network provider (SCNP), users can effectively share their connection with the neighborhood in return for some monthly monetary benefits. However, deployment maps of existing WSCNs 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 asimple to implement, successful a mechanism is successful if it achieves its intended purpose. For example in this work, a successful mechanism would help install routers in a locality}, \\emph{budget-balanced}, \\emph{ex-post efficient}, and \\...

  7. Extraction of hidden information by efficient community detection in networks

    CERN Document Server

    Lee, Juyong; Lee, Jooyoung

    2012-01-01

    Currently, we are overwhelmed by a deluge of experimental data, and network physics has the potential to become an invaluable method to increase our understanding of large interacting datasets. However, this potential is often unrealized for two reasons: uncovering the hidden community structure of a network, known as community detection, is difficult, and further, even if one has an idea of this community structure, it is not a priori obvious how to efficiently use this information. Here, to address both of these issues, we, first, identify optimal community structure of given networks in terms of modularity by utilizing a recently introduced community detection method. Second, we develop an approach to use this community information to extract hidden information from a network. When applied to a protein-protein interaction network, the proposed method outperforms current state-of-the-art methods that use only the local information of a network. The method is generally applicable to networks from many areas.

  8. Towards online multiresolution community detection in large-scale networks.

    Directory of Open Access Journals (Sweden)

    Jianbin Huang

    Full Text Available The investigation of community structure in networks has aroused great interest in multiple disciplines. One of the challenges is to find local communities from a starting vertex in a network without global information about the entire network. Many existing methods tend to be accurate depending on a priori assumptions of network properties and predefined parameters. In this paper, we introduce a new quality function of local community and present a fast local expansion algorithm for uncovering communities in large-scale networks. The proposed algorithm can detect multiresolution community from a source vertex or communities covering the whole network. Experimental results show that the proposed algorithm is efficient and well-behaved in both real-world and synthetic networks.

  9. Community Attachment and Satisfaction: The Role of a Community's Social Network Structure

    Science.gov (United States)

    Crowe, Jessica

    2010-01-01

    This paper links the micro and macro levels of analysis by examining how different aspects of community sentiment are affected by one's personal ties to the community compared with the organizational network structure of the community. Using data collected from residents of six communities in Washington State, network analysis combined with…

  10. Influence of community structure on the synchronization of power network

    Science.gov (United States)

    Yang, Li-Xin; Jiang, Jun; Liu, Xiao-Jun

    2016-12-01

    This paper studies the synchronizability of power network with community structure. Second-order Kuramoto-like oscillators with dissimilar natural frequencies are used as a coarse-scale model for an electrical power network that contains generators and consumers. The impact of community structure on frequency synchronization of power network is investigated, focusing on the parameters such as community strength, community number and connection strategy between communities. Numerical simulations show that increasing the community strength above a certain critical threshold or adding new communities to the network will be beneficial for the synchronization. Of course, connecting high-degree nodes among communities will be a best strategy to enhance synchronization. Furthermore, it is observed that the synchronizability of the network is significantly influenced by adding new links with different characteristics.

  11. Community detection in networks with positive and negative links

    NARCIS (Netherlands)

    Traag, V.A.; Bruggeman, J.

    2009-01-01

    Detecting communities in complex networks accurately is a prime challenge, preceding further analyses of network characteristics and dynamics. Until now, community detection took into account only positively valued links, while many actual networks also feature negative links. We extend an existing

  12. Community detection in networks with positive and negative links

    NARCIS (Netherlands)

    Traag, V.A.; Bruggeman, J.

    2009-01-01

    Detecting communities in complex networks accurately is a prime challenge, preceding further analyses of network characteristics and dynamics. Until now, community detection took into account only positively valued links, while many actual networks also feature negative links. We extend an existing

  13. Community structure in introductory physics course networks

    CERN Document Server

    Traxler, Adrienne L

    2015-01-01

    Student-to-student interactions are foundational to many active learning environments, but are most often studied using qualitative methods. Network analysis tools provide a quantitative complement to this picture, allowing researchers to describe the social interactions of whole classrooms as systems. Past results from introductory physics courses have suggested a sharp division in the formation of social structure between large lecture sections and small studio classroom environments. Extending those results, this study focuses on calculus-based introductory physics courses at a large public university with a heavily commuter and nontraditional student population. Community detection network methods are used to characterize pre- and post-course collaborative structure in several sections, and differences are considered between small and large classes. These results are compared with expectations from earlier findings, and comment on implications for instruction and further study.

  14. Community Structure in Congressional Cosponsorship Networks

    CERN Document Server

    Zhang, Yan; Traud, Amanda L; Porter, Mason A; Fowler, James H; Mucha, Peter J

    2007-01-01

    We study the United States Congress by constructing networks between Members of Congress based on the legislation that they cosponsor. Using the concept of modularity, we identify the community structure of Congressmen, as connected via sponsorship/cosponsorship of the same legislation, to investigate the collaborative communities of legislators in both chambers of Congress. This analysis yields an explicit and conceptually clear measure of political polarization, demonstrating a sharp increase in partisan polarization which preceded and then culminated in the 104th Congress (1995-1996), when Republicans took control of both chambers. Although polarization has since waned in the U.S. Senate, it remains at historically high levels in the House of Representatives.

  15. The information transmission in community networks

    Science.gov (United States)

    Zhu, Zhi-Qiang; Liu, Chuan-Jian; Wu, Jian-Liang; Liu, Bin

    2013-09-01

    The community structure has been empirically found in many real networks. This paper proposes an efficient Double Shortest Path routing strategy trying to avoid the modules of traffic congestion, which means that we adopt the shortest routing strategy both in the inter-modules and in the intra-module. Simulations show that this routing algorithm is superior to the traditional shortest path routing protocol with appropriate selection of the tunable parameters. In addition, this algorithm can also be improved by integrating it with several alternative routing strategies.

  16. 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.

  17. Opinion Dynamics on Complex Networks with Communities

    Science.gov (United States)

    Wang, Ru; Chi, Li-Ping

    2008-04-01

    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.

  18. Opinion Dynamics on Complex Networks with Communities

    Institute of Scientific and Technical Information of China (English)

    WANG Ru; CHI Li-Ping; CAI Xu

    2008-01-01

    @@ The Ising or Ports 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 upspin 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.

  19. Optimal multi-community network modularity for information diffusion

    Science.gov (United States)

    Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong

    2016-02-01

    Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

  20. SCOUT: simultaneous time segmentation and community detection in dynamic networks

    CERN Document Server

    Hulovatyy, Yuriy

    2016-01-01

    Many evolving complex systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which identifies groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share one community organization. In reality, the truth likely lies between these two extremes, since some time periods can have community organization that evolves while others can have community organization that stays the same. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we int...

  1. 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.

  2. Maximal Neighbor Similarity Reveals Real Communities in Networks

    Science.gov (United States)

    Žalik, Krista Rizman

    2015-12-01

    An important problem in the analysis of network data is the detection of groups of densely interconnected nodes also called modules or communities. Community structure reveals functions and organizations of networks. Currently used algorithms for community detection in large-scale real-world networks are computationally expensive or require a priori information such as the number or sizes of communities or are not able to give the same resulting partition in multiple runs. In this paper we investigate a simple and fast algorithm that uses the network structure alone and requires neither optimization of pre-defined objective function nor information about number of communities. We propose a bottom up community detection algorithm in which starting from communities consisting of adjacent pairs of nodes and their maximal similar neighbors we find real communities. We show that the overall advantage of the proposed algorithm compared to the other community detection algorithms is its simple nature, low computational cost and its very high accuracy in detection communities of different sizes also in networks with blurred modularity structure consisting of poorly separated communities. All communities identified by the proposed method for facebook network and E-Coli transcriptional regulatory network have strong structural and functional coherence.

  3. Cascades with coupled map lattices in preferential attachment community networks

    Institute of Scientific and Technical Information of China (English)

    Cui Di; Gao Zi-You; Zhao Xiao-Mei

    2008-01-01

    In this paper,cascading failure is studied by coupled map lattice (CML) methods in preferential attachment community networks.It is found that external perturbation R is increasing with modularity Q growing by simulation.In particular,the large modularity Q can hold off the cascading failure dynamic process in community networks.Furthermore,different attack strategies also greatly affect the cascading failure dynamic process. It is particularly significant to control cascading failure process in real community networks.

  4. Community Air Sensor Network (CAIRSENSE) project ...

    Science.gov (United States)

    Advances in air pollution sensor technology have enabled the development of small and low cost systems to measure outdoor air pollution. The deployment of a large number of sensors across a small geographic area would have potential benefits to supplement traditional monitoring networks with additional geographic and temporal measurement resolution, if the data quality were sufficient. To understand the capability of emerging air sensor technology, the Community Air Sensor Network (CAIRSENSE) project deployed low cost, continuous and commercially-available air pollution sensors at a regulatory air monitoring site and as a local sensor network over a surrounding ~2 km area in Southeastern U.S. Co-location of sensors measuring oxides of nitrogen, ozone, carbon monoxide, sulfur dioxide, and particles revealed highly variable performance, both in terms of comparison to a reference monitor as well as whether multiple identical sensors reproduced the same signal. Multiple ozone, nitrogen dioxide, and carbon monoxide sensors revealed low to very high correlation with a reference monitor, with Pearson sample correlation coefficient (r) ranging from 0.39 to 0.97, -0.25 to 0.76, -0.40 to 0.82, respectively. The only sulfur dioxide sensor tested revealed no correlation (r 0.5), step-wise multiple linear regression was performed to determine if ambient temperature, relative humidity (RH), or age of the sensor in sampling days could be used in a correction algorihm to im

  5. Generating weighted community networks based on local events

    Institute of Scientific and Technical Information of China (English)

    Xu Qi-Xin; Xu Xin-Jian

    2009-01-01

    realistic networks have community structures, namely, a network consists of groups of nodes within which links are dense but among which links are sparse. This paper proposes a growing network model based on local processes, the addition of new nodes intra-community and new links intra- or inter-community. Also, it utilizes the preferential attachment for building connections determined by nodes' strengths, which evolves dynamically during the growth of the system. The resulting network reflects the intrinsic community structure with generalized power-law distributions of nodes' degrees and strengths.

  6. Efficient discovery of overlapping communities in massive networks.

    Science.gov (United States)

    Gopalan, Prem K; Blei, David M

    2013-09-03

    Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks.

  7. Node-Centric Detection of Overlapping Communities in Social Networks

    CERN Document Server

    Cohen, Yehonatan; Rubin, Amir

    2016-01-01

    We present NECTAR, a community detection algorithm that generalizes Louvain method's local search heuristic for overlapping community structures. NECTAR chooses dynamically which objective function to optimize based on the network on which it is invoked. Our experimental evaluation on both synthetic benchmark graphs and real-world networks, based on ground-truth communities, shows that NECTAR provides excellent results as compared with state of the art community detection algorithms.

  8. The ground truth about metadata and community detection in networks.

    Science.gov (United States)

    Peel, Leto; Larremore, Daniel B; Clauset, Aaron

    2017-05-01

    Across many scientific domains, there is a common need to automatically extract a simplified view or coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called ground truth communities. This works well in synthetic networks with planted communities because these networks' links are formed explicitly based on those known communities. However, there are no planted communities in real-world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. We show that metadata are not the same as ground truth and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch theorem for community detection, which implies that there can be no algorithm that is optimal for all possible community detection tasks. However, community detection remains a powerful tool and node metadata still have value, so a careful exploration of their relationship with network structure can yield insights of genuine worth. We illustrate this point by introducing two statistical techniques that can quantify the relationship between metadata and community structure for a broad class of models. We demonstrate these techniques using both synthetic and real-world networks, and for multiple types of metadata and community structures.

  9. Local modularity for community detection in complex networks

    Science.gov (United States)

    Xiang, Ju; Hu, Tao; Zhang, Yan; Hu, Ke; Li, Jian-Ming; Xu, Xiao-Ke; Liu, Cui-Cui; Chen, Shi

    2016-02-01

    Community detection is a topic of interest in the study of complex networks such as the protein-protein interaction networks and metabolic networks. In recent years, various methods were proposed to detect community structures of the networks. Here, a kind of local modularity with tunable parameter is derived from the Newman-Girvan modularity by a special self-loop strategy that depends on the community division of the networks. By the self-loop strategy, one can easily control the definition of modularity, and the resulting modularity can be optimized by using the existing modularity optimization algorithms. The local modularity is used as the target function for community detection, and a self-consistent method is proposed for the optimization of the local modularity. We analyze the behaviors of the local modularity and show the validity of the local modularity in detecting community structures on various networks.

  10. Joint community and anomaly tracking in dynamic networks

    CERN Document Server

    Baingana, Brian

    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 status, the knowledge of which is vital for targeted advertisement. In buyer-seller networks, community detection facilitates better product recommendations. Unfortunately, reliability of community assignments is hindered by anomalous user behavior often observed as unfair self-promotion, or "fake" highly-connected accounts created to promote fraud. The present paper advocates a novel approach for jointly tracking communities while detecting such anomalous nodes in time-varying networks. By postulating edge creation as the ...

  11. 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.

  12. Leaders in communities of real-world networks

    Science.gov (United States)

    Fu, Jingcheng; Wu, Jianliang; Liu, Chuanjian; Xu, Jin

    2016-02-01

    Community structures have important influence on the properties and dynamic characteristics of the complex networks. However, to the best of our knowledge, there is not much attention given to investigating the internal structure of communities in the literature. In this paper, we study community structures of more than twenty existing networks using ten commonly used community-detecting methods, and discovery that most communities have several leaders whose degrees are particularly large. We use statistical parameter, variance, to classify the communities as leader communities and self-organized communities. In a leader community, we defined the nodes with largest 10 % degree as its leaders. In our experiences, when removing the leaders, on average community's internal edges are reduced by more than 40 % and inter-communities edges are reduced by more than 20 %. In addition, community's average clustering coefficient decreases. These facts suggest that the leaders play an important role in keeping communities denser and more clustered, and it is the leaders that are more likely to link to other communities. Moreover, similar results for several random networks are obtained, and a theoretical lower bound of the lost internal edges is given. Our study shed the light on the further understanding and application of the internal community structure in complex networks.

  13. 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.

  14. The ground truth about metadata and community detection in networks

    CERN Document Server

    Peel, Leto; Clauset, Aaron

    2016-01-01

    Across many scientific domains, there is common need to automatically extract a simplified view or a coarse-graining of how a complex system's components interact. This general task is called community detection in networks and is analogous to searching for clusters in independent vector data. It is common to evaluate the performance of community detection algorithms by their ability to find so-called \\textit{ground truth} communities. This works well in synthetic networks with planted communities because such networks' links are formed explicitly based on the planted communities. However, there are no planted communities in real world networks. Instead, it is standard practice to treat some observed discrete-valued node attributes, or metadata, as ground truth. Here, we show that metadata are not the same as ground truth, and that treating them as such induces severe theoretical and practical problems. We prove that no algorithm can uniquely solve community detection, and we prove a general No Free Lunch the...

  15. Identifying Social Communities in Complex Communications for Network Efficiency

    Science.gov (United States)

    Hui, Pan; Yoneki, Eiko; Crowcroft, Jon; Chan, Shu-Yan

    Complex communication networks, more particular Mobile Ad Hoc Networks (MANET) and Pocket Switched Networks (PSN), rely on short range radio and device mobility to transfer data across the network. These kind of mobile networks contain duality in nature: they are radio networks at the same time also human networks, and hence knowledge from social networks can be also applicable here. In this paper, we demonstrate how identifying social communities can significantly improve the forwarding efficiencies in term of delivery ratio and delivery cost. We verify our hypothesis using data from five human mobility experiments and test on two application scenarios, asynchronous messaging and publish/subscribe service.

  16. Community detection in temporal multilayer networks, and its application to correlation networks

    CERN Document Server

    Bazzi, Marya; Williams, Stacy; McDonald, Mark; Fenn, Daniel J; Howison, Sam D

    2015-01-01

    Networks are a convenient way to represent complex systems of interacting entities. Many networks contain "communities" of nodes that are more densely connected to each other than to nodes in the rest of the network. In this paper, we investigate the detection of communities in temporal networks represented as multilayer networks. As a focal example, we study time-dependent financial-asset correlation networks. We first argue that the use of the "modularity" quality function---which is defined by comparing edge weights in an observed network to expected edge weights in a "null network"---is application-dependent. We differentiate between "null networks" and "null models" in our discussion of modularity maximization, and we highlight that the same null network can correspond to different null models. We then investigate a multilayer modularity-maximization problem to identify communities in temporal networks. Our multilayer analysis only depends on the form of the maximization problem and not on the specific q...

  17. Utilising eduroam[TM] Architecture in Building Wireless Community Networks

    Science.gov (United States)

    Huhtanen, Karri; Vatiainen, Heikki; Keski-Kasari, Sami; Harju, Jarmo

    2008-01-01

    Purpose: eduroam[TM] has already been proved to be a scalable, secure and feasible way for universities and research institutions to connect their wireless networks into a WLAN roaming community, but the advantages of eduroam[TM] have not yet been fully discovered in the wireless community networks aimed at regular consumers. This aim of this…

  18. 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.

  19. 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

    2017-01-01

    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. PMID:28235785

  20. 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.

  1. Social contagions on time-varying community networks

    CERN Document Server

    Liu, Mian-Xin; Liu, Ying; Tang, Ming; Cai, Shi-Min; Zhang, Hai-Feng

    2016-01-01

    Time-varying community structures widely exist in various real-world networks. However, the spreading dynamics on this kind of network has not been fully studied. To this end, we systematically study the effects of time-varying community structures on social contagions. We first propose a non-Markovian social contagion model on time-varying community networks based on the activity driven network model, in which an individual adopts a behavior if and only if the accumulated behavioral information it has ever received reaches a threshold. Then, we develop a mean-field theory to describe the proposed model. From theoretical analyses and numerical simulations, we find that behavior adoption in the social contagions exhibits a hierarchical feature, i.e., the behavior first quickly spreads in one of the communities, and then outbreaks in the other. Moreover, under different behavioral information transmission rates, the final behavior adoption proportion in the whole network versus the community strength shows one ...

  2. A network model for plant-pollinator community assembly.

    Science.gov (United States)

    Campbell, Colin; Yang, Suann; Albert, Réka; Shea, Katriona

    2011-01-04

    Community assembly models, usually constructed for food webs, are an important component of our understanding of how ecological communities are formed. However, models for mutualistic community assembly are still needed, especially because these communities are experiencing significant anthropogenic disturbances that affect their biodiversity. Here, we present a unique network model that simulates the colonization and extinction process of mutualistic community assembly. We generate regional source pools of species interaction networks on the basis of statistical properties reported in the literature. We develop a dynamic synchronous Boolean framework to simulate, with few free parameters, the dynamics of new mutualistic community formation from the regional source pool. This approach allows us to deterministically map out every possible trajectory of community formation. This level of detail is rarely observed in other analytic approaches and allows for thorough analysis of the dynamical properties of community formation. As for food web assembly, we find that the number of stable communities is quite low, and the composition of the source pool influences the abundance and nature of community outcomes. However, in contrast to food web assembly, stable mutualistic communities form rapidly. Small communities with minor fluctuations in species presence/absence (self-similar limit cycles) are the most common community outcome. The unique application of this Boolean network approach to the study of mutualistic community assembly offers a great opportunity to improve our understanding of these critical communities.

  3. A Heuristic Clustering Algorithm for Mining Communities in Signed Networks

    Institute of Scientific and Technical Information of China (English)

    Bo Yang; Da-You Liu

    2007-01-01

    Signed network is an important kind of complex network, which includes both positive relations and negative relations. Communities of a signed network are defined as the groups of vertices, within which positive relations are dense and between which negative relations are also dense. Being able to identify communities of signed networks is helpful for analysis of such networks. Hitherto many algorithms for detecting network communities have been developed. However, most of them are designed exclusively for the networks including only positive relations and are not suitable for signed networks.So the problem of mining communities of signed networks quickly and correctly has not been solved satisfactorily. In this paper, we propose a heuristic algorithm to address this issue. Compared with major existing methods, our approach has three distinct features. First, it is very fast with a roughly linear time with respect to network size. Second, it exhibits a good clustering capability and especially can work well with complex networks without well-defined community structures.Finally, it is insensitive to its built-in parameters and requires no prior knowledge.

  4. Modeling community structure and topics in dynamic text networks

    CERN Document Server

    Henry, Teague; Chai, Christine; Owens-Oas, Derek

    2016-01-01

    The last decade has seen great progress in both dynamic network modeling and topic modeling. This paper draws upon both areas to create a Bayesian method that allows topic discovery to inform the latent network model and the network structure to facilitate topic identification. We apply this method to the 467 top political blogs of 2012. Our results find complex community structure within this set of blogs, where community membership depends strongly upon the set of topics in which the blogger is interested.

  5. Military, Family, and Community Networks Helping with Reintegration

    Science.gov (United States)

    2010-09-01

    Limitations Ideally we would want to show that this type of network model can actually help troops, Veterans and their families reintegrate more smoothly...08-2-0655 TITLE: Military, Family, and Community Networks Helping with Reintegration PRINCIPAL INVESTIGATOR: Dr. Laurie Slone... Reintegration Dartmouth College Hanover, NH 03755 Dr. Laurie Slone A community based network to assist with the reintegration of service members and their

  6. 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...... sampling. The result is an effective multiple-membership model without the drawbacks of introducing complex definitions of "groups" and how they interact. We demonstrate results on the recovery of planted structure in synthetic networks and show very encouraging results on link prediction performances...... using multiple-networks models on a number of real-world network data sets....

  7. Terminal-Set-Enhanced Community Detection in Social Networks

    CERN Document Server

    Tong, G; Wu, W; Liu, C; Du, D-Z

    2016-01-01

    Community detection aims to reveal the community structure in a social network, which is one of the fundamental problems. In this paper we investigate the community detection problem based on the concept of terminal set. A terminal set is a group of users within which any two users belong to different communities. Although the community detection is hard in general, the terminal set can be very helpful in designing effective community detection algorithms. We first present a 2-approximation algorithm running in polynomial time for the original community detection problem. In the other issue, in order to better support real applications we further consider the case when extra restrictions are imposed on feasible partitions. For such customized community detection problems, we provide two randomized algorithms which are able to find the optimal partition with a high probability. Demonstrated by the experiments performed on benchmark networks the proposed algorithms are able to produce high-quality communities.

  8. Random field Ising model and community structure in complex networks

    Science.gov (United States)

    Son, S.-W.; Jeong, H.; Noh, J. D.

    2006-04-01

    We propose a method to determine the community structure of a complex network. In this method the ground state problem of a ferromagnetic random field Ising model is considered on the network with the magnetic field Bs = +∞, Bt = -∞, and Bi≠s,t=0 for a node pair s and t. The ground state problem is equivalent to the so-called maximum flow problem, which can be solved exactly numerically with the help of a combinatorial optimization algorithm. The community structure is then identified from the ground state Ising spin domains for all pairs of s and t. Our method provides a criterion for the existence of the community structure, and is applicable equally well to unweighted and weighted networks. We demonstrate the performance of the method by applying it to the Barabási-Albert network, Zachary karate club network, the scientific collaboration network, and the stock price correlation network. (Ising, Potts, etc.)

  9. Detecting Communities by Revised Max-flow Method in Networks

    Institute of Scientific and Technical Information of China (English)

    LIU Chuan-Jian; ZHU Zhi-Qiang; WU Jian-Liang

    2013-01-01

    A ubiquitous phenomenon in networks is the presence of communities within which the network connections are dense and between which they are sparser.This paper proposes a max-flow algorithm in bipartite networks to detect communities in general networks.Firstly,we construct a bipartite network in accordance with a general network and derive a revised max-flow problem in order to uncover the community structure.Then we present a local heuristic algorithm to find the optimal solution of the revised max-flow problem.This method is applied to a variety of real-world and artificial complex networks,and the partition results confirm its effectiveness and accuracy.

  10. 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.

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

    Directory of Open Access Journals (Sweden)

    Roderick B. Salisbury

    2012-12-01

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

  12. Community Evolution in International Migration Top1 Networks.

    Science.gov (United States)

    Peres, Mihaela; Xu, Helian; Wu, Gang

    2016-01-01

    Focusing on each country's topmost destination/origin migration relation with other countries, this study builds top1 destination networks and top1 origin networks in order to understand their skeletal construction and community dynamics. Each top1 network covers approximately 50% of the complete migrant network stock for each decade between 1960 and 2000. We investigate the community structure by implementing the Girvan-Newman algorithm and compare the number of components and communities to illustrate their differences. We find that (i) both top1 networks (origin and destination) exhibited communities with a clear structure and a surprising evolution, although 80% edges persist between each decade; (ii) top1 destination networks focused on developed countries exhibiting shorter paths and preferring more advance countries, while top1 origin networks focused both on developed as well as more substantial developing nations that presented a longer path and more stable groups; (iii) only few countries have a decisive influence on community evolution of both top1 networks. USA took the leading position as a destination country in top1 destination networks, while China and India were the main Asian emigration countries in top1 origin networks; European countries and the Russian Federation played an important role in both.

  13. Community Evolution in International Migration Top1 Networks.

    Directory of Open Access Journals (Sweden)

    Mihaela Peres

    Full Text Available Focusing on each country's topmost destination/origin migration relation with other countries, this study builds top1 destination networks and top1 origin networks in order to understand their skeletal construction and community dynamics. Each top1 network covers approximately 50% of the complete migrant network stock for each decade between 1960 and 2000. We investigate the community structure by implementing the Girvan-Newman algorithm and compare the number of components and communities to illustrate their differences. We find that (i both top1 networks (origin and destination exhibited communities with a clear structure and a surprising evolution, although 80% edges persist between each decade; (ii top1 destination networks focused on developed countries exhibiting shorter paths and preferring more advance countries, while top1 origin networks focused both on developed as well as more substantial developing nations that presented a longer path and more stable groups; (iii only few countries have a decisive influence on community evolution of both top1 networks. USA took the leading position as a destination country in top1 destination networks, while China and India were the main Asian emigration countries in top1 origin networks; European countries and the Russian Federation played an important role in both.

  14. Issues and Patterns for Community Networking.

    Science.gov (United States)

    Goddu, Roland

    Networking is a process for tapping and developing energy to address a need in a more responsive fashion. Most networks are informal and invisible, but they are real nevertheless. A new person or organization changes relationships in the network. Understanding the types of network organization enables one to systematically gain access. There are…

  15. Weighted Evolving Networks with Self-organized Communities

    Institute of Scientific and Technical Information of China (English)

    XIE Zhou; LI Xiang; WANG Xiao-Fan

    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 v≥ 1,γ> 2,and a > 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 data.sets to verify its effectiveness.

  16. Z-Score-Based Modularity for Community Detection in Networks.

    Science.gov (United States)

    Miyauchi, Atsushi; Kawase, Yasushi

    2016-01-01

    Identifying community structure in networks is an issue of particular interest in network science. The modularity introduced by Newman and Girvan is the most popular quality function for community detection in networks. In this study, we identify a problem in the concept of modularity and suggest a solution to overcome this problem. Specifically, we obtain a new quality function for community detection. We refer to the function as Z-modularity because it measures the Z-score of a given partition with respect to the fraction of the number of edges within communities. Our theoretical analysis shows that Z-modularity mitigates the resolution limit of the original modularity in certain cases. Computational experiments using both artificial networks and well-known real-world networks demonstrate the validity and reliability of the proposed quality function.

  17. Unfolding network communities by combining defensive and offensive label propagation

    CERN Document Server

    Šubelj, Lovro

    2011-01-01

    Label propagation has proven to be a fast method for detecting communities in complex networks. Recent work has also improved the accuracy and stability of the basic algorithm, however, a general approach is still an open issue. We propose different label propagation algorithms that convey two unique strategies of community formation, namely, defensive preservation and offensive expansion of communities. Furthermore, the strategies are combined in an advanced label propagation algorithm that retains the advantages of both approaches; and are enhanced with hierarchical community extraction, prominent for the use on larger networks. The proposed algorithms were empirically evaluated on different benchmarks networks with planted partition and on over 30 real-world networks of various types and sizes. The results confirm the adequacy of the propositions and give promising grounds for future analysis of (large) complex networks. Nevertheless, the main contribution of this work is in showing that different types of...

  18. On the Behaviour of Deviant Communities in Online Social Networks

    CERN Document Server

    Coletto, Mauro; Lucchese, Claudio; Silvestri, Fabrizio

    2016-01-01

    On-line social networks are complex ensembles of inter-linked communities that interact on different topics. Some communities are characterized by what are usually referred to as deviant behaviors, conducts that are commonly considered inappropriate with respect to the society's norms or moral standards. Eating disorders, drug use, and adult content consumption are just a few examples. We refer to such communities as deviant networks. It is commonly believed that such deviant networks are niche, isolated social groups, whose activity is well separated from the mainstream social-media life. According to this assumption, research studies have mostly considered them in isolation. In this work we focused on adult content consumption networks, which are present in many on-line social media and in the Web in general. We found that few small and densely connected communities are responsible for most of the content production. Differently from previous work, we studied how such communities interact with the whole soc...

  19. Correlations between community structure and link formation in complex networks.

    Directory of Open Access Journals (Sweden)

    Zhen Liu

    Full Text Available BACKGROUND: Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. METHODOLOGY/PRINCIPAL FINDINGS: Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. CONCLUSIONS/SIGNIFICANCE: Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction.

  20. 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.

  1. 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.

  2. The evolution of communities in the international oil trade network

    Science.gov (United States)

    Zhong, Weiqiong; An, Haizhong; Gao, Xiangyun; Sun, Xiaoqi

    2014-11-01

    International oil trade is a subset of global trade and there exist oil trade communities. These communities evolve over time and provide clues of international oil trade patterns. A better understanding of the international oil trade patterns is necessary for governments in policy making. To study the evolution of trade communities in the international oil trade network, we set up unweighted and weighted oil trade network models based on complex network theory using data from 2002 to 2011. We detected the communities in the oil trade networks and analyzed their evolutionary properties and stabilities over time. We found that the unweighted and weighted international oil trade networks show many different features in terms of community number, community scale, distribution of countries, quality of partitions, and stability of communities. Two turning points occurred in the evolution of community stability in the international oil trade network. One is the year 2004-2005 which correlates with changes in demand and supply in the world oil market after the Iraq War, and the other is the year 2008-2009 which is connected to the 2008 financial crisis. Different causations of instability show different features and this should be considered by policy makers.

  3. A layer reduction based community detection algorithm on multiplex networks

    Science.gov (United States)

    Wang, Xiaodong; Liu, Jing

    2017-04-01

    Detecting hidden communities is important for the analysis of complex networks. However, many algorithms have been designed for single layer networks (SLNs) while just a few approaches have been designed for multiplex networks (MNs). In this paper, we propose an algorithm based on layer reduction for detecting communities on MNs, which is termed as LRCD-MNs. First, we improve a layer reduction algorithm termed as neighaggre to combine similar layers and keep others separated. Then, we use neighaggre to find the community structure hidden in MNs. Experiments on real-life networks show that neighaggre can obtain higher relative entropy than the other algorithm. Moreover, we apply LRCD-MNs on some real-life and synthetic multiplex networks and the results demonstrate that, although LRCD-MNs does not have the advantage in terms of modularity, it can obtain higher values of surprise, which is used to evaluate the quality of partitions of a network.

  4. A Comparative Analysis of Community Detection Algorithms on Artificial Networks

    CERN Document Server

    Yang, Zhao; Tessone, Claudio Juan

    2016-01-01

    Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms' computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most sui...

  5. 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.

  6. Impulsive Cluster Synchronization in Community Network with Nonidentical Nodes

    Institute of Scientific and Technical Information of China (English)

    邓丽萍; 吴召艳

    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.

  7. Evolutionary prisoner's dilemma game on highly clustered community networks

    Institute of Scientific and Technical Information of China (English)

    Liu Yong-Kui; Li Zhi; Chen Xiao-Jie; Wang Long

    2009-01-01

    This paper studies the evolutionary prisoner's dilemma game on a highly clustered community network in which the clustering coefficient and the community size can be tuned. It finds that the clustering coefficient in such a degreehomogeneous network inhibits the emergence of cooperation for the entire range of the payoff parameter. Moreover,it finds that the community size can also have a marked influence on the evolution of cooperation, with a larger community size leading to not only a lower cooperation level but also a smaller threshold of the payoff parameter above which cooperators become extinct.

  8. Detect overlapping and hierarchical community structure in networks

    CERN Document Server

    Shen, Huawei; Cai, Kai; Hu, Mao-Bin

    2008-01-01

    Clustering and community structure is crucial for many network systems and the related dynamic processes. It has been shown that communities are usually overlapping and hierarchical. However, previous methods investigate these two properties of community structure separately. This paper propose an algorithm (EAGLE) to detect both the overlapping and hierarchical properties of complex community structure together. This algorithm deals with the set of maximal cliques and adopts an agglomerative framework. The quality function of modularity is extended to evaluate the goodness of a cover. The examples of application to real world networks give excellent results.

  9. Extraction of hidden information by efficient community detection in networks

    Science.gov (United States)

    Lee, Jooyoung; Lee, Juyong; Gross, Steven

    2013-03-01

    Currently, we are overwhelmed by a deluge of experimental data, and network physics has the potential to become an invaluable method to increase our understanding of large interacting datasets. However, this potential is often unrealized for two reasons: uncovering the hidden community structure of a network, known as community detection, is difficult, and further, even if one has an idea of this community structure, it is not a priori obvious how to efficiently use this information. Here, to address both of these issues, we, first, identify optimal community structure of given networks in terms of modularity by utilizing a recently introduced community detection method. Second, we develop an approach to use this community information to extract hidden information from a network. When applied to a protein-protein interaction network, the proposed method outperforms current state-of-the-art methods that use only the local information of a network. The method is generally applicable to networks from many areas. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (No. 20120001222).

  10. 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.

  11. 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....... In this paper we propose to use relational Bayesian networks for the specification of probabilistic network models, and develop inference techniques that solve the community detection problem based on these models. The use of relational Bayesian networks as a flexible high-level modeling framework enables us...... to express different models capturing different aspects of community detection in multiplex networks in a coherent manner, and to use a single inference mechanism for all models....

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

    Science.gov (United States)

    2013-12-26

    ... COMMISSION 47 CFR Part 73 Television Broadcasting Services; Oklahoma City, Oklahoma AGENCY: Federal... filed by Family Broadcasting Group, Inc. (``Family Broadcasting''), the licensee of station KSBI(TV... voluntary relocation agreement with Lower 700 MHz A Block licensees. Family Broadcasting has entered...

  13. Growing networks of overlapping communities with internal structure

    Science.gov (United States)

    Young, Jean-Gabriel; Hébert-Dufresne, Laurent; Allard, Antoine; Dubé, Louis J.

    2016-08-01

    We introduce an intuitive model that describes both the emergence of community structure and the evolution of the internal structure of communities in growing social networks. The model comprises two complementary mechanisms: One mechanism accounts for the evolution of the internal link structure of a single community, and the second mechanism coordinates the growth of multiple overlapping communities. The first mechanism is based on the assumption that each node establishes links with its neighbors and introduces new nodes to the community at different rates. We demonstrate that this simple mechanism gives rise to an effective maximal degree within communities. This observation is related to the anthropological theory known as Dunbar's number, i.e., the empirical observation of a maximal number of ties which an average individual can sustain within its social groups. The second mechanism is based on a recently proposed generalization of preferential attachment to community structure, appropriately called structural preferential attachment (SPA). The combination of these two mechanisms into a single model (SPA+) allows us to reproduce a number of the global statistics of real networks: The distribution of community sizes, of node memberships, and of degrees. The SPA+ model also predicts (a) three qualitative regimes for the degree distribution within overlapping communities and (b) strong correlations between the number of communities to which a node belongs and its number of connections within each community. We present empirical evidence that support our findings in real complex networks.

  14. 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.

  15. 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.

  16. A local algorithm for detecting community structures in dynamic networks

    CERN Document Server

    Massaro, Emanuele; Guazzini, Andrea; Passarella, Andrea; Bagnoli, Franco

    2013-01-01

    The emergence and the global adaptation of mobile devices has influenced human interactions at the individual, community, and social levels leading to the so called Cyber-Physical World (CPW) convergence scenario [1]. One of the most important features of CPW is the possibility of exploiting information about the structure of social communities of users, that manifest through joint movement patterns and frequency of physical co-location: mobile devices of users that belong to the same social community are likely to "see" each other (and thus be able to communicate through ad hoc networking techniques) more frequently and regularly than devices outside of the community. In mobile opportunistic networks, this fact can be exploited, for example, to optimize networking operations such as forwarding and dissemination of messages. In this paper we present a novel local cognitive-inspired algorithm for revealing the structure of these dynamic social networks by exploiting information about physical encounters, logge...

  17. Measuring robustness of community structure in complex networks

    CERN Document Server

    Li, Hui-Jia; Chen, Luonan

    2015-01-01

    The theory of community structure is a powerful tool for real networks, which can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the robustness of community structure is an urgent and important task. In this letter, we employ the critical threshold of resolution parameter in Hamiltonian function, $\\gamma_C$, to measure the robustness of a network. According to spectral theory, a rigorous proof shows that the index we proposed is inversely proportional to robustness of community structure. Furthermore, by utilizing the co-evolution model, we provides a new efficient method for computing the value of $\\gamma_C$. The research can be applied to broad clustering problems in network analysis and data mining due to its solid mathematical basis and experimental effects.

  18. Physical Heterogeneity and Aquatic Community Function in River Networks

    Science.gov (United States)

    The geomorphological character of a river network provides the template upon which evolution acts to create unique biological communities. Deciphering commonly observed patterns and processes within riverine landscapes resulting from the interplay between physical and biological...

  19. Community evolution mining and analysis in social network

    Science.gov (United States)

    Liu, Hongtao; Tian, Yuan; Liu, Xueyan; Jian, Jie

    2017-03-01

    With the development of digital and network technology, various social platforms emerge. These social platforms have greatly facilitated access to information, attracting more and more users. They use these social platforms every day to work, study and communicate, so every moment social platforms are generating massive amounts of data. These data can often be modeled as complex networks, making large-scale social network analysis possible. In this paper, the existing evolution classification model of community has been improved based on community evolution relationship over time in dynamic social network, and the Evolution-Tree structure is proposed which can show the whole life cycle of the community more clearly. The comparative test result shows that the improved model can excavate the evolution relationship of the community well.

  20. A Deep Stochastic Model for Detecting Community in Complex Networks

    Science.gov (United States)

    Fu, Jingcheng; Wu, Jianliang

    2017-01-01

    Discovering community structures is an important step to understanding the structure and dynamics of real-world networks in social science, biology and technology. In this paper, we develop a deep stochastic model based on non-negative matrix factorization to identify communities, in which there are two sets of parameters. One is the community membership matrix, of which the elements in a row correspond to the probabilities of the given node belongs to each of the given number of communities in our model, another is the community-community connection matrix, of which the element in the i-th row and j-th column represents the probability of there being an edge between a randomly chosen node from the i-th community and a randomly chosen node from the j-th community. The parameters can be evaluated by an efficient updating rule, and its convergence can be guaranteed. The community-community connection matrix in our model is more precise than the community-community connection matrix in traditional non-negative matrix factorization methods. Furthermore, the method called symmetric nonnegative matrix factorization, is a special case of our model. Finally, based on the experiments on both synthetic and real-world networks data, it can be demonstrated that our algorithm is highly effective in detecting communities.

  1. 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.

  2. Facilitating community building in Learning Networks through peer tutoring in ad hoc transient communities

    NARCIS (Netherlands)

    Kester, Liesbeth; Sloep, Peter; Van Rosmalen, Peter; Brouns, Francis; Koné, Malik; Koper, Rob

    2006-01-01

    De volledige referentie is: Kester, L., Sloep, P. B., Van Rosmalen, P., Brouns, F., Koné, M., & Koper, R. (2007). Facilitating Community Building in Learning Networks Through Peer-Tutoring in Ad Hoc Transient Communities. International Journal of Web based Communities, 3(2), 198-205.

  3. Information dynamics algorithm for detecting communities in networks

    CERN Document Server

    Massaro, E; Bagnoli, F; Liò, P

    2011-01-01

    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 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 ...

  4. Exploring the limits of community detection strategies in complex networks

    CERN Document Server

    Aldecoa, Rodrigo

    2013-01-01

    The characterization of network community structure has profound implications in several scientific areas. Therefore, testing the algorithms developed to establish the optimal division of a network into communities is a fundamental problem in the field. We performed here a highly detailed evaluation of community detection algorithms, which has two main novelties: 1) using complex closed benchmarks, which provide precise ways to assess whether the solutions generated by the algorithms are optimal; and, 2) A novel type of analysis, based on hierarchically clustering the solutions suggested by multiple community detection algorithms, which allows to easily visualize how different are those solutions. Surprise, a global parameter that evaluates the quality of a partition, confirms the power of these analyses. We show that none of the community detection algorithms tested provide consistently optimal results in all networks and that Surprise maximization, obtained by combining multiple algorithms, obtains quasi-op...

  5. Generalized method for finding community structures in networks

    CERN Document Server

    Chang, Chang

    2013-01-01

    To date, most algorithms aiming to find community structures in networks mainly focus on unipartite or bipartite networks. However, to our knowledge, there is no algorithm specifically designed for the mixture network, a third type defined in our paper that represents a wide range of real-world networks. Interestingly, unipartite and bipartite networks can be viewed as limiting cases of a mixture network, suggesting that the mixture network can be considered as a general condition. Based on this observation, we propose a probabilistic model based on the link community model for a unipartite, undirected network [B. Ball, B. Karrer, and M. E. Newman, Phys. Rev. E 84, 036103 (2011)] by redefining this model in the context of a bipartite network and generalizing the bipartite network version model to a mixture network, the general condition, which can be used to find modules in unipartite, bipartite, and mixture networks in a unified framework. We show that both the model of Ball et al. (unipartite, undirected ne...

  6. SCOUT: simultaneous time segmentation and community detection in dynamic networks

    Science.gov (United States)

    Hulovatyy, Yuriy; Milenković, Tijana

    2016-11-01

    Many evolving complex real-world systems can be modeled via dynamic networks. An important problem in dynamic network research is community detection, which finds groups of topologically related nodes. Typically, this problem is approached by assuming either that each time point has a distinct community organization or that all time points share a single community organization. The reality likely lies between these two extremes. To find the compromise, we consider community detection in the context of the problem of segment detection, which identifies contiguous time periods with consistent network structure. Consequently, we formulate a combined problem of segment community detection (SCD), which simultaneously partitions the network into contiguous time segments with consistent community organization and finds this community organization for each segment. To solve SCD, we introduce SCOUT, an optimization framework that explicitly considers both segmentation quality and partition quality. SCOUT addresses limitations of existing methods that can be adapted to solve SCD, which consider only one of segmentation quality or partition quality. In a thorough evaluation, SCOUT outperforms the existing methods in terms of both accuracy and computational complexity. We apply SCOUT to biological network data to study human aging.

  7. Backtalk: Adult Services in Oklahoma.

    Science.gov (United States)

    Giblon, Della L.; Henke, Esther Mae

    1980-01-01

    Describes projects of Oklahoma libraries designed to combat the problem of illiteracy among adults and explains Oklahoma Image, a humanities effort aimed at attracting out-of-school adults to public libraries by focusing on the state's multicultural heritage. Column also reports adult service news from other states. (JD)

  8. Guidelines to foster interaction in online communities for Learning Networks

    NARCIS (Netherlands)

    Berlanga, Adriana; Rusman, Ellen; Bitter-Rijpkema, Marlies; Sloep, Peter

    2009-01-01

    The original publication is available from www.springerlink.com. Berlanga, A., Rusman, E., Bitter-Rijpkema, M., & Sloep, P. B. (2009). Guidelines to foster interaction in online communities for Learning Networks. In R. Koper (Ed.), Learning Network Services for Professional Development (pp. 27-42).

  9. 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 ide

  10. Emerging Communities: Integrating Networked Information into Library Services (Book Review).

    Science.gov (United States)

    Afifi, Marianne

    1995-01-01

    Reviews this collection of papers, edited by Ann P. Bishop, which present the current state of networking as it relates to libraries and the community. Recommends the book as a compendium of lessons, learned and to be learned, as networked information becomes an integral and necessary part of the library world. (JMV)

  11. Pinning controllability of complex networks with community structure.

    Science.gov (United States)

    Miao, Qingying; Tang, Yang; Kurths, Jürgen; Fang, Jian-an; Wong, W K

    2013-09-01

    In this paper, we study the controllability of networks with different numbers of communities and various strengths of community structure. By means of simulations, we show that the degree descending pinning scheme performs best among several considered pinning schemes under a small number of pinned nodes, while the degree ascending pinning scheme is becoming more powerful by increasing the number of pinned nodes. It is found that increasing the number of communities or reducing the strength of community structure is beneficial for the enhancement of the controllability. Moreover, it is revealed that the pinning scheme with evenly distributed pinned nodes among communities outperforms other kinds of considered pinning schemes.

  12. Mobilizing Community Museum Networks in Mexico--and Beyond.

    Science.gov (United States)

    Healy, Kevin

    2003-01-01

    Since the late 1980s, a network of community museums has spread throughout Oaxaca (Mexico), serving as an autonomous force for broad-based cultural development, supporting the maintenance and revitalization of local Indigenous cultures, countering Western cultural hegemony, and involving Indigenous communities in museum development and related…

  13. The many facets of community detection in complex networks

    CERN Document Server

    Schaub, Michael T; Rosvall, Martin; Lambiotte, Renaud

    2016-01-01

    Community detection, the decomposition of a graph into meaningful building blocks, has been a core research topic in network science over the past years. Since a precise notion of what constitutes a community has remained evasive, community detection algorithms have often been compared on benchmark graphs with a particular form of community structure, and classified based on the mathematical techniques they employ. However, this can be misleading because apparent similarities in their mathematical machinery can disguise entirely different objectives. Here we provide a focused review of the different motivations that underpin community detection. This problem-driven classification is useful in applied network science, where it is important to select an appropriate algorithm for the given purpose. Moreover, highlighting the different facets of community detection also delineates the many lines of research, and points out open directions and avenues for future research.

  14. Defining least community as a homogeneous group in complex networks

    Science.gov (United States)

    Jiang, Bin; Ma, Ding

    2015-06-01

    This paper introduces a new concept of least community that is as homogeneous as a random graph, and develops a new community detection algorithm from the perspective of homogeneity or heterogeneity. Based on this concept, we adopt head/tail breaks-a newly developed classification scheme for data with a heavy-tailed distribution-and rely on edge betweenness given its heavy-tailed distribution to iteratively partition a network into many heterogeneous and homogeneous communities. Surprisingly, the derived communities for any self-organized and/or self-evolved large networks demonstrate very striking power laws, implying that there are far more small communities than large ones. This notion of far more small things than large ones constitutes a new fundamental way of thinking for community detection.

  15. Defining Least Community as a Homogeneous Group in Complex Networks

    CERN Document Server

    Jiang, Bin

    2015-01-01

    This paper introduces a new concept of least community that is as homogeneous as a random graph, and develops a new community detection algorithm from the perspective of homogeneity or heterogeneity. Based on this concept, we adopt head/tail breaks - a newly developed classification scheme for data with a heavy-tailed distribution - and rely on edge betweenness given its heavy-tailed distribution to iteratively partition a network into many heterogeneous and homogeneous communities. Surprisingly, the derived communities for any self-organized and/or self-evolved large networks demonstrate very striking power laws, implying that there are far more small communities than large ones. This notion of far more small things than large ones constitutes a new fundamental way of thinking for community detection. Keywords: head/tail breaks, ht-index, scaling, k-means, natural breaks, and classification

  16. Chinese temples and transnational networks: Hokkien communities in Singapore

    OpenAIRE

    Hue, G.

    2016-01-01

    This paper is intended as an overview of different categories of Chinese temples and other institutions in Singapore and their transnational networks, in particularly on Hokkien communiities in Singapore. It focusing on some preliminary research findings related to this Hokkien communities and their religious networks, examines the Minnan (South Fujian) Protector Gods (Regional or Village temple Main Gods) and the Minnan Taoist Altars, as well as their religious networks connecting Fujian, Ch...

  17. Finding missing edges and communities in incomplete networks

    OpenAIRE

    Yan, Bowen; Gregory, Steve

    2011-01-01

    Many algorithms have been proposed for predicting missing edges in networks, but they do not usually take account of which edges are missing. We focus on networks which have missing edges of the form that is likely to occur in real networks, and compare algorithms that find these missing edges. We also investigate the effect of this kind of missing data on community detection algorithms.

  18. A SOCIAL NETWORK ANALYSIS APPROACH TO UNDERSTAND CHANGES IN A CANCER DISPARITIES COMMUNITY PARTNERSHIP NETWORK.

    Science.gov (United States)

    Luque, John S; Tyson, Dinorah Martinez; Bynum, Shalanda A; Noel-Thomas, Shalewa; Wells, Kristen J; Vadaparampil, Susan T; Gwede, Clement K; Meade, Cathy D

    2011-11-01

    The Tampa Bay Community Cancer Network (TBCCN) is one of the Community Network Program sites funded (2005-10) by the National Cancer Institute's Center to Reduce Cancer Health Disparities. TBCCN was tasked to form a sustainable, community-based partnership network focused on the goal of reducing cancer health disparities among racial-ethnic minority and medically underserved populations. This article reports evaluation outcome results from a social network analysis and discusses the varying TBCCN partner roles-in education, training, and research-over a span of three years (2007-09). The network analysis included 20 local community partner organizations covering a tricounty area in Southwest Florida. In addition, multiple externally funded, community-based participatory research pilot projects with community-academic partners have either been completed or are currently in progress, covering research topics including culturally targeted colorectal and prostate cancer screening education, patient navigation focused on preventing cervical cancer in rural Latinas, and community perceptions of biobanking. The social network analysis identified a trend toward increased network decentralization based on betweenness centrality and overall increase in number of linkages, suggesting network sustainability. Degree centrality, trust, and multiplexity exhibited stability over the three-year time period. These results suggest increased interaction and interdependence among partner organizations and less dependence on the cancer center. Social network analysis enabled us to quantitatively evaluate partnership network functioning of TBCCN in terms of network structure and information and resources flows, which are integral to understanding effective coalition practice based on Community Coalition Action Theory ( Butterfoss and Kegler 2009). Sharing the results of the social network analysis with the partnership network is an important component of our coalition building efforts. A

  19. Power-law relations in random networks with communities

    CERN Document Server

    Stegehuis, Clara; van Leeuwaarden, Johan S H

    2016-01-01

    Most random graph models are locally tree-like - do not contain short cycles - which makes them unfit for modeling networks with a community structure. We introduce the hierarchical configuration model (HCM), a generalization of the configuration model that includes community structures, while properties such as the size of the giant component, and the size of the giant percolating cluster under bond percolation can still be derived analytically. Furthermore, viewing real-world networks as realizations of the HCM reveals two previously unobserved power-law relations: between the number of edges inside a community and the community sizes, and between the number of edges going out of a community and the community sizes. Many real-world networks have both a community structure and a power-law degree distribution. We relate the power-law exponent $\\tau$ of the degree distribution with the power-law exponent of the community size distribution $\\gamma$. In the special case of dense communities, this relation takes ...

  20. Social Networks: Gated Communities or Free Cantons?

    CERN Document Server

    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?

  1. Hypothesis Testing for Automated Community Detection in Networks

    CERN Document Server

    Bickel, Peter J

    2013-01-01

    Community detection in networks is a key exploratory tool with applications in a diverse set of areas, ranging from finding communities in social and biological networks to identifying link farms in the World Wide Web. The problem of finding communities or clusters in a network has received much attention from statistics, physics and computer science. However, most clustering algorithms assume knowledge of the number of clusters k. In this paper we propose to automatically determine k in a graph generated from a Stochastic Blockmodel. Our main contribution is twofold; first, we theoretically establish the limiting distribution of the principal eigenvalue of the suitably centered and scaled adjacency matrix, and use that distribution for our hypothesis test. Secondly, we use this test to design a recursive bipartitioning algorithm. Using quantifiable classification tasks on real world networks with ground truth, we show that our algorithm outperforms existing probabilistic models for learning overlapping clust...

  2. Complex brain networks: From topological communities to clustered dynamics

    Indian Academy of Sciences (India)

    Lucia Zemanová; Gorka Zamora-López; Changsong Zhou; Jürgen Kurths

    2008-06-01

    Recent research has revealed a rich and complicated network topology in the cortical connectivity of mammalian brains. A challenging task is to understand the implications of such network structures on the functional organisation of the brain activities. We investigate synchronisation dynamics on the corticocortical network of the cat by modelling each node of the network (cortical area) with a subnetwork of interacting excitable neurons. We find that this network of networks displays clustered synchronisation behaviour and the dynamical clusters closely coincide with the topological community structures observed in the anatomical network. The correlation between the firing rate of the areas and the areal intensity is additionally examined. Our results provide insights into the relationship between the global organisation and the functional specialisation of the brain cortex.

  3. 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.

  4. A local immunization strategy for networks with overlapping community structure

    Science.gov (United States)

    Taghavian, Fatemeh; Salehi, Mostafa; Teimouri, Mehdi

    2017-02-01

    Since full coverage treatment is not feasible due to limited resources, we need to utilize an immunization strategy to effectively distribute the available vaccines. On the other hand, the structure of contact network among people has a significant impact on epidemics of infectious diseases (such as SARS and influenza) in a population. Therefore, network-based immunization strategies aim to reduce the spreading rate by removing the vaccinated nodes from contact network. Such strategies try to identify more important nodes in epidemics spreading over a network. In this paper, we address the effect of overlapping nodes among communities on epidemics spreading. The proposed strategy is an optimized random-walk based selection of these nodes. The whole process is local, i.e. it requires contact network information in the level of nodes. Thus, it is applicable to large-scale and unknown networks in which the global methods usually are unrealizable. Our simulation results on different synthetic and real networks show that the proposed method outperforms the existing local methods in most cases. In particular, for networks with strong community structures, high overlapping membership of nodes or small size communities, the proposed method shows better performance.

  5. 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

    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......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...

  6. 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....

  7. George Washington Community High School: analysis of a partnership network.

    Science.gov (United States)

    Bringle, Robert G; Officer, Starla D H; Grim, Jim; Hatcher, Julie A

    2009-01-01

    After five years with no public schools in their community, residents and neighborhood organizations of the Near Westside of Indianapolis advocated for the opening of George Washington Community High School (GWCHS). As a neighborhood in close proximity to the campus of Indiana University-Purdue University Indianapolis, the Near Westside and campus worked together to address this issue and improve the educational success of youth. In fall 2000, GWCHS opened as a community school and now thrives as a national model, due in part to its network of community relationships. This account analyzes the development of the school by focusing on the relationships among the university, the high school, community organizations, and the residents of the Near Westside and highlights the unique partnership between the campus and school by defining the relational qualities and describing the network created to make sustainable changes with the high school.

  8. 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.

  9. Feature Analysis and Modeling of the Network Community Structure

    Institute of Scientific and Technical Information of China (English)

    袁超; 柴毅; 魏善碧

    2012-01-01

    Community structure has an important influence on the structural and dynamic characteristics of the complex systems.So it has attracted a large number of researchers.However,due to its complexity,the mechanism of action of the community structure is still not clear to this day.In this paper,some features of the community structure have been discussed.And a constraint model of the community has been deduced.This model is effective to identify the communities.And especially,it is effective to identify the overlapping nodes between the communities.Then a community detection algorithm,which has linear time complexity,is proposed based on this constraint model,a proposed node similarity model and the Modularity Q.Through some experiments on a series of real-world and synthetic networks,the high performances of the algorithm and the constraint model have been illustrated.

  10. Ground water in Oklahoma

    Science.gov (United States)

    Leonard, A.R.

    1960-01-01

    One of the first requisites for the intelligent planning of utilization and control of water and for the administration of laws relating to its use is data on the quantity, quality, and mode of occurrence of the available supplies. The collection, evaluation and interpretation, and publication of such data are among the primary functions of the U.S. Geological Survey. Since 1895 the Congress has made appropriations to the Survey for investigation of the water resources of the Nation. In 1929 the Congress adopted the policy of dollar-for-dollar cooperation with the States and local governmental agencies in water-resources investigations of the U.S. Geological Survey. In 1937 a program of ground-water investigations was started in cooperation with the Oklahoma Geological Survey, and in 1949 this program was expanded to include cooperation with the Oklahoma Planning and Resources Board. In 1957 the State Legislature created the Oklahoma Water Resources Board as the principal State water agency and it became the principal local cooperator. The Ground Water Branch of the U.S. Geological Survey collects, analyzes, and evaluates basic information on ground-water resources and prepares interpretive reports based on those data. Cooperative ground-water work was first concentrated in the Panhandle counties. During World War II most work was related to problems of water supply for defense requirements. Since 1945 detailed investigations of ground-water availability have been made in 11 areas, chiefly in the western and central parts of the State. In addition, water levels in more than 300 wells are measured periodically, principally in the western half of the State. In Oklahoma current studies are directed toward determining the source, occurrence, and availability of ground water and toward estimating the quantity of water and rate of replenishment to specific areas and water-bearing formations. Ground water plays an important role in the economy of the State. It is

  11. Analysis of community structure in networks of correlated data

    Energy Technology Data Exchange (ETDEWEB)

    Gomez, S.; Jensen, P.; Arenas, A.

    2008-12-25

    We present a reformulation of modularity that allows the analysis of the community structure in networks of correlated data. The new modularity preserves the probabilistic semantics of the original definition even when the network is directed, weighted, signed, and has self-loops. This is the most general condition one can find in the study of any network, in particular those defined from correlated data. We apply our results to a real network of correlated data between stores in the city of Lyon (France).

  12. A DC programming approach for finding communities in networks.

    Science.gov (United States)

    Le Thi, Hoai An; Nguyen, Manh Cuong; Dinh, Tao Pham

    2014-12-01

    Automatic discovery of community structures in complex networks is a fundamental task in many disciplines, including physics, biology, and the social sciences. The most used criterion for characterizing the existence of a community structure in a network is modularity, a quantitative measure proposed by Newman and Girvan (2004). The discovery community can be formulated as the so-called modularity maximization problem that consists of finding a partition of nodes of a network with the highest modularity. In this letter, we propose a fast and scalable algorithm called DCAM, based on DC (difference of convex function) programming and DCA (DC algorithms), an innovative approach in nonconvex programming framework for solving the modularity maximization problem. The special structure of the problem considered here has been well exploited to get an inexpensive DCA scheme that requires only a matrix-vector product at each iteration. Starting with a very large number of communities, DCAM furnishes, as output results, an optimal partition together with the optimal number of communities [Formula: see text]; that is, the number of communities is discovered automatically during DCAM's iterations. Numerical experiments are performed on a variety of real-world network data sets with up to 4,194,304 nodes and 30,359,198 edges. The comparative results with height reference algorithms show that the proposed approach outperforms them not only on quality and rapidity but also on scalability. Moreover, it realizes a very good trade-off between the quality of solutions and the run time.

  13. The Oklahoma Amish: Survival of an Ethnic Subculture.

    Science.gov (United States)

    Thompson, William E.

    1981-01-01

    Focuses on ways that an Oklahoma Amish community creates, defines, maintains, and manipulates various symbols in an effort to deal with five problems that threaten the survival of Amish life: disenchanted youth, inroads of modernity, tourism, vanishing farm land, and governmental intervention. (Author/GC)

  14. 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.

  15. Communities, roles, and informational organigrams in directed networks: the Twitter network of the UK riots

    CERN Document Server

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

    2013-01-01

    Directionality is a crucial ingredient in many complex networks, in which information, energy or influence are transmitted. We showcase a framework for flow-based analysis for directed networks through the study of a network of influential Twitter users during the 2011 riots in England. Our analysis extracts nuanced descriptions of the network in terms of a multiresolution structure of interest communities within which flows of information are contained and reinforced. Such communities identify groups according to location, profession, employer, and topic, and are largely undetected if edge directionality is ignored. The flow structure also allows us to generate an interest distance, affording a personalised view of the network from any given user. A complementary flow-based analysis leads to a classification of users into five roles beyond the standard leader-follower dichotomy. Integrating both viewpoints, we find that interest communities fall into distinct informational organigrams, which reflect their mi...

  16. 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.

  17. Identifying Influential Spreaders of Epidemics on Community Networks

    CERN Document Server

    Luo, Shi-Long; Kang, Li

    2016-01-01

    An efficient strategy for the identification of influential spreaders that could be used to control epidemics within populations would be of considerable importance. Generally, populations are characterized by its community structures and by the heterogeneous distributions of weak ties among nodes bridging over communities. A strategy for community networks capable of identifying influential spreaders that accelerate the spread of disease is here proposed. In this strategy, influential spreaders serve as target nodes. This is based on the idea that, in k-shell decomposition, weak ties and strong ties are processed separately. The strategy was used on empirical networks constructed from online social networks, and results indicated that this strategy is more accurate than other strategies. Its effectiveness stems from the patterns of connectivity among neighbors, and it successfully identified the important nodes. In addition, the performance of the strategy remained robust even when there were errors in the s...

  18. 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…

  19. OKLAHOMA BANK BEHAVIOR AND THE PANIC OF 1907

    Directory of Open Access Journals (Sweden)

    Loren Gatch

    2010-01-01

    Full Text Available While the Panic of 1907 began in New York City, its effects reverberated throughout the United States. This article examines the response of Oklahoma banks to the nationwide restriction of payments beginning in late October of that year. Despite the widespread support of local communities for their banks, Oklahoma institutions cut back on loans and built up their reserves to a greater degree than did country banks nationwide. Of particular concern for Oklahomans in late 1907 was the financing of the cotton crop. Balance sheet evidence suggests that Oklahoma banks in cotton-growing areas reacted more defensively than did banks in wheat-growing areas, where the harvest had already been completed. A multiple regression model exploring changes in Oklahoma bank reserves before and after the Panic not only confirms the relevance of cotton as a factor but also points to bank size, the use of cash substitutes, and political jurisdiction as variables that influenced the extent to which Oklahoma banks increased their reserves in response to the Panic.

  20. Social networking for web-based communities

    NARCIS (Netherlands)

    Issa, T.; Kommers, P.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 centu

  1. 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.

  2. Virality Prediction and Community Structure in Social Networks

    Science.gov (United States)

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

    2013-01-01

    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. PMID:23982106

  3. Trophic network models explain instability of Early Triassic terrestrial communities.

    Science.gov (United States)

    Roopnarine, Peter D; Angielczyk, Kenneth D; Wang, Steve C; Hertog, Rachel

    2007-09-07

    Studies of the end-Permian mass extinction have emphasized potential abiotic causes and their direct biotic effects. Less attention has been devoted to secondary extinctions resulting from ecological crises and the effect of community structure on such extinctions. Here we use a trophic network model that combines topological and dynamic approaches to simulate disruptions of primary productivity in palaeocommunities. We apply the model to Permian and Triassic communities of the Karoo Basin, South Africa, and show that while Permian communities bear no evidence of being especially susceptible to extinction, Early Triassic communities appear to have been inherently less stable. Much of the instability results from the faster post-extinction diversification of amphibian guilds relative to amniotes. The resulting communities differed fundamentally in structure from their Permian predecessors. Additionally, our results imply that changing community structures over time may explain long-term trends like declining rates of Phanerozoic background extinction.

  4. 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.

  5. Modelling opinion formation driven communities in social networks

    CERN Document Server

    Iñiguez, Gerardo; Kertész, János; Kaski, Kimmo K

    2010-01-01

    In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter $\\alpha$, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realized by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter $\\alpha$ and its influence on the conformation of opinion and the size of the resulting communities. We present numer...

  6. Dynamical evolution of the community structure of complex earthquake network

    CERN Document Server

    Abe, Sumiyoshi

    2012-01-01

    Earthquake network is known to be complex in the sense that it is scale-free, small-world, hierarchically organized and assortatively mixed. Here, the time evolution of earthquake networks is analyzed around main shocks in the context of the community structure. It is found that the maximum of the modularity measure quantifying existence of communities exhibits a peculiar behavior: its maximum value stays at a large value before a main shock, suddenly drops to a small values at the main shock, and then increases to relax to a large value again relatively slowly. In this way, a main shock is characterized in the language of theory of complex networks. The result is also interpreted in terms of the clustering structure of the earthquake network.

  7. Temporal prediction of epidemic patterns in community networks

    CERN Document Server

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

    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 analyze these three timescales and their dependence on the average node degree of each c...

  8. A graph clustering method for community detection in complex networks

    Science.gov (United States)

    Zhou, HongFang; Li, Jin; Li, JunHuai; Zhang, FaCun; Cui, YingAn

    2017-03-01

    Information mining from complex networks by identifying communities is an important problem in a number of research fields, including the social sciences, biology, physics and medicine. First, two concepts are introduced, Attracting Degree and Recommending Degree. Second, a graph clustering method, referred to as AR-Cluster, is presented for detecting community structures in complex networks. Third, a novel collaborative similarity measure is adopted to calculate node similarities. In the AR-Cluster method, vertices are grouped together based on calculated similarity under a K-Medoids framework. Extensive experimental results on two real datasets show the effectiveness of AR-Cluster.

  9. Review of Learning in ICT-enabled Networks and Communities

    OpenAIRE

    ALA-MUTKA Kirsti Maria

    2009-01-01

    This report is part of a project launched by IPTS with DG Education and Culture to study the innovations for learning, which are emerging in the new collaborative and informal settings enabled by ICT. The report gathers and analyses evidence from learning opportunities that are emerging in ICT-enabled networks and communities. In these new virtual spaces, participation is motivated by an interest to a topic, by creative production and by search for social connection. Online networks and commu...

  10. Community Networks and the Nature of Emergence in Civil Society

    Directory of Open Access Journals (Sweden)

    Jenny Onyx

    2010-03-01

    Full Text Available Our research challenges the limitations of extant knowledge of social formation by its focus on the ordinary, everyday lived reality of maintaining community and on identifying its operations from the internal perspective of civil society. We aim to explore the actual mobilising processes and structures that underpin the formation of social capital in the community. We examine how networks emerge and operate.

  11. 78 FR 41088 - Solicitation for a Cooperative Agreement-Support Services for Community Services Division Networks

    Science.gov (United States)

    2013-07-09

    ... Services Division Networks AGENCY: National Institute of Corrections, U.S., Department of Justice. ACTION... support services to NIC Community Services Division sponsored networks. The networks are designed for NIC... practices. The NIC Community Services Division currently sponsors 5 networks: (1) Community...

  12. Comparison and validation of community structures in complex networks

    CERN Document Server

    Gustafsson, M; Lombardi, A; Gustafsson, Mika; Hornquist, Michael; Lombardi, Anna

    2006-01-01

    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 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 f...

  13. A local fuzzy method based on “p-strong” community for detecting communities in networks

    Science.gov (United States)

    Yi, Shen; Gang, Ren; Yang, Liu; Jia-Li, Xu

    2016-06-01

    In this paper, we propose a local fuzzy method based on the idea of “p-strong” community to detect the disjoint and overlapping communities in networks. In the method, a refined agglomeration rule is designed for agglomerating nodes into local communities, and the overlapping nodes are detected based on the idea of making each community strong. We propose a contribution coefficient to measure the contribution of an overlapping node to each of its belonging communities, and the fuzzy coefficients of the overlapping node can be obtained by normalizing the to all its belonging communities. The running time of our method is analyzed and varies linearly with network size. We investigate our method on the computer-generated networks and real networks. The testing results indicate that the accuracy of our method in detecting disjoint communities is higher than those of the existing local methods and our method is efficient for detecting the overlapping nodes with fuzzy coefficients. Furthermore, the local optimizing scheme used in our method allows us to partly solve the resolution problem of the global modularity. Project supported by the National Natural Science Foundation of China (Grant Nos. 51278101 and 51578149), the Science and Technology Program of Ministry of Transport of China (Grant No. 2015318J33080), the Jiangsu Provincial Post-doctoral Science Foundation, China (Grant No. 1501046B), and the Fundamental Research Funds for the Central Universities, China (Grant No. Y0201500219).

  14. Community Detection in Sparse Random Networks

    Science.gov (United States)

    2013-08-13

    hypothesis testing, Erdös- Rényi random graph, scan statistic, planted clique problem, largest connected component. 1 Introduction Community detection...very special case where p1 = 1, meaning that the anomalous subgraph S is a clique . This is the case that Sun and Nobel (2008) consider motivated by a...data mining application. We confirmed the intuition that the clique test, which is based on the size of the largest clique , is asymptotically minimax

  15. Clustering and community detection in directed networks: A survey

    Science.gov (United States)

    Malliaros, Fragkiskos D.; Vazirgiannis, Michalis

    2013-12-01

    Networks (or graphs) appear as dominant structures in diverse domains, including sociology, biology, neuroscience and computer science. In most of the aforementioned cases graphs are directed - in the sense that there is directionality on the edges, making the semantics of the edges nonsymmetric as the source node transmits some property to the target one but not vice versa. An interesting feature that real networks present is the clustering or community structure property, under which the graph topology is organized into modules commonly called communities or clusters. The essence here is that nodes of the same community are highly similar while on the contrary, nodes across communities present low similarity. Revealing the underlying community structure of directed complex networks has become a crucial and interdisciplinary topic with a plethora of relevant application domains. Therefore, naturally there is a recent wealth of research production in the area of mining directed graphs - with clustering being the primary method sought and the primary tool for community detection and evaluation. The goal of this paper is to offer an in-depth comparative review of the methods presented so far for clustering directed networks along with the relevant necessary methodological background and also related applications. The survey commences by offering a concise review of the fundamental concepts and methodological base on which graph clustering algorithms capitalize on. Then we present the relevant work along two orthogonal classifications. The first one is mostly concerned with the methodological principles of the clustering algorithms, while the second one approaches the methods from the viewpoint regarding the properties of a good cluster in a directed network. Further, we present methods and metrics for evaluating graph clustering results, demonstrate interesting application domains and provide promising future research directions.

  16. The Evolution of Community Structure in a Coauthorship Network

    Directory of Open Access Journals (Sweden)

    William Mcdowell

    2011-12-01

    Full Text Available Mechanisms such as triadic closure and preferential attachment drive the evolution of social networks. Many models use these mechanisms to predict future links, and they generate realistic networks with scale-free degree distributions. These social networks also have community structure, or sets of vertices which are more connected to each other than the rest of the network. To study the evolution of research groups of scientists in a coauthorship network, we use a timeheterarchy representation to extend the mechanisms driving the evolution of the network to the level of this community structure. Specifically, we examine changes in the structure of groups in terms of mechanisms analogous to triadic closure and preferential attachment, and as a result, we find that the network evolves in the same way at the group-level and the individual-level. In addition, we find that interactions at the group-level might affect interactions at the individual-level in that members of a single group are more likely to strengthen their relationships than members of separate groups.

  17. 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.

  18. Maps of random walks on complex networks reveal community structure.

    Science.gov (United States)

    Rosvall, Martin; Bergstrom, Carl T

    2008-01-29

    To comprehend the multipartite organization of large-scale biological and social systems, we introduce an information theoretic approach that reveals community structure in weighted and directed networks. We use the probability flow of random walks on a network as a proxy for information flows in the real system and decompose the network into modules by compressing a description of the probability flow. The result is a map that both simplifies and highlights the regularities in the structure and their relationships. We illustrate the method by making a map of scientific communication as captured in the citation patterns of >6,000 journals. We discover a multicentric organization with fields that vary dramatically in size and degree of integration into the network of science. Along the backbone of the network-including physics, chemistry, molecular biology, and medicine-information flows bidirectionally, but the map reveals a directional pattern of citation from the applied fields to the basic sciences.

  19. Hiding Individuals and Communities in a Social Network

    CERN Document Server

    Waniek, Marcin; Rahwan, Talal; Wooldridge, Michael

    2016-01-01

    The Internet and social media have fueled enormous interest in social network analysis. New tools continue to be developed and used to analyse our personal connections, with particular emphasis on detecting communities or identifying key individuals in a social network. This raises privacy concerns that are likely to exacerbate in the future. With this in mind, we ask the question: Can individuals or groups actively manage their connections to evade social network analysis tools? By addressing this question, the general public may better protect their privacy, oppressed activist groups may better conceal their existence, and security agencies may better understand how terrorists escape detection. We first study how an individual can evade "network centrality" analysis without compromising his or her influence within the network. We prove that an optimal solution to this problem is hard to compute. Despite this hardness, we demonstrate that even a simple heuristic, whereby attention is restricted to the indivi...

  20. 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.

  1. A seed-expanding method based on random walks for community detection in networks with ambiguous community structures

    Science.gov (United States)

    Su, Yansen; Wang, Bangju; Zhang, Xingyi

    2017-02-01

    Community detection has received a great deal of attention, since it could help to reveal the useful information hidden in complex networks. Although most previous modularity-based and local modularity-based community detection algorithms could detect strong communities, they may fail to exactly detect several weak communities. In this work, we define a network with clear or ambiguous community structures based on the types of its communities. A seed-expanding method based on random walks is proposed to detect communities for networks, especially for the networks with ambiguous community structures. We identify local maximum degree nodes, and detect seed communities in a network. Then, the probability of a node belonging to each community is calculated based on the total probability model and random walks, and each community is expanded by repeatedly adding the node which is most likely to belong to it. Finally, we use the community optimization method to ensure that each node is in a community. Experimental results on both computer-generated and real-world networks demonstrate that the quality of the communities detected by the proposed algorithm is superior to the- state-of-the-art algorithms in the networks with ambiguous community structures.

  2. Efficient community-based control strategies in adaptive networks

    CERN Document Server

    Yang, Hui; Zhang, Hai-Feng

    2012-01-01

    Most researches on adaptive networks mainly concentrate on the properties of steady state, but neglect transient dynamics. In this study, we pay attention to the emergence of community structures in transient process and the effects of community-based control strategies on epidemic spreading. First, by normalizing modularity $Q$, we investigate the evolution of community structures during the transient process, and find that very strong community structures are induced by rewiring mechanism in the early stage of epidemic spreading, which remarkably delays the outbreaks of epidemic. Then we study the effects of control strategies started from different stages on the prevalence. Both immunization and quarantine strategies indicate that it is not "the earlier, the better" for the implementing of control measures. And the optimal control effect is obtained if control measures can be efficiently implemented in the period of strong community structure. For immunization strategy, immunizing the S nodes on SI links a...

  3. Community Detection in Multi-Dimensional Networks

    Science.gov (United States)

    2010-01-01

    left side of Eq. (26), which is equivalent to PCA applied to data of the following form: X = [ S(1), S(2), · · · , S(d) ] (27) Suppose the SVD of X...community assignment idx. 1. Compute top ` eigenvectors of the utility matrix as stated in Eq. (14); 2. Compute slim SVD of X = [S(1), S(2), · · ·S(d...because feature integration denoises the information presented in each dimension, thus is able to obtain a more robust clustering result. Net- work

  4. Jumpstarting the Information Design for a Community Network.

    Science.gov (United States)

    Vaughan, Misha W.; Schwartz, Nancy

    1999-01-01

    Describes the process and outcome of an effort to develop an information design for a community network's Web site. Topics include user-centered design techniques, diffusion of innovations literature, targeting innovators and early adopters, the use of focus groups, and usability tests. (Author/LRW)

  5. 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…

  6. 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.

  7. 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…

  8. Bootstrap Percolation on Complex Networks with Community Structure

    CERN Document Server

    Chong, Wu; Rui, Zhang; Liujun, Chen; Jiawei, Chen; Xiaobin, Li; Yanqing, Hu

    2014-01-01

    Real complex networks usually involve community structure. How innovation and new products spread on social networks which have internal structure is a practically interesting and fundamental question. In this paper we study the bootstrap percolation on a single network with community structure, in which we initiate the bootstrap process by activating different fraction of nodes in each community. A previously inactive node transfers to active one if it detects at least $k$ active neighbors. The fraction of active nodes in community $i$ in the final state $S_i$ and its giant component size $S_{gci}$ are theoretically obtained as functions of the initial fractions of active nodes $f_i$. We show that such functions undergo multiple discontinuous transitions; The discontinuous jump of $S_i$ or $S_{gci}$ in one community may trigger a simultaneous jump of that in the other, which leads to multiple discontinuous transitions for the total fraction of active nodes $S$ and its associated giant component size $S_{gc}$...

  9. Emerging communities in networks - a flow of ties

    CERN Document Server

    Gawronski, Przemyslaw; Kulakowski, Krzysztof

    2015-01-01

    Algorithms for search of communities in networks usually consist discrete variations of links. Here we discuss a flow method, driven by a set of differential equations. Two examples are demonstrated in detail. First is a partition of a signed graph into two parts, where the proposed equations are interpreted in terms of removal of a cognitive dissonance by agents placed in the network nodes. There, the signs and values of links refer to positive or negative interpersonal relationships of different strength. Second is an application of a method akin to the previous one, dedicated to communities identification, to the Sierpinski triangle of finite size. During the time evolution, the related graphs are weighted; yet at the end the discrete character of links is restored. In the case of the Sierpinski triangle, the method is supplemented by adding a small noise to the initial connectivity matrix. By breaking the symmetry of the network, this allows to a successful handling of overlapping nodes.

  10. RPWCN or Roaming Protocol in Wireless Community Network

    Directory of Open Access Journals (Sweden)

    Thomas Djotio

    2014-03-01

    Full Text Available Many Roaming protocols have already been proposed in wireless community networks (WCN, but none has yet been the subject of standardization. Also, the networks topologies required for deployments are all very costly when it is necessary to take into account the socio-economic conditions of developing countries. In this paper, we propose the RPWCN Protocol (Roaming Protocol in Wireless Community Network which design logic is strongly inspired by the functioning of the Roaming Protocol in GSM. The WCN architecture on which we modelled our Protocol takes into account the socio-economic conditions of developing countries. After the modelling of the RPWCN Protocol, the AVISPA tool has been solicited for its validation

  11. Adaptive impulsive cluster synchronization in community network with nonidentical nodes

    Science.gov (United States)

    Gong, Xiaoli; Gan, Luyining; Wu, Zhaoyan

    2016-07-01

    In this paper, cluster synchronization in community network with nonidentical nodes is investigated. Through introducing proper adaptive strategy into impulsive control scheme, adaptive impulsive controllers are designed for achieving the cluster synchronization. In this adaptive impulsive control scheme, for any given networks, the impulsive gains can adjust themselves to proper values according to the proposed adaptive strategy when the impulsive intervals are fixed. The impulsive instants can be estimated by solving a sequence of maximum value problems when the impulsive gains are fixed. Both community networks without and with coupling delay are considered. Based on the Lyapunov function method and mathematical analysis technique, two synchronization criteria are derived. Several numerical examples are performed to verify the effectiveness of the derived theoretical results.

  12. Analysis of Community Detection Algorithms for Large Scale Cyber Networks

    Energy Technology Data Exchange (ETDEWEB)

    Mane, Prachita; Shanbhag, Sunanda; Kamath, Tanmayee; Mackey, Patrick S.; Springer, John

    2016-09-30

    The aim of this project is to use existing community detection algorithms on an IP network dataset to create supernodes within the network. This study compares the performance of different algorithms on the network in terms of running time. The paper begins with an introduction to the concept of clustering and community detection followed by the research question that the team aimed to address. Further the paper describes the graph metrics that were considered in order to shortlist algorithms followed by a brief explanation of each algorithm with respect to the graph metric on which it is based. The next section in the paper describes the methodology used by the team in order to run the algorithms and determine which algorithm is most efficient with respect to running time. Finally, the last section of the paper includes the results obtained by the team and a conclusion based on those results as well as future work.

  13. Modeling Risk Perception in Networks with Community Structure

    CERN Document Server

    Bagnoli, Franco; Guazzini, Andrea; Massaro, Emanuele; Rudolph, Stefan

    2012-01-01

    We study the influence of global, local and community-level risk perception on the extinction probability of a disease in several models of social networks. In particular, we study the infection progression as a susceptible-infected-susceptible (SIS) model on several modular networks, formed by a certain number of random and scale-free communities. We find that in the scale-free networks the progression is faster than in random ones with the same average connectivity degree. For what concerns the role of perception, we find that the knowledge of the infection level in one's own neighborhood is the most effective property in stopping the spreading of a disease, but at the same time the more expensive one in terms of the quantity of required information, thus the cost/effectiveness optimum is a tradeoff between several parameters.

  14. Libraries in Oklahoma: MedlinePlus

    Science.gov (United States)

    ... this page: https://medlineplus.gov/libraries/oklahoma.html Libraries in Oklahoma To use the sharing features on ... please enable JavaScript. Ada MERCY HOSPITAL ADA MEDICAL LIBRARY ILL 430 NORTH MONTE VISTA ADA, OK 74820 ...

  15. Emergence of Bursts and Communities in Evolving Weighted Networks

    CERN Document Server

    Jo, Hang-Hyun; Kaski, Kimmo

    2011-01-01

    Understanding the patterns of human dynamics and social interaction, and the way they lead to the formation of an organized and functional society are important issues especially for techno-social development. Addressing these issues of social networks has recently become possible through large scale data analysis of e.g. mobile phone call records, which has revealed the existence of modular or community structure with many links between nodes of the same community and relatively few links between nodes of different communities. The weights of links, e.g. the number of calls between two users, and the network topology are found correlated such that intra-community links are stronger compared to the weak inter-community links. This is known as Granovetter's "The strength of weak ties" hypothesis. In addition to this inhomogeneous community structure, the temporal patterns of human dynamics turn out to be inhomogeneous or bursty, characterized by the heavy tailed distribution of inter-event time between two con...

  16. 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.

  17. Communities of minima in local optima networks of combinatorial spaces

    Science.gov (United States)

    Daolio, Fabio; Tomassini, Marco; Vérel, Sébastien; Ochoa, Gabriela

    2011-05-01

    In this work, we present a new methodology to study the structure of the configuration spaces of hard combinatorial problems. It consists in building the network that has as nodes the locally optimal configurations and as edges the weighted oriented transitions between their basins of attraction. We apply the approach to the detection of communities in the optima networks produced by two different classes of instances of a hard combinatorial optimization problem: the quadratic assignment problem (QAP). We provide evidence indicating that the two problem instance classes give rise to very different configuration spaces. For the so-called real-like class, the networks possess a clear modular structure, while the optima networks belonging to the class of random uniform instances are less well partitionable into clusters. This is convincingly supported by using several statistical tests. Finally, we briefly discuss the consequences of the findings for heuristically searching the corresponding problem spaces.

  18. Resolving Rupture Directivity of Moderate Strike-Slip Earthquakes in Sparse Network with Ambient Noise Location: A Case Study with the 2011 M5.6 Oklahoma Earthquake

    Science.gov (United States)

    He, X.; Ni, S.

    2015-12-01

    Earthquake rupture directivity is essential for improving reliability of shakemap and understanding seismogenic processes by resolving the ruptured fault. Compared with field geological survey and InSAR technique, rupture directivity analysis based on seismological data provides rapid characterization of the rupture finiteness parameters or is almost the only way for resolving ruptured fault for earthquakes weaker than M5. In recent years, ambient seismic noise has been widely used in tomography and as well as earthquake location. Barmin et al. (2011) and Levshin et al. (2012) proposed to locate the epicenter by interpolating the estimated Green's functions (EGFs) determined by cross-correlation of ambient noise to arbitrary hypothetical event locations. This method does not rely on an earth model, but it requires a dense local array. Zhan et al. (2011) and Zeng et al. (2014) used the EGFs between a nearby station and remote stations as calibration for 3D velocity structure and then obtained the centroid location. In contrast, the hypocenter can be determined by P wave onsets. When assuming unilateral rupture, we can resolve the rupture directivity with relative location of the centroid location and hypocenter. We apply this method to the 2011 M5.6 Oklahoma earthquake. One M4.8 foreshock and one M4+ aftershock are chosen as reference event to calibrate the systematic bias of ambient noise location. The resolved rupture plane strikes southwest-northeast, consistent with the spatial distribution of aftershocks (McNamara et al., 2015) and finite fault inversion result (Sun et al., 2014). This method works for unilaterally ruptured strike-slip earthquakes, and more case studies are needed to test its effectiveness.

  19. 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.

  20. Fern Habitats and Rare Ferns in Oklahoma

    Directory of Open Access Journals (Sweden)

    Bruce A. Smith

    2008-12-01

    Full Text Available This paper features some of the more common fern habitats in Oklahoma and provides information on four rare Oklahoma ferns from two fern families: Aspleniaceae and Pteridaceae. Surprisingly, ferns can be found in a variety of habitats across Oklahoma.

  1. Social Networks and Community-Based Natural Resource Management

    Science.gov (United States)

    Lauber, T. Bruce; Decker, Daniel J.; Knuth, Barbara A.

    2008-10-01

    We conducted case studies of three successful examples of collaborative, community-based natural resource conservation and development. Our purpose was to: (1) identify the functions served by interactions within the social networks of involved stakeholders; (2) describe key structural properties of these social networks; and (3) determine how these structural properties varied when the networks were serving different functions. The case studies relied on semi-structured, in-depth interviews of 8 to 11 key stakeholders at each site who had played a significant role in the collaborative projects. Interview questions focused on the roles played by key stakeholders and the functions of interactions between them. Interactions allowed the exchange of ideas, provided access to funding, and enabled some stakeholders to influence others. The exchange of ideas involved the largest number of stakeholders, the highest percentage of local stakeholders, and the highest density of interactions. Our findings demonstrated the value of tailoring strategies for involving stakeholders to meet different needs during a collaborative, community-based natural resource management project. Widespread involvement of local stakeholders may be most appropriate when ideas for a project are being developed. During efforts to exert influence to secure project approvals or funding, however, involving specific individuals with political connections or influence on possible sources of funds may be critical. Our findings are consistent with past work that has postulated that social networks may require specific characteristics to meet different needs in community-based environmental management.

  2. Vertex-centred Method to Detect Communities in Evolving Networks

    CERN Document Server

    Canu, Maël; d'Allonnes, Adrien Revault

    2016-01-01

    Finding communities in evolving networks is a difficult task and raises issues different from the classic static detection case. We introduce an approach based on the recent vertex-centred paradigm. The proposed algorithm, named DynLOCNeSs, detects communities by scanning and evaluating each vertex neighbourhood, which can be done independently in a parallel way. It is done by means of a preference measure, using these preferences to handle community changes. We also introduce a new vertex neighbourhood preference measure, CWCN, more efficient than current existing ones in the considered context. Experimental results show the relevance of this measure and the ability of the proposed approach to detect classical community evolution patterns such as grow-shrink and merge-split.

  3. INTERNET ENVIRONMENT BEING A FACTOR THAT SHAPES THE NETWORKING COMMUNITY

    Directory of Open Access Journals (Sweden)

    Lyudmila Aleksandrovna SAENKO

    2015-01-01

    Full Text Available The main indicator showing the standing of the contem-porary society is nowadays spreading of network forms of communication and liaisons among the individuals, and their advancement. The paper considered the main factor involving a reshaping of the contemporary society, i.e. the World Wide Web and impact they exerted on the shape of the networking community. Particular attention has been paid to information and technology aspects of advancement of the contemporary social medium, owing to which the vast majority of the population over the world being active users of the global network. Against this background, exploring new processes being under-way in society seemed to be urgent, as those turned to be available solely owing to social and communicative opportunities the Internet network provided. The paper pointed out the main features of the World Wide Web being a social and communicative medium, and suggest-ed a relatively new type of social interaction shaped as well, in particular, emergence of an upgraded networking community and their advancement.

  4. SIS model of epidemic spreading on dynamical networks with community

    Institute of Scientific and Technical Information of China (English)

    Chengyi XIA; Shiwen SUN; Feng RAO; Junqing SUN; Jinsong WANG; Zengqiang CHEN

    2009-01-01

    We present a new epidemic Susceptible-Infected-Susceptible (SIS) model to investigate the spreading behav-ior on networks with dynamical topology and community structure. Individuals in the model are mobile agents who are allowed to perform the inter-community (i.e., long-range) motion with the probability p. The mean-field theory is uti-lized to derive the critical threshold (λ_C) of epidemic spread-ing inside separate communities and the influence of the long-range motion on the epidemic spreading. The results indicate that λ_C is only related with the population density within the community, and the long-range motion will make the original disease-free community become the endemic state. Large-scale numerical simulations also demonstrate the theoretical approximations based on our new epidemic model. The current model and analysis will help us to fur-ther understand the propagation behavior of real epidemics taking place on social networks.

  5. Communities in Neuronal Complex Networks Revealed by Activation Patterns

    CERN Document Server

    Costa, Luciano da Fontoura

    2008-01-01

    Recently, it has been shown that the communities in neuronal networks of the integrate-and-fire type can be identified by considering patterns containing the beginning times for each cell to receive the first non-zero activation. The received activity was integrated in order to facilitate the spiking of each neuron and to constrain the activation inside the communities, but no time decay of such activation was considered. The present article shows that, by taking into account exponential decays of the stored activation, it is possible to identify the communities also in terms of the patterns of activation along the initial steps of the transient dynamics. The potential of this method is illustrated with respect to complex neuronal networks involving four communities, each of a different type (Erd\\H{o}s-R\\'eny, Barab\\'asi-Albert, Watts-Strogatz as well as a simple geographical model). Though the consideration of activation decay has been found to enhance the communities separation, too intense decays tend to y...

  6. SOCIAL RELATION NETWORKS IN UT-ONLINE COMMUNITY FORUM

    Directory of Open Access Journals (Sweden)

    Mohammad Imam FARISI

    2012-01-01

    Full Text Available 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, and geographic. Since the early decades of the 1990s, the existence of virtual community forum has attracted much attention and interest to the researchers, because it has significance as a social and cultural capital, and as a socio-technological solution for creating a learning community building forces. The UT-Online community forum is a virtual community forum that was built by (Universitas Terbuka UT in year 2006 to facilitate students to share and discuss various information, ideas, experiences in relation with academic or/and non-academic. This study examines and explains the contents of relation, social ties and structures of social relation networks in UT-Online Community Forum. The results of the study are important to the distance education institutions for building sense of community to DE students.

  7. 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.

  8. Exploring the locus of invention : The dynamics of network communities and firms’ invention productivity

    NARCIS (Netherlands)

    Sytch, M.; Tatarynowicz, A.

    2014-01-01

    Departing from prior research analyzing the implications of social structure for actors' outcomes by applying either an ego network or a global network perspective, this study examines the implications of network communities for the invention productivity of firms. Network communities represent

  9. Social Networks and Social Support: Implications for Natural Helper and Community Level Interventions.

    Science.gov (United States)

    Israel, Barbara A.

    1985-01-01

    Focuses on the linkage between social support and social networks and health educational programs that involve interventions at the network and community level. Addresses programs enhancing entire networks through natural helpers; and programs strengthening overlapping networks/communities through key opinion and informal leaders who are engaged…

  10. The community seismic network and quake-catcher network: enabling structural health monitoring through instrumentation by community participants

    Science.gov (United States)

    Kohler, Monica D.; Heaton, Thomas H.; Cheng, Ming-Hei

    2013-04-01

    A new type of seismic network is in development that takes advantage of community volunteers to install low-cost accelerometers in houses and buildings. The Community Seismic Network and Quake-Catcher Network are examples of this, in which observational-based structural monitoring is carried out using records from one to tens of stations in a single building. We have deployed about one hundred accelerometers in a number of buildings ranging between five and 23 stories in the Los Angeles region. In addition to a USB-connected device which connects to the host's computer, we have developed a stand-alone sensor-plug-computer device that directly connects to the internet via Ethernet or wifi. In the case of the Community Seismic Network, the sensors report both continuous data and anomalies in local acceleration to a cloud computing service consisting of data centers geographically distributed across the continent. Visualization models of the instrumented buildings' dynamic linear response have been constructed using Google SketchUp and an associated plug-in to matlab with recorded shaking data. When data are available from only one to a very limited number of accelerometers in high rises, the buildings are represented as simple shear beam or prismatic Timoshenko beam models with soil-structure interaction. Small-magnitude earthquake records are used to identify the first set of horizontal vibrational frequencies. These frequencies are then used to compute the response on every floor of the building, constrained by the observed data. These tools are resulting in networking standards that will enable data sharing among entire communities, facility managers, and emergency response groups.

  11. Community-aware task allocation for social networked multiagent systems.

    Science.gov (United States)

    Wang, Wanyuan; Jiang, Yichuan

    2014-09-01

    In this paper, we propose a novel community-aware task allocation model for social networked multiagent systems (SN-MASs), where the agent' cooperation domain is constrained in community and each agent can negotiate only with its intracommunity member agents. Under such community-aware scenarios, we prove that it remains NP-hard to maximize system overall profit. To solve this problem effectively, we present a heuristic algorithm that is composed of three phases: 1) task selection: select the desirable task to be allocated preferentially; 2) allocation to community: allocate the selected task to communities based on a significant task-first heuristics; and 3) allocation to agent: negotiate resources for the selected task based on a nonoverlap agent-first and breadth-first resource negotiation mechanism. Through the theoretical analyses and experiments, the advantages of our presented heuristic algorithm and community-aware task allocation model are validated. 1) Our presented heuristic algorithm performs very closely to the benchmark exponential brute-force optimal algorithm and the network flow-based greedy algorithm in terms of system overall profit in small-scale applications. Moreover, in the large-scale applications, the presented heuristic algorithm achieves approximately the same overall system profit, but significantly reduces the computational load compared with the greedy algorithm. 2) Our presented community-aware task allocation model reduces the system communication cost compared with the previous global-aware task allocation model and improves the system overall profit greatly compared with the previous local neighbor-aware task allocation model.

  12. Networks and emotion-driven user communities at popular blogs

    Science.gov (United States)

    Mitrović, M.; Paltoglou, G.; Tadić, B.

    2010-10-01

    Online communications at web portals represents technology-mediated user interactions, leading to massive data and potentially new techno-social phenomena not seen in real social mixing. Apart from being dynamically driven, the user interactions via posts is indirect, suggesting the importance of the contents of the posted material. We present a systematic way to study Blog data by combined approaches of physics of complex networks and computer science methods of text analysis. We are mapping the Blog data onto a bipartite network where users and posts with comments are two natural partitions. With the machine learning methods we classify the texts of posts and comments for their emotional contents as positive or negative, or otherwise objective (neutral). Using the spectral methods of weighted bipartite graphs, we identify topological communities featuring the users clustered around certain popular posts, and underly the role of emotional contents in the emergence and evolution of these communities.

  13. An MDL approach to efficiently discover communities in bipartite network

    Institute of Scientific and Technical Information of China (English)

    徐开阔; 曾春秋; 元昌安; 李川; 唐常杰

    2014-01-01

    An minimum description length (MDL) criterion is proposed to choose a good partition for a bipartite network. A heuristic algorithm based on combination theory is presented to approach the optimal partition. As the heuristic algorithm automatically searches for the number of partitions, no user intervention is required. Finally, experiments are conducted on various datasets, and the results show that our method generates higher quality results than the state-of-art methods, cross-association and bipartite, recursively induced modules. Experiment results also show the good scalability of the proposed algorithm. The method is applied to traditional Chinese medicine (TCM) formula and Chinese herbal network whose community structure is not well known, and found that it detects significant and it is informative community division.

  14. 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.

  15. 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.

  16. The Implementation of Telemedicine within a Community Cancer Network

    OpenAIRE

    London, Jack W; Morton, Daniel E.; Marinucci, Donna; Catalano, Robert; Comis, Robert L.

    1997-01-01

    Telemedicine is being used by physicians at the member hospitals of the Jefferson Cancer Network (JCN) for consultations regarding the diagnosis and management of cancer patients. The technology employed for this telemedicine system was chosen to meet three related specifications: low capital and operating cost, internal maintainability by community hospital data processing staffs, and compatibility with the existing technologic infrastructure. The solution selected is the u...

  17. 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.

  18. Model-based clustering in networks with Stochastic Community Finding

    CERN Document Server

    McDaid, Aaron F; Friel, Nial; Hurley, Neil J

    2012-01-01

    In the model-based clustering of networks, blockmodelling may be used to identify roles in the network. We identify a special case of the Stochastic Block Model (SBM) where we constrain the cluster-cluster interactions such that the density inside the clusters of nodes is expected to be greater than the density between clusters. This corresponds to the intuition behind community-finding methods, where nodes tend to clustered together if they link to each other. We call this model Stochastic Community Finding (SCF) and present an efficient MCMC algorithm which can cluster the nodes, given the network. The algorithm is evaluated on synthetic data and is applied to a social network of interactions at a karate club and at a monastery, demonstrating how the SCF finds the 'ground truth' clustering where sometimes the SBM does not. The SCF is only one possible form of constraint or specialization that may be applied to the SBM. In a more supervised context, it may be appropriate to use other specializations to guide...

  19. Characterization and exploitation of community structure in cover song networks

    CERN Document Server

    Serrà, Joan; Herrera, Perfecto; Serra, Xavier

    2011-01-01

    The use of community detection algorithms is explored within the framework of cover song identification, i.e. the automatic detection of different audio renditions of the same underlying musical piece. Until now, this task has been posed as a typical query-by-example task, where one submits a query song and the system retrieves a list of possible matches ranked by their similarity to the query. In this work, we propose a new approach which uses song communities to provide more relevant answers to a given query. Starting from the output of a state-of-the-art system, songs are embedded in a complex weighted network whose links represent similarity (related musical content). Communities inside the network are then recognized as groups of covers and this information is used to enhance the results of the system. In particular, we show that this approach increases both the coherence and the accuracy of the system. Furthermore, we provide insight into the internal organization of individual cover song communities, s...

  20. Transition at Age 3: Steps for Success Transition Guide for Oklahoma Children with Disabilities, Their Families, SoonerStart Early Intervention Services, Schools, and Community Programs.

    Science.gov (United States)

    Sharp, Mark, Ed.; Villines-Hackney, Amber, Ed.; Rush, Dathan, Ed.; Shelden, M'Lisa, Ed.; Hansen, Laura, Ed.

    This guide was developed to support families, the SoonerStart Early Intervention Program, schools, and community programs in meeting the federal and state requirements under the Individuals with Disabilities Education Act (IDEA) Amendments of 1997 for transition of children at age three. It provides guiding principles, recommended practices, and…

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

    Science.gov (United States)

    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. PMID:28278242

  2. The Quake-Catcher Network: An Innovative Community-Based Seismic Network

    Science.gov (United States)

    Saltzman, J.; Cochran, E. S.; Lawrence, J. F.; Christensen, C. M.

    2009-12-01

    The Quake-Catcher Network (QCN) is a volunteer computing seismic network that engages citizen scientists, teachers, and museums to participate in the detection of earthquakes. In less than two years, the network has grown to over 1000 participants globally and continues to expand. QCN utilizes Micro-Electro-Mechanical System (MEMS) accelerometers, in laptops and external to desktop computers, to detect moderate to large earthquakes. One goal of the network is to involve K-12 classrooms and museums by providing sensors and software to introduce participants to seismology and community-based scientific data collection. The Quake-Catcher Network provides a unique opportunity to engage participants directly in the scientific process, through hands-on activities that link activities and outcomes to their daily lives. Partnerships with teachers and museum staff are critical to growth of the Quake Catcher Network. Each participating institution receives a MEMS accelerometer to connect, via USB, to a computer that can be used for hands-on activities and to record earthquakes through a distributed computing system. We developed interactive software (QCNLive) that allows participants to view sensor readings in real time. Participants can also record earthquakes and download earthquake data that was collected by their sensor or other QCN sensors. The Quake-Catcher Network combines research and outreach to improve seismic networks and increase awareness and participation in science-based research in K-12 schools.

  3. Sewage flow optimization algorithm for large-scale urban sewer networks based on network community division

    Institute of Scientific and Technical Information of China (English)

    Lihui CEN; Yugeng XI

    2008-01-01

    By considering the flow control of urban sewer networks to minimize the electricity consumption of pumping stations.a decomposition-coordination strategy for energy savings based on network community division is developed in this paper. A mathematical model characterizing the smady-state flow of urball sewer networks is first constructed,consisting of a set of algebraic equations with the structure transportation capacities captured as constraints.Since the sewer networks have no apparent natural hierarchical structure in general.it is very difficult to identify the clustered groups.A fast network division approach through calculating the betweenness of each edge is successfully applied to identify the groups and a sewer network with arbitrary configuration could be then decomposed into subnetworks.By integrating the coupling constraints of the subnetworks.the original problem is separated into N optimization subproblems in accordance with the network decomposition.Each subproblem is solved locally and the solutions to the subproblems are coordinated to form an appropriate global solution.Finally,an application to a specified large-scale sewer network is also investigated to demonstrate the validity of the proposed algorithm.

  4. Correlation network analysis applied to complex biofilm communities.

    Directory of Open Access Journals (Sweden)

    Ana E Duran-Pinedo

    Full Text Available The complexity of the human microbiome makes it difficult to reveal organizational principles of the community and even more challenging to generate testable hypotheses. It has been suggested that in the gut microbiome species such as Bacteroides thetaiotaomicron are keystone in maintaining the stability and functional adaptability of the microbial community. In this study, we investigate the interspecies associations in a complex microbial biofilm applying systems biology principles. Using correlation network analysis we identified bacterial modules that represent important microbial associations within the oral community. We used dental plaque as a model community because of its high diversity and the well known species-species interactions that are common in the oral biofilm. We analyzed samples from healthy individuals as well as from patients with periodontitis, a polymicrobial disease. Using results obtained by checkerboard hybridization on cultivable bacteria we identified modules that correlated well with microbial complexes previously described. Furthermore, we extended our analysis using the Human Oral Microbe Identification Microarray (HOMIM, which includes a large number of bacterial species, among them uncultivated organisms present in the mouth. Two distinct microbial communities appeared in healthy individuals while there was one major type in disease. Bacterial modules in all communities did not overlap, indicating that bacteria were able to effectively re-associate with new partners depending on the environmental conditions. We then identified hubs that could act as keystone species in the bacterial modules. Based on those results we then cultured a not-yet-cultivated microorganism, Tannerella sp. OT286 (clone BU063. After two rounds of enrichment by a selected helper (Prevotella oris OT311 we obtained colonies of Tannerella sp. OT286 growing on blood agar plates. This system-level approach would open the possibility of

  5. Network-driven reputation in online scientific communities.

    Science.gov (United States)

    Liao, Hao; Xiao, Rui; Cimini, Giulio; Medo, Matúš

    2014-01-01

    The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers) which makes the method readily applicable to the existing online scientific communities.

  6. Network-driven reputation in online scientific communities.

    Directory of Open Access Journals (Sweden)

    Hao Liao

    Full Text Available The ever-increasing quantity and complexity of scientific production have made it difficult for researchers to keep track of advances in their own fields. This, together with growing popularity of online scientific communities, calls for the development of effective information filtering tools. We propose here an algorithm which simultaneously computes reputation of users and fitness of papers in a bipartite network representing an online scientific community. Evaluation on artificially-generated data and real data from the Econophysics Forum is used to determine the method's best-performing variants. We show that when the input data is extended to a multilayer network including users, papers and authors and the algorithm is correspondingly modified, the resulting performance improves on multiple levels. In particular, top papers have higher citation count and top authors have higher h-index than top papers and top authors chosen by other algorithms. We finally show that our algorithm is robust against persistent authors (spammers which makes the method readily applicable to the existing online scientific communities.

  7. Digital data sets that describe aquifer characteristics of the Central Oklahoma aquifer in central Oklahoma

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This data set consists of digitized polygons of a constant hydraulic conductivity value for the Central Oklahoma aquifer in central Oklahoma. This area encompasses...

  8. Digital data sets that describe aquifer characteristics of the Central Oklahoma aquifer in central Oklahoma

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This data set consists of digitized water-level elevation contours for the Central Oklahoma aquifer in central Oklahoma. This area encompasses all or part of...

  9. Digital data sets that describe aquifer characteristics of the Central Oklahoma aquifer in central Oklahoma

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This data set consists of digitized aquifer boundaries created for a previously published report about the Central Oklahoma aquifer in central Oklahoma. This area...

  10. Digital data sets that describe aquifer characteristics of the Central Oklahoma aquifer in central Oklahoma

    Data.gov (United States)

    U.S. Geological Survey, Department of the Interior — This data set consists of digitized polygons of a constant recharge value for the Central Oklahoma aquifer in central Oklahoma. This area encompasses all or part of...

  11. Ubiquitousness of link-density and link-pattern communities in real-world networks

    CERN Document Server

    Šubelj, Lovro

    2011-01-01

    Community structure appears to be an intrinsic property of many complex real-world networks. However, recent work shows that real-world networks reveal even more sophisticated modules than classical cohesive (link-density) communities. In particular, networks can also be naturally partitioned according to similar patterns of connectedness between the nodes, revealing link-pattern communities. We here propose a balanced propagation based algorithm that can extract both link-density and link-pattern communities, without any prior knowledge of the true structure. The algorithm was first validated on different classes of synthetic benchmark networks with community structure, and also on random networks. We have then further applied the algorithm to different social, information, technological and biological networks, where it indeed reveals meaningful (composites of) link-density and link-pattern communities. The results thus seem to imply that, similarly as link-density counterparts, link-pattern communities app...

  12. 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

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

    NARCIS (Netherlands)

    Quetulio-Navarra, Melissa; Znidarsic, Anja; Niehof, A.

    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

  14. 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.

  15. Stochastic fluctuations and the detectability limit of network communities

    CERN Document Server

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

    2013-01-01

    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 conclusions that the native, putative partition of the network is completely lost even before the in-degree/out-degree ratio becomes equal to the one of a structure-less Erd\\"os-R\\'enyi 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 (semi-quantitatively) 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...

  16. Effects of Weak Ties on Epidemic Predictability in Community Networks

    CERN Document Server

    Shu, Panpan; Gong, Kai; Liu, Ying

    2012-01-01

    Weak ties play a significant role in the structures and the dynamics of community networks. Based on the susceptible-infected model in contact process, we study numerically how weak ties influence the predictability of epidemic dynamics. We first investigate the effects of different kinds of weak ties on the variabilities of both the arrival time and the prevalence of disease, and find that the bridgeness with small degree can enhance the predictability of epidemic spreading. Once weak ties are settled, compared with the variability of arrival time, the variability of prevalence displays a diametrically opposed changing trend with both the distance of the initial seed to the bridgeness and the degree of the initial seed. More specifically, the further distance and the larger degree of the initial seed can induce the better predictability of arrival time and the worse predictability of prevalence. Moreover, we discuss the effects of weak tie number on the epidemic variability. As community strength becomes ver...

  17. 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. PMID:28334003

  18. Periodic Wave of Epidemic Spreading in Community Networks

    Institute of Scientific and Technical Information of China (English)

    ZHOU Yin-Zuo; LIU Zong-Hua; ZHOU Jie

    2007-01-01

    It was reported by Cummings et al. [Nature 427 (2004)344] that there are periodic waves in the spatiotemporal data of epidemics. For understanding the mechanism, we study the epidemic spreading on community networks by both the SIS model and the SIRS model. We find that with the increase of infection rate, the number of total infected nodes may be stabilized at a fixed point, oscillatory waves, and periodic cycles. Moreover, the epidemic spreading in the SIS model can be explained by an analytic map.

  19. Unfolding communities in large complex networks: Combining defensive and offensive label propagation for core extraction

    CERN Document Server

    Šubelj, Lovro; 10.1103/PhysRevE.83.036103

    2011-01-01

    Label propagation has proven to be a fast method for detecting communities in large complex networks. Recent developments have also improved the accuracy of the approach, however, a general algorithm is still an open issue. We present an advanced label propagation algorithm that combines two unique strategies of community formation, namely, defensive preservation and offensive expansion of communities. Two strategies are combined in a hierarchical manner, to recursively extract the core of the network, and to identify whisker communities. The algorithm was evaluated on two classes of benchmark networks with planted partition and on almost 25 real-world networks ranging from networks with tens of nodes to networks with several tens of millions of edges. It is shown to be comparable to the current state-of-the-art community detection algorithms and superior to all previous label propagation algorithms, with comparable time complexity. In particular, analysis on real-world networks has proven that the algorithm ...

  20. 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.

  1. Community detection in complex networks using density-based clustering algorithm and manifold learning

    Science.gov (United States)

    You, Tao; Cheng, Hui-Min; Ning, Yi-Zi; Shia, Ben-Chang; Zhang, Zhong-Yuan

    2016-12-01

    Like clustering analysis, community detection aims at assigning nodes in a network into different communities. Fdp is a recently proposed density-based clustering algorithm which does not need the number of clusters as prior input and the result is insensitive to its parameter. However, Fdp cannot be directly applied to community detection due to its inability to recognize the community centers in the network. To solve the problem, a new community detection method (named IsoFdp) is proposed in this paper. First, we use IsoMap technique to map the network data into a low dimensional manifold which can reveal diverse pair-wised similarity. Then Fdp is applied to detect the communities in the network. An improved partition density function is proposed to select the proper number of communities automatically. We test our method on both synthetic and real-world networks, and the results demonstrate the effectiveness of our algorithm over the state-of-the-art methods.

  2. 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.

  3. 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.

  4. Discovering link communities in complex networks by an integer programming model and a genetic algorithm.

    Science.gov (United States)

    Li, Zhenping; Zhang, Xiang-Sun; Wang, Rui-Sheng; Liu, Hongwei; Zhang, Shihua

    2013-01-01

    Identification of communities in complex networks is an important topic and issue in many fields such as sociology, biology, and computer science. Communities are often defined as groups of related nodes or links that correspond to functional subunits in the corresponding complex systems. While most conventional approaches have focused on discovering communities of nodes, some recent studies start partitioning links to find overlapping communities straightforwardly. In this paper, we propose a new quantity function for link community identification in complex networks. Based on this quantity function we formulate the link community partition problem into an integer programming model which allows us to partition a complex network into overlapping communities. We further propose a genetic algorithm for link community detection which can partition a network into overlapping communities without knowing the number of communities. We test our model and algorithm on both artificial networks and real-world networks. The results demonstrate that the model and algorithm are efficient in detecting overlapping community structure in complex networks.

  5. Detecting community structure in complex networks using an interaction optimization process

    Science.gov (United States)

    Kim, Paul; Kim, Sangwook

    2017-01-01

    Most complex networks contain community structures. Detecting these community structures is important for understanding and controlling the networks. Most community detection methods use network topology and edge density to identify optimal communities; however, these methods have a high computational complexity and are sensitive to network forms and types. To address these problems, in this paper, we propose an algorithm that uses an interaction optimization process to detect community structures in complex networks. This algorithm efficiently searches the candidates of optimal communities by optimizing the interactions of the members within each community based on the concept of greedy optimization. During this process, each candidate is evaluated using an interaction-based community model. This model quickly and accurately measures the difference between the quantity and quality of intra- and inter-community interactions. We test our algorithm on several benchmark networks with known community structures that include diverse communities detected by other methods. Additionally, after applying our algorithm to several real-world complex networks, we compare our algorithm with other methods. We find that the structure quality and coverage results achieved by our algorithm surpass those of the other methods.

  6. The Rise of China in the International Trade Network: A Community Core Detection Approach

    CERN Document Server

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

    2014-01-01

    Theory of complex networks proved successful in the description of a variety of static networks ranging from biology to computer and social sciences 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. Most importantly, we have a multilevel description of the evolution where the global dynamics (i.e., communities disappear or reemerge) tend to be correlated with the regional dynamics (i.e., community core changes between community members). In...

  7. Communities and beyond: mesoscopic analysis of a large social network with complementary methods

    CERN Document Server

    Tibely, Gergely; Kovanen, Lauri; Kaski, Kimmo; Kertesz, Janos; Saramaki, Jari

    2010-01-01

    Large complex networks show different levels of organization. At the mesoscopic scale communities are considered the most important structures that relate to system function but also other formations like trees or stars may appear. Communities are characterized as groups of nodes with dense internal and loose inter-group connectivity, but beyond this simple notion, even the definition of a community is a controversial issue. Numerous community detection methods have been proposed and assessed either on small empirical networks or larger synthetic benchmarks. However, little is known about their performance on large real-world networks and about the meaningfulness of the community structure they produce. Here we apply three community detection methods, Infomap, the Louvain method, and clique percolation to a large real-world social network based on mobile telephone calls and compare their results. Benchmarks are fabricated to capture only selected aspects of reality, while large empirical networks are much mor...

  8. A community integration strategy based on an improved modularity density increment for large-scale networks

    Science.gov (United States)

    Shang, Ronghua; Zhang, Weitong; Jiao, Licheng; Stolkin, Rustam; Xue, Yu

    2017-03-01

    This paper presents a community integration strategy for large-scale networks, based on pre-partitioning, followed by optimization of an improved modularity density increment Δ D. Our proposed method initially searches for local core nodes in the network, i.e. potential community centers, and expands these communities to include neighbor nodes which have sufficiently high similarity with the core node. In this way, we can effectively exploit the information of the node and structure of the network, to accurately pre-partition the network into communities. Next, we arrange these pre-partitioned communities according to their external connections in descending order. In this way, we can ensure that communities with greater influence are prioritized during the process of community integration. At the same time, this paper proposes an improved modularity density increment Δ D, and shows how to use this as an objective function during the community integration optimization process. During the process of community consolidation, those neighbor communities with few external connections are prioritized for merging, thereby avoiding the fusion errors. Finally, we incorporate global reasoning into the process of local integration. We calculate and compare the improved modularity density increment of each pair of communities, to determine whether or not they should be integrated, effectively improve the accuracy of community integration. Experimental results show that our proposed algorithm can obtain superior community classification results on 5 large-scale networks, as compared with 8 other well known algorithms from the literature.

  9. The community network: an Aboriginal community football club bringing people together.

    Science.gov (United States)

    Thorpe, Alister; Anders, Wendy; Rowley, Kevin

    2014-01-01

    There are few empirical studies about the role of Aboriginal sporting organisations in promoting wellbeing. The aim of the present study was to understand the impact of an Aboriginal community sporting team and its environment on the social, emotional and physical wellbeing of young Aboriginal men, and to identify barriers and motivators for participation. A literature review of the impact of sport on the health and wellbeing of Aboriginal participants was conducted. This informed a qualitative study design with a grounded theory approach. Four semistructured interviews and three focus groups were completed with nine current players and five past players of the Fitzroy Stars Football Club to collect data about the social, emotional and physical wellbeing impact of an Aboriginal football team on its Aboriginal players. Results of the interviews were consistent with the literature, with common concepts emerging around community connection, cultural values and identity, health, values, racism and discrimination. However, the interviews provided further detail around the significance of cultural values and community connection for Aboriginal people. The complex nature of social connections and the strength of Aboriginal community networks in sports settings were also evident. Social reasons were just as important as individual health reasons for participation. Social and community connection is an important mechanism for maintaining and strengthening cultural values and identity. Barriers and motivators for participation in Aboriginal sports teams can be complex and interrelated. Aboriginal sports teams have the potential to have a profound impact on the health of Aboriginal people, especially its players, by fostering a safe and culturally strengthening environment and encompassing a significant positive social hub for the Aboriginal community.

  10. The Implementation of Telemedicine within a Community Cancer Network

    Science.gov (United States)

    London, Jack W.; Morton, Daniel E.; Marinucci, Donna; Catalano, Robert; Comis, Robert L.

    1997-01-01

    Telemedicine is being used by physicians at the member hospitals of the Jefferson Cancer Network (JCN) for consultations regarding the diagnosis and management of cancer patients. The technology employed for this telemedicine system was chosen to meet three related specifications: low capital and operating cost, internal maintainability by community hospital data processing staffs, and compatibility with the existing technologic infrastructure. The solution selected is the ubiquitous desktop personal computer and associated software, and Integrated Services Digital Network (ISDN) communications links. The overall performance of this technology has been very satisfactory; ISDN communications has sufficient bandwidth for the transfer of patient data, including text reports, radiographs, and pathology slide images. The presence of the radiologist's interpretation along with the radiographic images allows the presentation of the images on these systems to be acceptable for review purposes. The video frame rates of these systems (12 to 15 frames per second) is adequate, particularly given the “talking heads” nature of the video presentations. Furthermore, the quality of the video image (resolution, size, frame rate) is secondary to the quality of the presentation of the medical information displayed and the capability for mutual annotation of the patient data during the consultation. PMID:8988470

  11. Sampling microbial communities in the National Ecological Observatory Network

    Science.gov (United States)

    Adams, H. E.; Parnell, J.; Powell, H.

    2012-12-01

    The National Ecological Observatory Network (NEON) is a national-scale research platform to enable the community to assess impacts of climate change, land-use change, and invasive species on ecosystem structure and function at regional and continental scales. The NEON Observatory will collect data on aquatic organisms over 30 years in 36 sites across the United States, including Alaska and Puerto Rico as well as terrestrial organisms at 60 sites including Hawaii. Included in the biological measurements are microbial measurements in terrestrial and aquatic environments, including small, wadeable streams and shallow lakes. Microbial sampling in both aquatic and terrestrial habitats is being planned to coincide with biogeochemical sampling due to similarity of time scale and influence of external drivers. Aquatic sampling is geared toward species diversity and function. Terrestrial sampling aims to collect data on diversity, function, and spatial distribution dynamics. We are in the process of prioritizing data products, so that the most dynamic processes such as enzymatic activity will be measured more frequently and more intensive measures such as metagenome sequence data will be measured on a periodic basis. Here we present our initial microbial sampling strategy and invite the community to provide comment on the design and learn about microbial data products from the Observatory.

  12. 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.

  13. Advanced Bibliometric Methods To Model the Relationship between Entry Behavior and Networking in Emerging Technological Communities.

    Science.gov (United States)

    Debackere, Koenraad; Clarysse, Bart

    1998-01-01

    Technological communities used bibliometric data on 411 plant biotechnology organizations to study the effect of field density and structure of the research and development network. Findings indicate the usefulness of bibliometric data in mapping change in technological communities and in the effects of networking on entry behavior. (PEN)

  14. The Changing Role of Community Networks in Providing Citizen Access to the Internet.

    Science.gov (United States)

    Keenan, Thomas P.; Trotter, David Mitchell

    1999-01-01

    Examines the changing role of community network associations or freenets in providing Internet access by examining the case of the Calgary Community Network Association (CCNA) in Alberta, Canada. Discusses the withdrawal of states from the telecommunications field, priorities of the Canadian government, and the role of the private sector.…

  15. DyCoNet: a Gephi plugin for community detection in dynamic complex networks.

    Directory of Open Access Journals (Sweden)

    Julie Kauffman

    Full Text Available Community structure detection has proven to be important in revealing the underlying organisation of complex networks. While most current analyses focus on static networks, the detection of communities in dynamic data is both challenging and timely. An analysis and visualisation procedure for dynamic networks is presented here, which identifies communities and sub-communities that persist across multiple network snapshots. An existing method for community detection in dynamic networks is adapted, extended, and implemented. We demonstrate the applicability of this method to detect communities in networks where individuals tend not to change their community affiliation very frequently. When stability of communities cannot be assumed, we show that the sub-community model may be a better alternative. This is illustrated through test cases of social and biological networks. A plugin for Gephi, an open-source software program used for graph visualisation and manipulation, named "DyCoNet", was created to execute the algorithm and is freely available from https://github.com/juliemkauffman/DyCoNet.

  16. 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.

  17. A Method for Community Detection in Protein Networks Using Spectral Optimization

    Directory of Open Access Journals (Sweden)

    Sminu Izudheen

    2011-12-01

    Full Text Available Identification of community structures in complex networks has been a challenge in many domain and discipline. In protein networks these community interactions play a vital role in identifying the outcome of many cellular mechanisms. This paper reports the use of spectral optimization of triangular modularity as an effective method to identify these community structures. The algorithm has been carefully tested on real biological data and the results acknowledge that this is a powerful method for extracting community structures from protein networks.

  18. Practice-based Research Networks (PBRNs) Bridging the Gaps between Communities, Funders, and Policymakers

    Science.gov (United States)

    Gaglioti, Anne H.; Werner, James J.; Rust, George; Fagnan, Lyle J.; Neale, Anne Victoria

    2016-01-01

    In this commentary, we propose that practice-based research networks (PBRNs) engage with funders and policymakers by applying the same engagement strategies they have successfully used to build relationships with community stakeholders. A community engagement approach to achieve new funding streams for PBRNs should include a strategy to engage key stakeholders from the communities of funders, thought leaders, and policymakers using collaborative principles and methods. PBRNs that implement this strategy would build a robust network of engaged partners at the community level, across networks, and would reach state and federal policymakers, academic family medicine departments, funding bodies, and national thought leaders in the redesign of health care delivery. PMID:27613796

  19. Incremental Density-Based Link Clustering Algorithm for Community Detection in Dynamic Networks

    Directory of Open Access Journals (Sweden)

    Fanrong Meng

    2016-01-01

    Full Text Available Community detection in complex networks has become a research hotspot in recent years. However, most of the existing community detection algorithms are designed for the static networks; namely, the connections between the nodes are invariable. In this paper, we propose an incremental density-based link clustering algorithm for community detection in dynamic networks, iDBLINK. This algorithm is an extended version of DBLINK which is proposed in our previous work. It can update the local link community structure in the current moment through the change of similarity between the edges at the adjacent moments, which includes the creation, growth, merging, deletion, contraction, and division of link communities. Extensive experimental results demonstrate that iDBLINK not only has a great time efficiency, but also maintains a high quality community detection performance when the network topology is changing.

  20. Triangles as basis to detect communities: an appication to Twitter's network

    CERN Document Server

    Abdelsadek, Youcef; Herrmann, Francine; Kacem, Imed; Otjacques, Benoît

    2016-01-01

    Nowadays, the interest given by the scientific community to the investigation of the data generated by social networks is increasing as much as the exponential increasing of social network data. The data structure complexity is one among the snags, which slowdown their understanding. On the other hand, community detection in social networks helps the analyzers to reveal the structure and the underlying semantic within communities. In this paper we propose an interactive visualization approach relying on our application NLCOMS, which uses synchronous and related views for graph and community visualization. Additionally, we present our algorithm for community detection in networks. A computation study is conducted on instances generated with the LFR [9]-[10] benchmark. Finally, in order to assess our approach on real-world data, we consider the data of the ANR-Info-RSN project. The latter addresses community detection in Twitter.

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

    Directory of Open Access Journals (Sweden)

    Rodica Ioana Lung

    Full Text Available 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.

  2. 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.

  3. 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.

  4. Development Network Pottery to Support Strength of Community

    Directory of Open Access Journals (Sweden)

    T. Jangprajuck

    2011-01-01

    Full Text Available Problem statement: The pottery was an importance manufacture of Thai rural. The objectives of this research were to study: (1 the historical background of pottery in Central Region, (2 the network of pottery in Central Region and (3 the development of network of pottery fro developing the community business strength in Central Region. Approach: The research area included Patuntani, Nondaburi, Suanburi, Nakonpatom and Rachaburi Provinces. The samples, 90 persons, were Proposive Sampling. Qualitative Research Methodology was administered. Data were collected by Interviewing, Observation and Focus Group Discussion. For data analysis, it was classified by objective. The findings were presented in descriptive analysis. Results: Patumtani Province Pottery had a long historical evidence at Samkoke District with pattern conveying that it was Peguans’ art both of the telling and comparing to Ongsamkoke Art. Later on, there were people leading in inventing by using the other material wanted by the market. Nondaburi Province Pottery also had long historical evidence at Ko-kred, upper Peguans. So, their descendants inherited the occupation in pottery until now. For Supanburi Province, its pottery had Benjarong Pattern by obtaining knowledge from Chinese. The word “Benjarong”, was a Thai calling, referring to drawing design on pottery covering with 5 colors. In general, there would be black, white, yellow, red and green, or indigo blue. Nakonpatom Province Pottery called Siladon, was another kind of pottery with green color an striped surface. It was collected and inherited Thai Wisdom for a long period of time by experts’ tactics ancestors from generation to generation since Sukothai Period until Ayudaya Period until now. For Rachaburi Province Pottery, occurred by a Chinese Pioneer to Thailand and persuading his friends to collaborate in investing for setting up the Taosenglee Factory disseminating throughout the province. In the

  5. A multi-community homogeneous small-world network and its fundamental characteristics

    Science.gov (United States)

    Tanimoto, Jun

    2016-10-01

    We introduce a new small-world network-which we call the multi-community homogeneous-small-world network-that is divided into multiple communities that are relatively isolated, similar to sparsely connected islands. A generating algorithm is presented and its network parameters are explored. To elucidate the fundamental characteristics of the proposed topology, we adopt spatial prisoner's dilemma games as a template for discussion. Comparing with a conventional homogeneous small-world network, more enhanced network reciprocity is observed in games where a stag hunt-type dilemma is large. With intensive analysis, we find how this enhancement is brought about.

  6. Mobilizing Ideas in Knowledge Networks: A Social Network Analysis of the Human Resource Management Community 1990-2005

    Science.gov (United States)

    Henneberg, Stephan C.; Swart, Juani; Naude, Peter; Jiang, Zhizhong; Mouzas, Stefanos

    2009-01-01

    Purpose: The purpose of this paper is to show the role of social networks in mobilizing how actors both impact and are impacted on by their colleagues. It seeks to compare the human resource management (HRM) academic community with two other comparable communities, and to identify those groups that are seen to work closely together.…

  7. Mobilizing Ideas in Knowledge Networks: A Social Network Analysis of the Human Resource Management Community 1990-2005

    Science.gov (United States)

    Henneberg, Stephan C.; Swart, Juani; Naude, Peter; Jiang, Zhizhong; Mouzas, Stefanos

    2009-01-01

    Purpose: The purpose of this paper is to show the role of social networks in mobilizing how actors both impact and are impacted on by their colleagues. It seeks to compare the human resource management (HRM) academic community with two other comparable communities, and to identify those groups that are seen to work closely together.…

  8. Surveying traffic congestion based on the concept of community structure of complex networks

    Science.gov (United States)

    Ma, Lili; Zhang, Zhanli; Li, Meng

    2016-07-01

    In this paper, taking the traffic of Beijing city as an instance, we study city traffic states, especially traffic congestion, based on the concept of network community structure. Concretely, using the floating car data (FCD) information of vehicles gained from the intelligent transport system (ITS) of the city, we construct a new traffic network model which is with floating cars as network nodes and time-varying. It shows that this traffic network has Gaussian degree distributions at different time points. Furthermore, compared with free traffic situations, our simulations show that the traffic network generally has more obvious community structures with larger values of network fitness for congested traffic situations, and through the GPSspg web page, we show that all of our results are consistent with the reality. Then, it indicates that network community structure should be an available way for investigating city traffic congestion problems.

  9. Properties of Teacher Networks in Twitter: Are They Related to Community-Based Peer Production?

    Science.gov (United States)

    Macià, Maria; Garcia, Iolanda

    2017-01-01

    Teachers participate in social networking sites to share knowledge and collaborate with other teachers to create education-related content. In this study we selected several communities in order to better understand the networks that these participants establish in Twitter and the role that the social network plays in their activity within the…

  10. The Community Science Workshop Network Story: Case Studies of the CSW Sites

    Science.gov (United States)

    St. John, Mark

    2014-01-01

    The Community Science Workshops (CSWs)--with funding from the S.D. Bechtel, Jr. Foundation, and the Gordon and Betty Moore Foundation--created a network among the CSW sites in California. The goals of the CSW Network project have been to improve programs, build capacity throughout the Network, and establish new sites. Inverness Research has been…

  11. 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…

  12. Community Violence, Social Support Networks, Ethnic Group Differences, and Male Perpetration of Intimate Partner Violence

    Science.gov (United States)

    Raghavan, Chitra; Rajah, Valli; Gentile, Katie; Collado, Lillian; Kavanagh, Ann Marie

    2009-01-01

    The authors examined how witnessing community violence influenced social support networks and how these networks were associated with male-to-female intimate partner violence (IPV) in ethnically diverse male college students. The authors assessed whether male social support members themselves had perpetrated IPV (male network violence) and whether…

  13. Null Models and Modularity Based Community Detection in Multi-Layer Networks

    CERN Document Server

    Paul, Subhadeep

    2016-01-01

    Multi-layer networks are networks on a set of entities (nodes) with multiple types of relations (edges) among them where each type of relation/interaction is represented as a network layer. As with single layer networks, community detection is an important task in multi-layer networks. A large group of popular community detection methods in networks are based on optimizing a quality function known as the modularity score, which is a measure of presence of modules or communities in networks. Hence a first step in community detection is defining a suitable modularity score that is appropriate for the network in question. Here we introduce several multi-layer network modularity measures under different null models of the network, motivated by empirical observations in networks from a diverse field of applications. In particular we define the multi-layer configuration model, the multi-layer expected degree model and their various modifications as null models for multi-layer networks to derive different modulariti...

  14. Weighted modularity optimization for crisp and fuzzy community detection in large-scale networks

    Science.gov (United States)

    Cao, Jie; Bu, Zhan; Gao, Guangliang; Tao, Haicheng

    2016-11-01

    Community detection is a classic and very difficult task in the field of complex network analysis, principally for its applications in domains such as social or biological networks analysis. One of the most widely used technologies for community detection in networks is the maximization of the quality function known as modularity. However, existing work has proved that modularity maximization algorithms for community detection may fail to resolve communities in small size. Here we present a new community detection method, which is able to find crisp and fuzzy communities in undirected and unweighted networks by maximizing weighted modularity. The algorithm derives new edge weights using the cosine similarity in order to go around the resolution limit problem. Then a new local moving heuristic based on weighted modularity optimization is proposed to cluster the updated network. Finally, the set of potentially attractive clusters for each node is computed, to further uncover the crisply fuzzy partition of the network. We give demonstrative applications of the algorithm to a set of synthetic benchmark networks and six real-world networks and find that it outperforms the current state of the art proposals (even those aimed at finding overlapping communities) in terms of quality and scalability.

  15. Triadic closure as a basic generating mechanism of communities in complex networks

    Science.gov (United States)

    Bianconi, Ginestra; Darst, Richard K.; Iacovacci, Jacopo; Fortunato, Santo

    2014-10-01

    Most of the complex social, technological, and biological networks have a significant community structure. Therefore the community structure of complex networks has to be considered as a universal property, together with the much explored small-world and scale-free properties of these networks. Despite the large interest in characterizing the community structures of real networks, not enough attention has been devoted to the detection of universal mechanisms able to spontaneously generate networks with communities. Triadic closure is a natural mechanism to make new connections, especially in social networks. Here we show that models of network growth based on simple triadic closure naturally lead to the emergence of community structure, together with fat-tailed distributions of node degree and high clustering coefficients. Communities emerge from the initial stochastic heterogeneity in the concentration of links, followed by a cycle of growth and fragmentation. Communities are the more pronounced, the sparser the graph, and disappear for high values of link density and randomness in the attachment procedure. By introducing a fitness-based link attractivity for the nodes, we find a phase transition where communities disappear for high heterogeneity of the fitness distribution, but a different mesoscopic organization of the nodes emerges, with groups of nodes being shared between just a few superhubs, which attract most of the links of the system.

  16. 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.

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

    CERN Document Server

    Lou, Yang; Fan, Zhengping; Xiang, Luna

    2016-01-01

    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 reach global consensus on naming an object asymptotically through iterative pair-wise conversations. The underlying communication network indicates the relationships among the individuals. In this paper, three typical topologies of human communication networks, 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) when the intra-community connections increase while the inter-community connections remain to be unchanged, the convergence to global consensus is slow ...

  18. Force-Based Incremental Algorithm for Mining Community Structure in Dynamic Network

    Institute of Scientific and Technical Information of China (English)

    Bo Yang; Da-You Liu

    2006-01-01

    Community structure is an important property of network. Being able to identify communities can provide invaluable help in exploiting and understanding both social and non-social networks. Several algorithms have been developed up till now. However, all these algorithms can work well only with small or moderate networks with vertexes of order 104.Besides, all the existing algorithms are off-line and cannot work well with highly dynamic networks such as web, in which web pages are updated frequently. When an already clustered network is updated, the entire network including original and incremental parts has to be recalculated, even though only slight changes are involved. To address this problem, an incremental algorithm is proposed, which allows for mining community structure in large-scale and dynamic networks. Based on the community structure detected previously, the algorithm takes little time to reclassify the entire network including both the original and incremental parts. Furthermore, the algorithm is faster than most of the existing algorithms such as Girvan and Newman's algorithm and its improved versions. Also, the algorithm can help to visualize these community structures in network and provide a new approach to research on the evolving process of dynamic networks.

  19. 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…

  20. The Community Seismic Network and Quake-Catcher Network: Monitoring building response to earthquakes through community instrumentation

    Science.gov (United States)

    Cheng, M.; Kohler, M. D.; Heaton, T. H.; Clayton, R. W.; Chandy, M.; Cochran, E.; Lawrence, J. F.

    2013-12-01

    The Community Seismic Network (CSN) and Quake-Catcher Network (QCN) are dense networks of low-cost ($50) accelerometers that are deployed by community volunteers in their homes in California. In addition, many accelerometers are installed in public spaces associated with civic services, publicly-operated utilities, university campuses, and high-rise buildings. Both CSN and QCN consist of observation-based structural monitoring which is carried out using records from one to tens of stations in a single building. We have deployed about 150 accelerometers in a number of buildings ranging between five and 23 stories in the Los Angeles region. In addition to a USB-connected device which connects to the host's computer, we have developed a stand-alone sensor-plug-computer device that directly connects to the internet via Ethernet or WiFi. In the case of CSN, the sensors report data to the Google App Engine cloud computing service consisting of data centers geographically distributed across the continent. This robust infrastructure provides parallelism and redundancy during times of disaster that could affect hardware. The QCN sensors, however, are connected to netbooks with continuous data streaming in real-time via the distributed computing Berkeley Open Infrastructure for Network Computing software program to a server at Stanford University. In both networks, continuous and triggered data streams use a STA/LTA scheme to determine the occurrence of significant ground accelerations. Waveform data, as well as derived parameters such as peak ground acceleration, are then sent to the associated archives. Visualization models of the instrumented buildings' dynamic linear response have been constructed using Google SketchUp and MATLAB. When data are available from a limited number of accelerometers installed in high rises, the buildings are represented as simple shear beam or prismatic Timoshenko beam models with soil-structure interaction. Small-magnitude earthquake records

  1. An improved algorithm for finding community structure in networks with an application to IPv6 backbone network

    Institute of Scientific and Technical Information of China (English)

    GUO Yingxin; XU Ke

    2007-01-01

    The discovery of community structure in a large number of complex networks has attracted lots of interest in recent years.One category of algorithms for detecting community structure,the divisive algorithms,has been proposed and improved impressively.In this paper,we propose an improved divisive algorithm,the basic idea of which is to take more than one parameters into consideration to describe the networks from different points of view.Although its basic idea appears to be a little simple,it is shown experimentally that it outperforms some other algorithms when it is applied to the networks with a relatively obscure community structure.We also demonstrate its effectiveness by applying it to IPv6 backbone network.The communities detected by our algorithm indicate that although underdeveloped compared with IPv4 network,IPv6 network has already exhibited a preliminary community structure.Moreover,our algorithm can be further extended and adapted in the future.In fact,it suggests a simple yet possibly efficient way to improve algorithms.

  2. On Varying Topology of Complex Networks and Performance Limitations of Community Detection Algorithms

    CERN Document Server

    Pasta, Muhammad Qasim; Melançon, Guy

    2016-01-01

    One of the most widely studied problem in mining and analysis of complex networks is the detection of community structures. The problem has been extensively studied by researchers due to its high utility and numerous applications in various domains. Many algorithmic solutions have been proposed for the community detection problem but the quest to find the best algorithm is still on. More often than not, researchers focus on developing fast and accurate algorithms that can be generically applied to networks from a variety of domains without taking into consideration the structural and topological variations in these networks. In this paper, we evaluate the performance of different clustering algorithms as a function of varying network topology. Along with the well known LFR model to generate benchmark networks with communities,we also propose a new model named Naive Scale Free Model to study the behavior of community detection algorithms with respect to different topological features. More specifically, we are...

  3. On the relationship between the structural and socioacademic communities of an interdisciplinary coauthorship network

    CERN Document Server

    Rodriguez, Marko A

    2008-01-01

    This article presents a study that compares detected structural communities in a coauthorship network to the socioacademic characteristics of the scholars that compose the network. The coauthorship network was created from the bibliographic record of an overt interdisciplinary research group focused on sensor networks and wireless communication. The popular leading eigenvector community detection algorithm was employed to assign a structural community to each scholar in the network. Socioacademic characteristics were gathered from the scholars and include such information as their academic department, academic affiliation, country of origin, and academic position. A Pearson's $\\chi^2$ test, with a simulated Monte Carlo, revealed that structural communities best represent groupings of individuals working in the same academic department and at the same institution. A generalization of this result indicates that, contrary to the common conception of a multi-institutional interdisciplinary research group, collabo...

  4. Studying the Complex Communities of Ants and Their Symbionts Using Ecological Network Analysis.

    Science.gov (United States)

    Ivens, Aniek B F; von Beeren, Christoph; Blüthgen, Nico; Kronauer, Daniel J C

    2016-01-01

    Ant colonies provide well-protected and resource-rich environments for a plethora of symbionts. Historically, most studies of ants and their symbionts have had a narrow taxonomic scope, often focusing on a single ant or symbiont species. Here we discuss the prospects of studying these assemblies in a community ecology context using the framework of ecological network analysis. We introduce three basic network metrics that we consider particularly relevant for improving our knowledge of ant-symbiont communities: interaction specificity, network modularity, and phylogenetic signal. We then discuss army ant symbionts as examples of large and primarily parasitic communities, and symbiotic sternorrhynchans as examples of generally smaller and primarily mutualistic communities in the context of these network analyses. We argue that this approach will provide new and complementary insights into the evolutionary and ecological dynamics between ants and their many associates, and will facilitate comparisons across different ant-symbiont assemblages as well as across different types of ecological networks.

  5. 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.

  6. Using a two-phase evolutionary framework to select multiple network spreaders based on community structure

    Science.gov (United States)

    Fu, Yu-Hsiang; Huang, Chung-Yuan; Sun, Chuen-Tsai

    2016-11-01

    Using network community structures to identify multiple influential spreaders is an appropriate method for analyzing the dissemination of information, ideas and infectious diseases. For example, data on spreaders selected from groups of customers who make similar purchases may be used to advertise products and to optimize limited resource allocation. Other examples include community detection approaches aimed at identifying structures and groups in social or complex networks. However, determining the number of communities in a network remains a challenge. In this paper we describe our proposal for a two-phase evolutionary framework (TPEF) for determining community numbers and maximizing community modularity. Lancichinetti-Fortunato-Radicchi benchmark networks were used to test our proposed method and to analyze execution time, community structure quality, convergence, and the network spreading effect. Results indicate that our proposed TPEF generates satisfactory levels of community quality and convergence. They also suggest a need for an index, mechanism or sampling technique to determine whether a community detection approach should be used for selecting multiple network spreaders.

  7. Quartz Mountain/Oklahoma Summer Arts Institute.

    Science.gov (United States)

    Frates, Mary Y.; Madeja, Stanley S.

    1982-01-01

    Describes the Quartz Mountain Oklahoma Summer Arts Institute program. It is designed to nurture artistic talent and to provide intensive arts experiences in music, dance, theater, and the visual arts for talented students aged 14-18. (AM)

  8. Võrumaa Kutsehariduskeskuses esietendus kuulus "Oklahoma" / Tiit Raud

    Index Scriptorium Estoniae

    Raud, Tiit

    2006-01-01

    12. aprillil esietendus "Oklahoma", mis valmis Rogersi ja Hammersteini samanimelise muusikali põhjal. Esitasid kutsehariduskeskuse noortekoori lauljad-näitlejad. Tantsujuht Maire Udras, kunstnik Lilian-Hanna Taimla, muusikaline juht Tiit Raud

  9. Sensitivity and Reliability in Incomplete Networks: Centrality Metrics to Community Scoring Functions

    CERN Document Server

    Sarkar, Soumya; Kumar, Suhansanu; Mukherjee, Animesh

    2016-01-01

    Network analysis is an important tool in understanding the behavior of complex systems of interacting entities. However, due to the limitations of data gathering technologies, some interactions might be missing from the network model. This is a ubiquitous problem in all domains that use network analysis, from social networks to hyper-linked web networks to biological networks. Consequently, an important question in analyzing networks is to understand how increasing the noise level (i.e. percentage of missing edges) affects different network parameters. In this paper we evaluate the effect of noise on community scoring and centrality-based parameters with respect to two different aspects of network analysis: (i) sensitivity, that is how the parameter value changes as edges are removed and (ii) reliability in the context of message spreading, that is how the time taken to broadcast a message changes as edges are removed. Our experiments on synthetic and real-world networks and three different noise models demon...

  10. Self-similar community structure in a network of human interactions.

    Science.gov (United States)

    Guimerà, R; Danon, L; Díaz-Guilera, A; Giralt, F; Arenas, A

    2003-12-01

    We propose a procedure for analyzing and characterizing complex networks. We apply this to the social network as constructed from email communications within a medium sized university with about 1700 employees. Email networks provide an accurate and nonintrusive description of the flow of information within human organizations. Our results reveal the self-organization of the network into a state where the distribution of community sizes is self-similar. This suggests that a universal mechanism, responsible for emergence of scaling in other self-organized complex systems, as, for instance, river networks, could also be the underlying driving force in the formation and evolution of social networks.

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

    Energy Technology Data Exchange (ETDEWEB)

    Yin, Guisheng; Chi, Kuo, E-mail: chik89769@hrbeu.edu.cn; Dong, Yuxin; Dong, Hongbin

    2017-04-25

    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.

  12. Network community structure alterations in adult schizophrenia: identification and localization of alterations.

    Science.gov (United States)

    Lerman-Sinkoff, Dov B; Barch, Deanna M

    2016-01-01

    A growing body of literature suggests functional connectivity alterations in schizophrenia. While findings have been mixed, evidence points towards a complex pattern of hyper-connectivity and hypo-connectivity. This altered connectivity can be represented and analyzed using the mathematical frameworks provided by graph and information theory to represent functional connectivity data as graphs comprised of nodes and edges linking the nodes. One analytic technique in this framework is the determination and analysis of network community structure, which is the grouping of nodes into linked communities or modules. This data-driven technique finds a best-fit structure such that nodes in a given community have greater connectivity with nodes in their community than with nodes in other communities. These community structure representations have been found to recapitulate known neural-systems in healthy individuals, have been used to identify novel functional systems, and have identified and localized community structure alterations in a childhood onset schizophrenia cohort. In the present study, we sought to determine whether community structure alterations were present in an adult onset schizophrenia cohort while stringently controlling for sources of imaging artifacts. Group level average graphs in healthy controls and individuals with schizophrenia exhibited visually similar network community structures and high amounts of normalized mutual information (NMI). However, testing of individual subject community structures identified small but significant alterations in community structure with alterations being driven by changes in node community membership in the somatosensory, auditory, default mode, salience, and subcortical networks.

  13. 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.

  14. A multi-agent genetic algorithm for community detection in complex networks

    Science.gov (United States)

    Li, Zhangtao; Liu, Jing

    2016-05-01

    Complex networks are popularly used to represent a lot of practical systems in the domains of biology and sociology, and the structure of community is one of the most important network attributes which has received an enormous amount of attention. Community detection is the process of discovering the community structure hidden in complex networks, and modularity Q is one of the best known quality functions measuring the quality of communities of networks. In this paper, a multi-agent genetic algorithm, named as MAGA-Net, is proposed to optimize modularity value for the community detection. An agent, coded by a division of a network, represents a candidate solution. All agents live in a lattice-like environment, with each agent fixed on a lattice point. A series of operators are designed, namely split and merging based neighborhood competition operator, hybrid neighborhood crossover, adaptive mutation and self-learning operator, to increase modularity value. In the experiments, the performance of MAGA-Net is validated on both well-known real-world benchmark networks and large-scale synthetic LFR networks with 5000 nodes. The systematic comparisons with GA-Net and Meme-Net show that MAGA-Net outperforms these two algorithms, and can detect communities with high speed, accuracy and stability.

  15. The Naming Game in Social Networks: Community Formation and Consensus Engineering

    CERN Document Server

    Lu, Qiming; Szymanski, B K; 10.1007/s11403-009-0057-7

    2010-01-01

    We study the dynamics of the Naming Game [Baronchelli et al., (2006) J. Stat. Mech.: Theory Exp. P06014] in empirical social networks. This stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

  16. Community Detecting and Feature Analysis in Real Directed Weighted Social Networks

    Directory of Open Access Journals (Sweden)

    Yao Liu

    2013-06-01

    Full Text Available Real social networks usually have some structural features of the complex networks, such as community structure, the scale-free degree distribution, clustering, "small world" network, dynamic evolution and so on. A new community detecting algorithm for directed and weighted social networks is proposed in this paper. Due to the use of more reference information, the accuracy of the algorithm is better than some of the typical detecting algorithms. And because of the use of heap structure and multi-task modular architecture, the algorithm also got a high computational efficiency than other algorithms. The effectiveness and efficiency of the algorithm is validated by experiments on real social networks. Based on the theories and models of complex networks, the features of the real large social networks are analyzed.

  17. A thermal flux-diffusing model for complex networks and its applications in community structure detection

    Science.gov (United States)

    Shen, Yi

    2013-05-01

    We introduce a thermal flux-diffusing model for complex networks. Based on this model, we propose a physical method to detect the communities in the complex networks. The method allows us to obtain the temperature distribution of nodes in time that scales linearly with the network size. Then, the local community enclosing a given node can be easily detected for the reason that the dense connections in the local communities lead to the temperatures of nodes in the same community being close to each other. The community structure of a network can be recursively detected by randomly choosing the nodes outside the detected local communities. In the experiments, we apply our method to a set of benchmarking networks with known pre-determined community structures. The experiment results show that our method has higher accuracy and precision than most existing globe methods and is better than the other existing local methods in the selection of the initial node. Finally, several real-world networks are investigated.

  18. 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.

  19. 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

  20. Community Detection in Signed Networks: the Role of Negative ties in Different Scales

    Science.gov (United States)

    Esmailian, Pouya; Jalili, Mahdi

    2015-09-01

    Extracting community structure of complex network systems has many applications from engineering to biology and social sciences. There exist many algorithms to discover community structure of networks. However, it has been significantly under-explored for networks with positive and negative links as compared to unsigned ones. Trying to fill this gap, we measured the quality of partitions by introducing a Map Equation for signed networks. It is based on the assumption that negative relations weaken positive flow from a node towards a community, and thus, external (internal) negative ties increase the probability of staying inside (escaping from) a community. We further extended the Constant Potts Model, providing a map spectrum for signed networks. Accordingly, a partition is selected through balancing between abridgment and expatiation of a signed network. Most importantly, multi-scale spectrum of signed networks revealed how informative are negative ties in different scales, and quantified the topological placement of negative ties between dense positive ones. Moreover, an inconsistency was found in the signed Modularity: as the number of negative ties increases, the density of positive ties is neglected more. These results shed lights on the community structure of signed networks.

  1. Environmental Assessment for Proposed General Purpose Warehouse Construction at Defense Distribution Officer Oklahoma City, Oklahoma (DDOO)

    Science.gov (United States)

    2008-05-01

    complex (SUND) - Teller fine sandy learn (T~B) - Teller -Urban land corT’jJiex (nLO) - Tnbbey fino sandy loam (TriA) - Urban land (URB) - Vanoss srlt...Mr. John Harrington 21 E Main Suite 100 Oklahoma City OK 73104-2405 405-234-2264 Audubon Society of Central Oklahoma President Ms. Jane Cunningham 5505

  2. Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm

    Institute of Scientific and Technical Information of China (English)

    Mao-Guo Gong; Ling-Jun Zhang; Jing-Jing Ma; Li-Cheng Jiao

    2012-01-01

    Community structure is one of the most important properties in social networks,and community detection has received an enormous amount of attention in recent years.In dynamic networks,the communities may evolve over time so that pose more challenging tasks than in static ones.Community detection in dynamic networks is a problem which can naturally be formulated with two contradictory objectives and consequently be solved by multiobjective optimization algorithms.In this paper,a novel multiobjective immune algorithm is proposed to solve the community detection problem in dynamic networks.It employs the framework of nondominated neighbor immune algorithm to simultaneously optimize the modularity and normalized mutual information,which quantitatively measure the quality of the community partitions and temporal cost,respectively.The problem-specific knowledge is incorporated in genetic operators and local search to improve the effectiveness and efficiency of our method.Experimental studies based on four synthetic datasets and two real-world social networks demonstrate that our algorithm can not only find community structure and capture community evolution more accurately but also be more steadily than the state-of-the-art algorithms.

  3. Using Social Networks to Create Powerful Learning Communities

    Science.gov (United States)

    Lenox, Marianne; Coleman, Maurice

    2010-01-01

    Regular readers of "Computers in Libraries" are aware that social networks are forming increasingly important linkages to professional and personal development in all libraries. Live and virtual social networks have become the new learning playground for librarians and library staff. Social networks have the ability to connect those who are…

  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. Developing an inter-organizational community-based health network: an Australian investigation.

    Science.gov (United States)

    Short, Alison; Phillips, Rebecca; Nugus, Peter; Dugdale, Paul; Greenfield, David

    2015-12-01

    Networks in health care typically involve services delivered by a defined set of organizations. However, networked associations between the healthcare system and consumers or consumer organizations tend to be open, fragmented and are fraught with difficulties. Understanding the role and activities of consumers and consumer groups in a formally initiated inter-organizational health network, and the impacts of the network, is a timely endeavour. This study addresses this aim in three ways. First, the Unbounded Network Inter-organizational Collaborative Impact Model, a purpose-designed framework developed from existing literature, is used to investigate the process and products of inter-organizational network development. Second, the impact of a network artefact is explored. Third, the lessons learned in inter-organizational network development are considered. Data collection methods were: 16 h of ethnographic observation; 10 h of document analysis; six interviews with key informants and a survey (n = 60). Findings suggested that in developing the network, members used common aims, inter-professional collaboration, the power and trust engendered by their participation, and their leadership and management structures in a positive manner. These elements and activities underpinned the inter-organizational network to collaboratively produce the Health Expo network artefact. This event brought together healthcare providers, community groups and consumers to share information. The Health Expo demonstrated and reinforced inter-organizational working and community outreach, providing consumers with community-based information and linkages. Support and resources need to be offered for developing community inter-organizational networks, thereby building consumer capacity for self-management in the community. © The Author (2014). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

  6. A Novel Top-k Strategy for Influence Maximization in Complex Networks with Community Structure.

    Directory of Open Access Journals (Sweden)

    Jia-Lin He

    Full Text Available In complex networks, it is of great theoretical and practical significance to identify a set of critical spreaders which help to control the spreading process. Some classic methods are proposed to identify multiple spreaders. However, they sometimes have limitations for the networks with community structure because many chosen spreaders may be clustered in a community. In this paper, we suggest a novel method to identify multiple spreaders from communities in a balanced way. The network is first divided into a great many super nodes and then k spreaders are selected from these super nodes. Experimental results on real and synthetic networks with community structure show that our method outperforms the classic methods for degree centrality, k-core and ClusterRank in most cases.

  7. Finding Community Structure in Networks Using a Shortest-Path-Based k-Means Algorithm

    Institute of Scientific and Technical Information of China (English)

    Jinglu GAO

    2013-01-01

    We consider the problem of detecting the community structure in a complex network,groups of nodes with a higher-than-average density of edges connecting them.In this paper we use the simulated annealing strategy to maximize the modularity,which has been indicated as a robust benefit function,associating with a shortest-path-based k-means iterative procedure for network partition.The proposed algorithm can not only find the communities,but also identify the nodes which occupy central positions under the metric of the shortest path within the communities to which they belong.The optimal number of communities can be automatically determined without any prior knowledge about the network structure.The applications to both artificial and real-world networks demonstrate the effectiveness of our algorithm.

  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, April). Socio-semantic Networks of Research Publications in the Learning Analytics Community. Presentation at the Learning Analystic and Knowelege (LAK13), Leuven, Belgium.

  9. From social network to safety net: Dementia-friendly communities in rural northern Ontario.

    Science.gov (United States)

    Wiersma, Elaine C; Denton, Alison

    2016-01-01

    Dementia-friendly communities, as communities that enable people with dementia to remain involved and active and have control over their lives for as long as possible, centrally involve social support and social networks for people living with dementia. The purpose of this research was to explore and understand the context of dementia in rural northern communities in Ontario with an emphasis on understanding how dementia friendly the communities were. Using qualitative methods, interviews were conducted with a total of 71 participants, including 37 health service providers, 15 care partners, 2 people living with dementia and 17 other community members such as local business owners, volunteers, local leaders, friends and neighbours. The strong social networks and informal social support that were available to people living with dementia, and the strong commitment by community members, families and health care providers to support people with dementia, were considered a significant asset to the community. A culture of care and looking out for each other contributed to the social support provided. In particular, the familiarity with others provided a supportive community environment. People with dementia were looked out for by community members, and continued to remain connected in their communities. The social support provided in these communities demonstrated that although fragile, this type of support offered somewhat of a safety net for individuals living with dementia. This work provides important insights into the landscape of dementia in rural northern Ontario communities, and the strong social supports that sustain people with dementia remaining in the communities.

  10. 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.

  11. Ant colony clustering with fitness perception and pheromone diffusion for community detection in complex networks

    Science.gov (United States)

    Ji, Junzhong; Song, Xiangjing; Liu, Chunnian; Zhang, Xiuzhen

    2013-08-01

    Community structure detection in complex networks has been intensively investigated in recent years. In this paper, we propose an adaptive approach based on ant colony clustering to discover communities in a complex network. The focus of the method is the clustering process of an ant colony in a virtual grid, where each ant represents a node in the complex network. During the ant colony search, the method uses a new fitness function to percept local environment and employs a pheromone diffusion model as a global information feedback mechanism to realize information exchange among ants. A significant advantage of our method is that the locations in the grid environment and the connections of the complex network structure are simultaneously taken into account in ants moving. Experimental results on computer-generated and real-world networks show the capability of our method to successfully detect community structures.

  12. Dynamic community detection based on network structural perturbation and topological similarity

    Science.gov (United States)

    Wang, Peizhuo; Gao, Lin; Ma, Xiaoke

    2017-01-01

    Community detection in dynamic networks has been extensively studied since it sheds light on the structure-function relation of the overall complex systems. Recently, it has been demonstrated that the structural perturbation in static networks is excellent in characterizing the topology. In order to investigate the perturbation structural theory in dynamic networks, we extend the theory by considering the dynamic variation information between networks of consecutive time. Then a novel similarity is proposed by combing structural perturbation and topological features. Finally, we present an evolutionary clustering algorithm to detect dynamic communities under the temporal smoothness framework. Experimental results on both artificial and real dynamic networks demonstrate that the proposed similarity is promising in dynamic community detection since it improves the clustering accuracy compared with state-of-the-art methods, indicating the superiority of the presented similarity measure.

  13. Environmental Assessment for Upgrades to Target and Road Facilities in the Oklahoma Range, Fort Greely, Alaska

    Science.gov (United States)

    2004-11-01

    the Royal Australian Air Force, the Royal Canadian Air Force, the Royal Singapore Air Force, Japanese Defense Forces, and other national forces...blackpoll warbler . Sensitive species include Osprey and Trumpeter Swan (Alaska Army Lands Withdrawal Renewal. Final EIS 1998). 21 OKLAHOMA RANGE...communities. Mixtures of balsam poplar, quaking aspen and white spruce dominate this community. A closed needle leaf forest consisting of stands of white

  14. 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.

  15. Economic networks and social communities in online-auction sites

    CERN Document Server

    Reichardt, J; Reichardt, Joerg; Bornholdt, Stefan

    2005-01-01

    Markets of individual traders exchanging goods can be seen as social and economic networks. Given the abundance of these networks and their economic importance, it is surprising how little detail is known about the structure and the evolution of such networks. We here study the transactions of almost 1.9 million users of an online auction site during the Pre-Christmas season of 2004. We analyze the topology of the resulting network and find fat tailed distributions for a number of fundamental network parameters. The network shows a high modularity and we are able to group traders according to their primary buying interest during the time observed, solely based on the network structure. The results help our understanding of the self-organization of markets and society.

  16. Clustering drug-drug interaction networks with energy model layouts: community analysis and drug repurposing

    OpenAIRE

    Lucreţia Udrescu; Laura Sbârcea; Alexandru Topîrceanu; Alexandru Iovanovici; Ludovic Kurunczi; Paul Bogdan; Mihai Udrescu

    2016-01-01

    Analyzing drug-drug interactions may unravel previously unknown drug action patterns, leading to the development of new drug discovery tools. We present a new approach to analyzing drug-drug interaction networks, based on clustering and topological community detection techniques that are specific to complex network science. Our methodology uncovers functional drug categories along with the intricate relationships between them. Using modularity-based and energy-model layout community detection...

  17. Periodic synchronization of community networks with non-identical nodes uncertain parameters and adaptive coupling strength

    Science.gov (United States)

    Chai, Yuan; Chen, Li-Qun

    2014-03-01

    In this paper, we propose a novel approach for simultaneously identifying unknown parameters and synchronizing time-delayed complex community networks with nonidentical nodes. Based on the LaSalle's invariance principle, a criterion is established by constructing an effective control identification scheme and adjusting automatically the adaptive coupling strength. The proposed control law is applied to a complex community network which is periodically synchronized with different chaotic states. Numerical simulations are conducted to demonstrate the feasibility of the proposed method.

  18. 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.

  19. Genotypic variation in foundation species generates network structure that may drive community dynamics and evolution.

    Science.gov (United States)

    Lau, Matthew K; Keith, Arthur R; Borrett, Stuart R; Shuster, Stephen M; Whitham, Thomas G

    2016-03-01

    Although genetics in a single species is known to impact whole communities, little is known about how genetic variation influences species interaction networks in complex ecosystems. Here, we examine the interactions in a community of arthropod species on replicated genotypes (clones) of a foundation tree species, Populus angustifolia James (narrowleaf cottonwood), in a long-term, common garden experiment using a bipartite "genotype-species" network perspective. We combine this empirical work with a simulation experiment designed to further investigate how variation among individual tree genotypes can impact network structure. Three findings emerged: (1) the empirical "genotype-species network" exhibited significant network structure with modularity being greater than the highly conservative null model; (2) as would be expected given a modular network structure, the empirical network displayed significant positive arthropod co-occurrence patterns; and (3) furthermore, the simulations of "genotype-species" networks displayed variation in network structure, with modularity in particular clearly increasing, as genotypic variation increased. These results support the conclusion that genetic variation in a single species contributes to the structure of ecological interaction networks, which could influence eco-ogical dynamics (e.g., assembly and stability) and evolution in a community context.

  20. 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. PMID:25136895

  1. 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.

  2. 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.

  3. Variability of community interaction networks in marine reserves and adjacent exploited areas

    Science.gov (United States)

    Montano-Moctezuma, G.; Li, H.W.; Rossignol, P.A.

    2008-01-01

    Regional and small-scale local oceanographic conditions can lead to high variability in community structure even among similar habitats. Communities with identical species composition can depict distinct networks due to different levels of disturbance as well as physical and biological processes. In this study we reconstruct community networks in four different areas off the Oregon Coast by matching simulated communities with observed dynamics. We compared reserves with harvested areas. Simulations suggested that different community networks, but with the same species composition, can represent each study site. Differences were found in predator-prey interactions as well as non-predatory interactions between community members. In addition, each site can be represented as a set of models, creating alternative stages among sites. The set of alternative models that characterize each study area depicts a sequence of functional responses where each specific model or interaction structure creates different species composition patterns. Different management practices, either in the past or of the present, may lead to alternative communities. Our findings suggest that management strategies should be analyzed at a community level that considers the possible consequences of shifting from one community scenario to another. This analysis provides a novel conceptual framework to assess the consequences of different management options for ecological communities. ?? 2008 Elsevier B.V. All rights reserved.

  4. 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.

  5. From calls to communities: a model for time varying social networks

    CERN Document Server

    Laurent, Guillaume; Karsai, Márton

    2015-01-01

    Social interactions vary in time and appear to be driven by intrinsic mechanisms, which in turn shape the emerging structure of the social network. Large-scale empirical observations of social interaction structure have become possible only recently, and modelling their dynamics is an actual challenge. Here we propose a temporal network model which builds on the framework of activity-driven time-varying networks with memory. The model also integrates key mechanisms that drive the formation of social ties - social reinforcement, focal closure and cyclic closure, which have been shown to give rise to community structure and the global connectedness of the network. We compare the proposed model with a real-world time-varying network of mobile phone communication and show that they share several characteristics from heterogeneous degrees and weights to rich community structure. Further, the strong and weak ties that emerge from the model follow similar weight-topology correlations as real-world social networks, i...

  6. 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

  7. Social Networks, Communication Styles, and Learning Performance in a CSCL Community

    Science.gov (United States)

    Cho, Hichang; Gay, Geri; Davidson, Barry; Ingraffea, Anthony

    2007-01-01

    The aim of this study is to empirically investigate the relationships between communication styles, social networks, and learning performance in a computer-supported collaborative learning (CSCL) community. Using social network analysis (SNA) and longitudinal survey data, we analyzed how 31 distributed learners developed collaborative learning…

  8. Effects of community-care networks on psychiatric emergency contacts, hospitalisation and involuntary admission

    NARCIS (Netherlands)

    A.I. Wierdsma (André); H.D. Poodt (Hilde); C.L. Mulder (Niels)

    2007-01-01

    textabstractBackground: Community-care networks are a partnership between the local police force, housing corporations, general social services, specialised home care and mental healthcare services. The networks were set up to improve the healthcare for patients with (chronic) psychiatric problems t

  9. Effects of community-care networks on psychiatric emergency contacts, hospitalisation and involuntary admission

    NARCIS (Netherlands)

    A.I. Wierdsma (André); H.D. Poodt (Hilde); C.L. Mulder (Niels)

    2007-01-01

    textabstractBackground: Community-care networks are a partnership between the local police force, housing corporations, general social services, specialised home care and mental healthcare services. The networks were set up to improve the healthcare for patients with (chronic) psychiatric problems

  10. Fostering Sociability in Learning Networks through Ad-Hoc Transient Communities

    NARCIS (Netherlands)

    Sloep, Peter

    2008-01-01

    Sloep, P. B. (2009). Fostering Sociability in Learning Networks through Ad-Hoc Transient Communities. In M. Purvis & B. T. R. Savarimuthu (Eds.), Computer-Mediated Social Networking. First International Conference, ICCMSN 2008, LNAI 5322 (pp. 62-75). Heidelberg, Germany: Springer. June, 11-13, 2008,

  11. Fostering Sociability in Learning Networks through Ad-Hoc Transient Communities

    NARCIS (Netherlands)

    Sloep, Peter

    2008-01-01

    Sloep, P. B. (2009). Fostering Sociability in Learning Networks through Ad-Hoc Transient Communities. In M. Purvis & B. T. R. Savarimuthu (Eds.), Computer-Mediated Social Networking. First International Conference, ICCMSN 2008, LNAI 5322 (pp. 62-75). Heidelberg, Germany: Springer. June, 11-13, 2008,

  12. Clusters, Graphs, and Networks for Analysing Internet-Web-Supported Communication within a Virtual Community

    CERN Document Server

    Polanco, Xavier

    2002-01-01

    The proposal is to use clusters, graphs and networks as models in order to analyse the Web structure. Clusters, graphs and networks provide knowledge representation and organization. Clusters were generated by co-site analysis. The sample is a set of academic Web sites from the countries belonging to the European Union. These clusters are here revisited from the point of view of graph theory and social network analysis. This is a quantitative and structural analysis. In fact, the Internet is a computer network that connects people and organizations. Thus we may consider it to be a social network. The set of Web academic sites represents an empirical social network, and is viewed as a virtual community. The network structural properties are here analysed applying together cluster analysis, graph theory and social network analysis.

  13. Using Social Network Analysis to Understand Sense of Community in an Online Learning Environment

    Science.gov (United States)

    Shen, Demei; Nuankhieo, Piyanan; Huang, Xinxin; Amelung, Christopher; Laffey, James

    2008-01-01

    This study uses social network analysis (SNA) in an innovative way to describe interaction and explain how interaction influences sense of community of students in online learning environments. The findings reveal differences on sense of community between two similarly structured online courses, and show unique interaction patterns for students in…

  14. Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks

    Directory of Open Access Journals (Sweden)

    Jingjing Ma

    2014-01-01

    Full Text Available Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

  15. Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks.

    Science.gov (United States)

    Ma, Jingjing; Liu, Jie; Ma, Wenping; Gong, Maoguo; Jiao, Licheng

    2014-01-01

    Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms.

  16. A Theoretical Framework for Building Online Communities of Practice with Social Networking Tools

    Science.gov (United States)

    Gunawardena, Charlotte N.; Hermans, Mary Beth; Sanchez, Damien; Richmond, Carol; Bohley, Maribeth; Tuttle, Rebekah

    2009-01-01

    This paper proposes a theoretical framework as a foundation for building online communities of practice when a suite of social networking applications referred to as collective intelligence tools are utilized to develop a product or solutions to a problem. Drawing on recent developments in Web 2.0 tools, research on communities of practice and…

  17. 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…

  18. 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…

  19. 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…

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

    NARCIS (Netherlands)

    Vollenbroek, Wouter; Vries, de Sjoerd; 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 o

  1. Identifying 'Hidden' Communities of Practice within Electronic Networks: Some Preliminary Premises

    CERN Document Server

    Ribeiro, Richard

    2008-01-01

    This paper examines the possibility of discovering 'hidden' (potential) Communities of Practice (CoPs) inside electronic networks, and then using this knowledge to nurture them into a fully developed Virtual Community of Practice (VCoP). Starting from the standpoint of the need to manage knowledge, it discusses several questions related to this subject: the characteristics of 'hidden' communities; the relation between CoPs, Virtual Communities (VCs), Distributed Communities of Practice (DCoPs) and Virtual Communities of Practice (VCoPs); the methods used to search for 'hidden' CoPs; and the possible ways of changing 'hidden' CoPs into fully developed VCoPs. The paper also presents some preliminary findings from a semi-structured interview conducted in The Higher Education Academy Psychology Network (UK). These findings are contrasted against the theory discussed and some additional proposals are suggested at the end.

  2. 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.

  3. Purpose-Driven Communities in Multiplex Networks: Thresholding User-Engaged Layer Aggregation

    Science.gov (United States)

    2016-06-01

    category sorting (Noordin example). . . . . . . 58 ix Figure 3.14 Step 3: Community detection algorithm (Noordin example). . . . 58 Figure 3.15 Step 4...methodology on a 4-layer Biological network, in which each layer represents information from a different subset of genes or proteins. The multiplex modularity...explanation of these topological characteristics terms, see Lewis ’ book, Network science: Theory and applications [48]. 3.1.1 Noordin Top Network The

  4. Finding instabilities in the community structure of complex networks

    Science.gov (United States)

    Gfeller, David; Chappelier, Jean-Cédric; de Los Rios, Paolo

    2005-11-01

    The problem of finding clusters in complex networks has been studied by mathematicians, computer scientists, and, more recently, by physicists. Many of the existing algorithms partition a network into clear clusters without overlap. Here we introduce a method to identify the nodes lying “between clusters,” allowing for a general measure of the stability of the clusters. This is done by adding noise over the edge weights. Our method can in principle be used with almost any clustering algorithm able to deal with weighted networks. We present several applications on real-world networks using two different clustering algorithms.

  5. 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.

  6. 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…

  7. Local Community Detection in Complex Networks Based on Maximum Cliques Extension

    Directory of Open Access Journals (Sweden)

    Meng Fanrong

    2014-01-01

    Full Text Available Detecting local community structure in complex networks is an appealing problem that has attracted increasing attention in various domains. However, most of the current local community detection algorithms, on one hand, are influenced by the state of the source node and, on the other hand, cannot effectively identify the multiple communities linked with the overlapping nodes. We proposed a novel local community detection algorithm based on maximum clique extension called LCD-MC. The proposed method firstly finds the set of all the maximum cliques containing the source node and initializes them as the starting local communities; then, it extends each unclassified local community by greedy optimization until a certain objective is satisfied; finally, the expected local communities will be obtained until all maximum cliques are assigned into a community. An empirical evaluation using both synthetic and real datasets demonstrates that our algorithm has a superior performance to some of the state-of-the-art approaches.

  8. 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)

  9. Muriel Wright: Telling the Story of Oklahoma Indian Nations

    Science.gov (United States)

    Cesar, Dana; Smith, Joan K.; Noley, Grayson

    2004-01-01

    The Wright family, descended from the patriarch Allen Wright, who arrived in the new Choctaw Nation after surviving the "Trail of Tears," played an important role in Oklahoma politics and society. Following removal to Oklahoma, Allen went on to become Principal Chief of the Choctaw Nation and gave the name, Oklahoma, to the southwest territory. He…

  10. Community Structure in Large Networks: Natural Cluster Sizes and the Absence of Large Well-Defined Clusters

    CERN Document Server

    Leskovec, Jure; Dasgupta, Anirban; Mahoney, Michael W

    2008-01-01

    A large body of work has been devoted to defining and identifying clusters or communities in social and information networks. We explore from a novel perspective several questions related to identifying meaningful communities in large social and information networks, and we come to several striking conclusions. We employ approximation algorithms for the graph partitioning problem to characterize as a function of size the statistical and structural properties of partitions of graphs that could plausibly be interpreted as communities. In particular, we define the network community profile plot, which characterizes the "best" possible community--according to the conductance measure--over a wide range of size scales. We study over 100 large real-world social and information networks. Our results suggest a significantly more refined picture of community structure in large networks than has been appreciated previously. In particular, we observe tight communities that are barely connected to the rest of the network ...

  11. A model framework for the enhancement of community detection in complex networks

    Science.gov (United States)

    He, Dongxiao; Wang, Hongcui; Jin, Di; Liu, Baolin

    2016-11-01

    Community detection is an important data analysis problem in many different areas, and how to enhance the quality of community detection in complicated real applications is still a challenge. Current community detection enhancement methods often take the enhancement as a preprocess of community detection. They mainly focus on how to design the suitable topological similarity of nodes to adjust the original network, but did not consider how to make use of this topological similarity more effectively. In order to better utilize the similarity information, we propose a model framework which integrates the enhancement into the whole community detection procedure. First, we calculate the structural similarity of nodes based on network topology. Second, we present a stochastic model to describe the community memberships of nodes; we then model the strong constraint based on structural similarity, i.e., we make each node have the same community membership distribution with its most similar neighbors; and then we model the weak constraint, i.e., if two nodes have a high similarity we will make their community membership distributions close, otherwise we will make them not close. Finally, we present a nonnegative matrix factorization approach to learn the model parameters. We evaluate our method on both synthetic and real-world networks with ground-truths, and compare it with five comparable methods. The experimental results demonstrate the superior performance of our new method over the competing ones for community detection and enhancement.

  12. Community detection in networks based on minimum spanning tree and modularity

    Science.gov (United States)

    Saoud, Bilal; Moussaoui, Abdelouahab

    2016-10-01

    In this paper we propose a novel splitting and merging method for community detection in which a minimum spanning tree (MST) of dissimilarity between nodes in graph is employed. In the splitting process, edges with high dissimilarity in the MST are removed to construct small disconnected subgroups of nodes from the same community. In the merging process, subgroup pairs are iteratively merged to identify the final community structure maximizing the modularity. The proposed method requires no parameter. We provide a general framework for implementing such a method. Experimental results obtained by applying the method on computer-generated networks and different real-world networks show the effectiveness of the proposed method.

  13. Community detection in networks: Modularity optimization and maximum likelihood are equivalent

    CERN Document Server

    Newman, M E J

    2016-01-01

    We demonstrate an exact equivalence between two widely used methods of community detection in networks, the method of modularity maximization in its generalized form which incorporates a resolution parameter controlling the size of the communities discovered, and the method of maximum likelihood applied to the special case of the stochastic block model known as the planted partition model, in which all communities in a network are assumed to have statistically similar properties. Among other things, this equivalence provides a mathematically principled derivation of the modularity function, clarifies the conditions and assumptions of its use, and gives an explicit formula for the optimal value of the resolution parameter.

  14. The GÉANT network: addressing current and future needs of the HEP community

    Science.gov (United States)

    Capone, Vincenzo; Usman, Mian

    2015-12-01

    The GÉANT infrastructure is the backbone that serves the scientific communities in Europe for their data movement needs and their access to international research and education networks. Using the extensive fibre footprint and infrastructure in Europe the GÉANT network delivers a portfolio of services aimed to best fit the specific needs of the users, including Authentication and Authorization Infrastructure, end-to-end performance monitoring, advanced network services (dynamic circuits, L2-L3VPN, MD-VPN). This talk will outline the factors that help the GÉANT network to respond to the needs of the High Energy Physics community, both in Europe and worldwide. The Pan-European network provides the connectivity between 40 European national research and education networks. In addition, GÉANT also connects the European NRENs to the R&E networks in other world region and has reach to over 110 NREN worldwide, making GÉANT the best connected Research and Education network, with its multiple intercontinental links to different continents e.g. North and South America, Africa and Asia-Pacific. The High Energy Physics computational needs have always had (and will keep having) a leading role among the scientific user groups of the GÉANT network: the LHCONE overlay network has been built, in collaboration with the other big world REN, specifically to address the peculiar needs of the LHC data movement. Recently, as a result of a series of coordinated efforts, the LHCONE network has been expanded to the Asia-Pacific area, and is going to include some of the main regional R&E network in the area. The LHC community is not the only one that is actively using a distributed computing model (hence the need for a high-performance network); new communities are arising, as BELLE II. GÉANT is deeply involved also with the BELLE II Experiment, to provide full support to their distributed computing model, along with a perfSONAR-based network monitoring system. GÉANT has also

  15. Identification of community structure in networks with convex optimization

    CERN Document Server

    Hildebrand, Roland

    2008-01-01

    We reformulate the problem of modularity maximization over the set of partitions of a network as a conic optimization problem over the completely positive cone, converting it from a combinatorial optimization problem to a convex continuous one. A semidefinite relaxation of this conic program then allows to compute upper bounds on the maximum modularity of the network. Based on the solution of the corresponding semidefinite program, we design a randomized algorithm generating partitions of the network with suboptimal modularities. We apply this algorithm to several benchmark networks, demonstrating that it is competitive in accuracy with the best algorithms previously known. We use our method to provide the first proof of optimality of a partition for a real-world network.

  16. Communities, modules and large-scale structure in networks

    Science.gov (United States)

    Newman, M. E. J.

    2012-01-01

    Networks, also called graphs by mathematicians, provide a useful abstraction of the structure of many complex systems, ranging from social systems and computer networks to biological networks and the state spaces of physical systems. In the past decade there have been significant advances in experiments to determine the topological structure of networked systems, but there remain substantial challenges in extracting scientific understanding from the large quantities of data produced by the experiments. A variety of basic measures and metrics are available that can tell us about small-scale structure in networks, such as correlations, connections and recurrent patterns, but it is considerably more difficult to quantify structure on medium and large scales, to understand the `big picture'. Important progress has been made, however, within the past few years, a selection of which is reviewed here.

  17. Are communities just bottlenecks? Trees and treelike networks have high modularity

    CERN Document Server

    Bagrow, James P

    2012-01-01

    Much effort has gone into understanding the modular nature of complex networks. Communities, also known as clusters or modules, are densely interconnected groups of nodes that are only sparsely connected to other groups in the network. Discovering high quality communities is a difficult and important problem in a number of areas. The most popular approach is the objective function known as Modularity, used to both discover communities and measure their strength. To understand the modular structure of networks it is then crucial to know how such functions evaluate different topologies, what features they account for and what implicit assumptions they may make. We show that trees and treelike networks can have unexpectedly and often arbitrarily high values of modularity. This is surprising since trees are maximally sparse connected graphs and are not typically considered to possess modular structure, yet the non-local null model used by modularity assigns low probabilities, and thus high significance, to the de...

  18. Uncovering the community structure in signed social networks based on greedy optimization

    Science.gov (United States)

    Chen, Yan; Yan, Jiaqi; Yang, Yu; Chen, Junhua

    2017-05-01

    The formality of signed relationships has been recently adopted in a lot of complicated systems. The relations among these entities are complicated and multifarious. We cannot indicate these relationships only by positive links, and signed networks have been becoming more and more universal in the study of social networks when community is being significant. In this paper, to identify communities in signed networks, we exploit a new greedy algorithm, taking signs and the density of these links into account. The main idea of the algorithm is the initial procedure of signed modularity and the corresponding update rules. Specially, we employ the “Asymmetric and Constrained Belief Evolution” procedure to evaluate the optimal number of communities. According to the experimental results, the algorithm is proved to be able to run well. More specifically, the proposed algorithm is very efficient for these networks with medium size, both dense and sparse.

  19. Eigenvector localization as a tool to study small communities in online social networks

    CERN Document Server

    Slanina, Frantisek; 10.1142/S0219525910002840

    2011-01-01

    We present and discuss a mathematical procedure for identification of small "communities" or segments within large bipartite networks. The procedure is based on spectral analysis of the matrix encoding network structure. The principal tool here is localization of eigenvectors of the matrix, by means of which the relevant network segments become visible. We exemplified our approach by analyzing the data related to product reviewing on Amazon.com. We found several segments, a kind of hybrid communities of densely interlinked reviewers and products, which we were able to meaningfully interpret in terms of the type and thematic categorization of reviewed items. The method provides a complementary approach to other ways of community detection, typically aiming at identification of large network modules.

  20. Detecting community structure in networks via consensus dynamics and spatial transformation

    Science.gov (United States)

    Yang, Bo; He, He; Hu, Xiaoming

    2017-10-01

    We present a novel clustering algorithm for community detection, based on the dynamics towards consensus and spatial transformation. The community detection problem is translated to a clustering problem in the N-dimensional Euclidean space by three stages: (1) the dynamics running on a network is emulated to a procedure of gas diffusion in a finite space; (2) the pressure distribution vectors are used to describe the influence that each node exerts on the whole network; (3) the similarity measures between two nodes are quantified in the N-dimensional Euclidean space by k-Nearest Neighbors method. After such steps, we could merge clusters according to their similarity distances and show the community structure of a network by a hierarchical clustering tree. Tests on several benchmark networks are presented and the results show the effectiveness and reliability of our algorithm.

  1. Investigating Student Communities with Network Analysis of Interactions in a Physics Learning Center

    CERN Document Server

    Brewe, Eric; Sawtelle, Vashti

    2011-01-01

    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. Finn and Rock [1] link the academic and social integration of students to increased rates of retention. 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 which contribute to participation in the learning community. ...

  2. 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

  3. 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.

  4. 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.

  5. 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.

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

    CERN Document Server

    Gurtner, Gérald; Cipolla, Marco; Lillo, Fabrizio; Mantegna, Rosario Nunzio; Miccichè, Salvatore; Pozzi, Simone

    2013-01-01

    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 airspaces and improve it by guiding the design of new ones. Specifically, we compare the performance of three community detection algorithms, 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.

  7. A weight's agglomerative method for detecting communities in weighted networks based on weight's similarity

    Institute of Scientific and Technical Information of China (English)

    Shen Yi

    2011-01-01

    This paper proposes the new definition of the community structure of the weighted networks that groups of nodes in which the edge's weights distribute uniformly but at random between them. It can describe the steady connections between nodes or some similarity between nodes' functions effectively.In order to detect the community structure efficiently,a threshold coefficient K to evaluate the equivalence of edges' weights and a new weighted modularity based on the weight's similarity are proposed. Then, constructing the weighted matrix and using the agglomerative mechanism,it presents a weight's agglomerative method based on optimizing the modularity to detect communities. For a network with n nodes, the algorithm can detect the community structure in time 0(n2 logn2).Simulations on networks show that the algorithm has higher accuracy and precision than the existing techniques. Furthermore, with the change of K the algorithm discovers a special hierarchical organization which can describe the various steady connections between nodes in groups.

  8. Detecting communities in social networks using label propagation with information entropy

    Science.gov (United States)

    Chen, Naiyue; Liu, Yun; Chen, Haiqiang; Cheng, Junjun

    2017-04-01

    Community detection has become an important and effective methodology to understand the structure and function of real world networks. The label propagation algorithm (LPA) is a near-linear time algorithm used to detect non-overlapping community. However, it merely considers the direct neighbor relationship. In this paper, we propose an algorithm to consider information entropy as the measurement of the relationship between direct neighbors and indirect neighbors. In a label update, we proposed a new belonging coefficient to describe the weight of the label. With the belonging coefficient no less than a threshold each node can keep one or more labels to constitute an overlapping community. Experimental results on both real-world and benchmark networks show that our algorithm also possesses high accuracy on detecting community structure in networks.

  9. 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.

  10. Node Embedding via Word Embedding for Network Community Discovery

    CERN Document Server

    Ding, Weicong; Ishwar, Prakash

    2016-01-01

    Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop a novel algorithm for unsupervised community discovery in graphs. Through extensive experimental studies on simulated and real-world data, we demonstrate that the proposed approach consistently improves over the current state-of-the-art. Specifically, our approach empirically attains the information-theoretic limits for community recovery under the benchmark Stochastic Block Models for graph generation and exhibits better stability and accuracy over both Spectral Clustering and Acyclic Belief Propagation in the community recovery limits.

  11. 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.

  12. Hybrid Self-Adaptive Algorithm for Community Detection in Complex Networks

    Directory of Open Access Journals (Sweden)

    Bin Xu

    2015-01-01

    Full Text Available The study of community detection algorithms in complex networks has been very active in the past several years. In this paper, a Hybrid Self-adaptive Community Detection Algorithm (HSCDA based on modularity is put forward first. In HSCDA, three different crossover and two different mutation operators for community detection are designed and then combined to form a strategy pool, in which the strategies will be selected probabilistically based on statistical self-adaptive learning framework. Then, by adopting the best evolving strategy in HSCDA, a Multiobjective Community Detection Algorithm (MCDA based on kernel k-means (KKM and ratio cut (RC objective functions is proposed which efficiently make use of recommendation of strategy by statistical self-adaptive learning framework, thus assisting the process of community detection. Experimental results on artificial and real networks show that the proposed algorithms achieve a better performance compared with similar state-of-the-art approaches.

  13. Sensemaking, safety, and situated communities in (con)temporary networks

    OpenAIRE

    2002-01-01

    This paper discusses the difficulties involved in managing knowledge-intensive, multinational, multiorganisational, and multifunctional project networks. The study is based on a 2-year quasi-ethnography of one such network engaged in the design and development of a complex new process control system for an existing pharmaceutical plant in Ireland. The case describes how, drawing upon the organisational heritage of the corporations involved and the logic implicit within their global partnershi...

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

    Science.gov (United States)

    2015-03-16

    level of transitivity are often more stable, balanced and harmonious . For social networks, Granovetter [3] in his work on “strength of weak ties...of schedules for the independent teams , relative to the other conferences in the FBS. Applying the proposed clustering algorithm to the FBS network...correctly identified all 11 conferences, as well as those teams that belong to those conferences. The “independent” teams were also assigned to a conference

  15. Partial Information Community Detection in a Multilayer Network

    Science.gov (United States)

    2016-06-01

    connected. They might live in the same hamlet, or attend the same mosque, or shop at the same markets. Some will communicate with others, and some are...Subgraphs Due to the large size and complexity of most networks, sometimes it is convenient to only analyze portions of a network. These smaller portions of...Transactions on, vol. 12, no. 6, pp. 1450– 1460, 2006. 103 [11] B. Lyon et al. (2003). The Opte Project. The Opte Project. [ Online ]. Avaial- able: http

  16. 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.

  17. 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...

  18. Finding communities in networks in the strong and almost-strong sense

    Science.gov (United States)

    Cafieri, Sonia; Caporossi, Gilles; Hansen, Pierre; Perron, Sylvain; Costa, Alberto

    2012-04-01

    Finding communities, or clusters or modules, in networks can be done by optimizing an objective function defined globally and/or by specifying conditions which must be satisfied by all communities. Radicchi [Proc. Natl. Acad. Sci. USAPNASA60027-842410.1073/pnas.0400054101 101, 2658 (2004)] define a susbset of vertices of a network to be a community in the strong sense if each vertex of that subset has a larger inner degree than its outer degree. A partition in the strong sense has only strong communities. In this paper we first define an enumerative algorithm to list all partitions in the strong sense of a network of moderate size. The results of this algorithm are given for the Zachary karate club data set, which is solved by hand, as well as for several well-known real-world problems of the literature. Moreover, this algorithm is slightly modified in order to apply it to larger networks, keeping only partitions with the largest number of communities. It is shown that some of the partitions obtained are informative, although they often have only a few communities, while they fail to give any information in other cases having only one community. It appears that degree 2 vertices play a big role in forcing large inhomogeneous communities. Therefore, a weakening of the strong condition is proposed and explored: we define a partition in the almost-strong sense by substituting a nonstrict inequality to a strict one in the definition of strong community for all vertices of degree 2. Results, for the same set of problems as before, then give partitions with a larger number of communities and are more informative.

  19. Reshaping urban lives: design as social intervention towards community networks

    OpenAIRE

    Franqueira, Teresa

    2007-01-01

    This paper aims to show some cases of creative communities based on collaborative services as a way to promote sustainable development. This scenario (creative communities and their services) offers design a different approach and a new opportunity to develop and enhance a sustainable future. The transition from the industrial age to the age of knowledge brings about diverse changes in the way we live. The collapse of the Welfare state and the globalisation have created new problems and...

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

    DEFF Research Database (Denmark)

    Ryberg, Thomas; Christiansen, Ellen

    2008-01-01

    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...... a multiplicity of sites. They show how the members thereby also become (co)producers of such resources, which then in turn become resources for other communities....

  1. 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.

  2. Characterization of Antimicrobial Resistance Dissemination across Plasmid Communities Classified by Network Analysis

    Directory of Open Access Journals (Sweden)

    Akifumi Yamashita

    2014-04-01

    Full Text Available The global clustering of gene families through network analysis has been demonstrated in whole genome, plasmid, and microbiome analyses. In this study, we carried out a plasmidome network analysis of all available complete bacterial plasmids to determine plasmid associations. A blastp clustering search at 100% aa identity cut-off and sharing at least one gene between plasmids, followed by a multilevel community network analysis revealed that a surprisingly large number of the plasmids were connected by one largest connected component (LCC, with dozens of community sub-groupings. The LCC consisted mainly of Bacilli and Gammaproteobacteria plasmids. Intriguingly, horizontal gene transfer (HGT was noted between different phyla (i.e., Staphylococcus and Pasteurellaceae, suggesting that Pasteurellaceae can acquire antimicrobial resistance (AMR genes from closely contacting Staphylococcus spp., which produce the external supplement of V-factor (NAD. Such community network analysis facilitate displaying possible recent HGTs like a class 1 integron, str and tet resistance markers between communities. Furthermore, the distribution of the Inc replicon type and AMR genes, such as the extended-spectrum ß-lactamase (ESBL CTX-M or the carbapenemases KPC NDM-1, implies that such genes generally circulate within limited communities belonging to typical bacterial genera. Thus, plasmidome network analysis provides a remarkable discriminatory power for plasmid-related HGT and evolution.

  3. 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.

  4. Community Discovery with Location-Interaction Disparity in Mobile Social Networks

    Institute of Scientific and Technical Information of China (English)

    Danmeng Liu; Wei Wei; Guojie Song; Ping Lu

    2015-01-01

    With the fast⁃growth of mobile social network, people ’s inter⁃actions are frequently marked with location information, such as longitude and latitude of visited base station. This boom of data has led to considerable interest in research fields such as user behavior mining, trajectory discovery and social demo⁃graphics. However, there is little research on community dis⁃covery in mobile social networks, and this is the problem this work tackles with. In this work, we take advantage of one sim⁃ple property that people in different locations often belong to different social circles in order to discover communities in these networks. Based on this property, which we referred to as Location⁃Interaction Disparity (LID), we proposed a state network and then define a quality function evaluating commu⁃nity detection results. We also propose a hybrid community⁃detection algorithm using LID for discovering location⁃based communities effectively and efficiently. Experiments on syn⁃thesis networks show that this algorithm can run effectively in time and discover communities with high precision. In real⁃world networks, the method reveals people ’s different social circles in different places with high efficiency.

  5. 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

    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...

  6. 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.

  7. Analysis of communities in a mythological social network

    CERN Document Server

    Miranda, Pedro J; Pinto, Sandro E de S

    2013-01-01

    The intriguing nature of classical Homeric narratives has always fascinated the occidental culture contributing to philosophy, history, mythology and straight forwardly to literature. However what would be so intriguing about Homer's narratives' At a first gaze we shall recognize the very literal appeal and aesthetic pleasure presented on every page across Homer's chants in Odyssey and rhapsodies in Iliad. Secondly we may perceive a biased aspect of its stories contents, varying from real-historical to fictional-mythological. To encompass this glance, there are some new archeological finding that supports historicity of some events described within Iliad, and consequently to Odyssey. Considering these observations and using complex network theory concepts, we managed to built and analyze a social network gathered across the classical epic, Odyssey of Homer. Longing for further understanding, topological quantities were collected in order to classify its social network qualitatively into real or fictional. It ...

  8. Detectability Thresholds and Optimal Algorithms for Community Structure in Dynamic Networks

    Science.gov (United States)

    Ghasemian, Amir; Zhang, Pan; Clauset, Aaron; Moore, Cristopher; Peel, Leto

    2016-07-01

    The detection of communities within a dynamic network is a common means for obtaining a coarse-grained view of a complex system and for investigating its underlying processes. While a number of methods have been proposed in the machine learning and physics literature, we lack a theoretical analysis of their strengths and weaknesses, or of the ultimate limits on when communities can be detected. Here, we study the fundamental limits of detecting community structure in dynamic networks. Specifically, we analyze the limits of detectability for a dynamic stochastic block model where nodes change their community memberships over time, but where edges are generated independently at each time step. Using the cavity method, we derive a precise detectability threshold as a function of the rate of change and the strength of the communities. Below this sharp threshold, we claim that no efficient algorithm can identify the communities better than chance. We then give two algorithms that are optimal in the sense that they succeed all the way down to this threshold. The first uses belief propagation, which gives asymptotically optimal accuracy, and the second is a fast spectral clustering algorithm, based on linearizing the belief propagation equations. These results extend our understanding of the limits of community detection in an important direction, and introduce new mathematical tools for similar extensions to networks with other types of auxiliary information.

  9. Community detection in weighted brain connectivity networks beyond the resolution limit

    CERN Document Server

    Nicolini, Carlo; Bifone, Angelo

    2016-01-01

    Graph theory provides a powerful framework to investigate brain functional connectivity networks and their modular organization. However, most graph-based methods suffer from a fundamental resolution limit that may have affected previous studies and prevented detection of modules, or communities, that are smaller than a specific scale. Surprise, a resolution-limit-free function rooted in discrete probability theory, has been recently introduced and applied to brain networks, revealing a wide size-distribution of functional modules, in contrast with many previous reports. However, the use of Surprise is limited to binary networks, while brain networks are intrinsically weighted, reflecting a continuous distribution of connectivity strengths between different brain regions. Here, we propose Asymptotical Surprise, a continuous version of Surprise, for the study of weighted brain connectivity networks, and validate this approach in synthetic networks endowed with a ground-truth modular structure. We compare Asymp...

  10. 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.

  11. Community Detection in Political Twitter Networks using Nonnegative Matrix Factorization Methods

    CERN Document Server

    Ozer, Mert; Davulcu, Hasan

    2016-01-01

    Community detection is a fundamental task in social network analysis. In this paper, first we develop an endorsement filtered user connectivity network by utilizing Heider's structural balance theory and certain Twitter triad patterns. Next, we develop three Nonnegative Matrix Factorization frameworks to investigate the contributions of different types of user connectivity and content information in community detection. We show that user content and endorsement filtered connectivity information are complementary to each other in clustering politically motivated users into pure political communities. Word usage is the strongest indicator of users' political orientation among all content categories. Incorporating user-word matrix and word similarity regularizer provides the missing link in connectivity only methods which suffer from detection of artificially large number of clusters for Twitter networks.

  12. 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.

  13. THE LICHENS OF NORTH CENTRAL OKLAHOMA

    Directory of Open Access Journals (Sweden)

    DARVIN WENDELL KECK

    2006-12-01

    Full Text Available Over 1,000 specimens of lichens were collected at 78 collecting stations in 11 counties of North Central Oklahoma during 1959 and 1960. The objectives were to identify lichens collected in the area; to establish a record of lichen distribution for each county in the area; and to analyze the ecological relationships.

  14. 76 FR 38263 - Oklahoma Disaster # OK-00052

    Science.gov (United States)

    2011-06-29

    ... ADMINISTRATION Oklahoma Disaster OK-00052 AGENCY: U.S. Small Business Administration. ACTION: Notice. SUMMARY: This is a Notice of the Presidential declaration of a major disaster for Public Assistance Only for... CONTACT: A. Escobar, Office of Disaster Assistance, U.S. Small Business Administration, 409 3rd Street,...

  15. Nutritional Risk among Oklahoma Congregate Meal Participants

    Science.gov (United States)

    Quigley, Kimberly K.; Hermann, Janice R.; Warde, William D.

    2008-01-01

    Objective: To determine if there were differences by demographic variables in response rates to Nutrition Screening Initiative (NSI) Checklist statements reported by over 50% of Oklahoma Older Americans Act Nutrition Program (OAANP) congregate meal participants categorized at high nutritional risk based on cumulative NSI Checklist scores. Design:…

  16. 76 FR 77578 - Oklahoma Disaster #OK-00057

    Science.gov (United States)

    2011-12-13

    ... ADMINISTRATION Oklahoma Disaster OK-00057 AGENCY: U.S. Small Business Administration. ACTION: Notice. SUMMARY...: 09/07/2012. ADDRESSES: Submit completed loan applications to: U.S. Small Business Administration... CONTACT: A. Escobar, Office of Disaster Assistance, U.S. Small Business Administration, 409 3rd Street...

  17. 78 FR 23622 - Oklahoma Disaster #OK-00070

    Science.gov (United States)

    2013-04-19

    ... ADMINISTRATION Oklahoma Disaster OK-00070 AGENCY: U.S. Small Business Administration. ACTION: Notice. SUMMARY...: Submit completed loan applications to: U.S. Small Business Administration, Processing and Disbursement... of Disaster Assistance, U.S. Small Business Administration, 409 3rd Street SW., Suite...

  18. 77 FR 34890 - Oklahoma Regulatory Program

    Science.gov (United States)

    2012-06-12

    ... a State to assume primacy for the regulation of surface coal mining and reclamation operations on... other things, ``* * * State law which provides for the regulation of surface coal mining and reclamation... Office of Surface Mining Reclamation and Enforcement 30 CFR Part 936 Oklahoma Regulatory Program AGENCY...

  19. Video Communication for Networked Communities: Challenges and Opportunities

    NARCIS (Netherlands)

    Stevens, T.; Cesar Garcia, P.S.; Kegel, I.; Farber, N.; Williams, D.; Ursu, M.; Stenton, P.; Torres, P.; Falekakis, M.; Kaiser, R.

    2012-01-01

    While advances in commercial video conferencing and social networking are driving more people to communicate using video, it is still difficult to achieve a sense of co-presence - that is to make the technology transparent to its users - when mediating ad hoc interactions between groups of people in

  20. Scalably Revealing the Dynamics of Soft Community Structure in Complex Networks

    Institute of Scientific and Technical Information of China (English)

    LI Huijia; LI Huiying

    2016-01-01

    Revealing the dynamics of community structure is of great concern for scientists from many fields.Specifically,how to quantify the dynamic details of soft community structure is a very interesting topic.In this paper,the authors propose a novel framework to study the scalable dynamic behavior of the soft community structure.First,the authors model the Potts dynamics to detect community structure using a "soft" Markov process.Then the soft stability of in a multiscale view is proposed to naturally uncover the local uniform behavior of spin values across multiple hierarchical levels.Finally,a new partition index is developed to detect fuzzy communities based on the stability and the dynamical information.Experiments on the both synthetically generated and real-world networks verify that the framework can be used to uncover hierarchical community structures effectively and efficiently.

  1. An improved algorithm for generalized community structure inference in complex networks

    Science.gov (United States)

    Qu, Yingfei; Shi, Weiren; Shi, Xin

    2017-07-01

    In recent years, the research of the community detection is not only on the structure that densely connected internally, but also on the structure of more patterns, such as heterogeneity, overlapping, core-periphery. In this paper, we build the network model based on the random graph models and propose an improved algorithm to infer the generalized community structures. We achieve it by introducing the generalized Bernstein polynomials and computing the latent parameters of vertices. The algorithm is tested both on the computer-generated benchmark networks and the real-world networks. Results show that the algorithm makes better performances on convergence speed and is able to discover the latent continuous structures in networks.

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

    Science.gov (United States)

    Cinner, Joshua E; Bodin, Orjan

    2010-08-11

    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. 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. 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 into communities' dependencies and usages of natural resources, and shows how patterns of

  3. Impact of a preceptor education board and computer network to engage community faculty at Dartmouth Medical School.

    Science.gov (United States)

    Kollisch, D O; Gephart, D; Brooks, W B; Gagne, R; Allen, C; Donahue, D

    1999-01-01

    In 1994, as part of the Generalist Physician Initiative of The Robert Wood Johnson Foundation, Dartmouth Medical School established two programs to support and engage community-based teaching. The Preceptor Education Board and Community Computer Network were established to support a network of community-based preceptors and to facilitate communication between course directors at the school and community-based teachers. The board's mission is to organize, develop, and support a network of community-based primary care faculty, and to create and review community-based curricula. Through the board, community faculty members have made substantial contributions to curriculum, evaluation, faculty development, governance, and financing in community-based teaching. The Community Computer Network provides hardware, software, network systems, and support. Course directors and students have reported improved community-based educational experiences as a direct result of the Network. These two initiatives are dynamic and effective ways to improve the quality of community-based education and preceptors' morale. These efforts have strengthened the community faculty and their connection to the academic medical center.

  4. Bread Loaf Rural Teacher Network: A Portable Community.

    Science.gov (United States)

    Active Learner: A Foxfire Journal for Teachers, 1998

    1998-01-01

    The experiences of two teachers describe how BreadNet, an online professional-development and educational conference, enables teachers with similar interests to work together and maintain a sense of community. BreadNet allowed their rural schools to participate in projects with distant schools, leading to improvements in the quantity and quality…

  5. The Evolution of Social and Semantic Networks in Epistemic Communities

    Science.gov (United States)

    Margolin, Drew Berkley

    2012-01-01

    This study describes and tests a model of scientific inquiry as an evolving, organizational phenomenon. Arguments are derived from organizational ecology and evolutionary theory. The empirical subject of study is an "epistemic community" of scientists publishing on a research topic in physics: the string theoretic concept of…

  6. 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…

  7. 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,…

  8. 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.

  9. [CAPNETZ. The competence network for community-acquired pneumonia (CAP)]. [Article in German

    DEFF Research Database (Denmark)

    von Plessen, Christian

    2016-01-01

    CAPNETZ is a medical competence network for community-acquired pneumonia (CAP), which was funded by the German Ministry for Education and Research. It has accomplished seminal work on pneumonia over the last 15 years. A unique infrastructure was established which has so far allowed us to recruit...... Sepsis) (PROGRESS), the Systems Medicine of Community Acquired Pneumonia Network (CAPSyS) and SFB-TR84 (Sonderforschungsbereich - Transregio 84). The main recipients (Charité Berlin, University Clinic Ulm and the Hannover Medical School) founded the CAPNETZ foundation and transferred all data...

  10. Detecting the community structure in complex networks based on quantum mechanics

    Science.gov (United States)

    Niu, Yan Qing; Hu, Bao Qing; Zhang, Wen; Wang, Min

    2008-10-01

    In this paper, we develop a novel method to detect the community structure in complex networks. This approach is based on the combination of kernel-based clustering using quantum mechanics, the spectral clustering technique and the concept of the Bayesian information criterion. We test the proposed algorithm on Zachary’s karate club network and the world of American college football. Experimental results indicate that our algorithm is efficient and effective at finding both the optimal number of clusters, and the best clustering of community structures.

  11. Community Structure of a Bank-Firm Credit Network in Japan

    Science.gov (United States)

    Iyetomi, Hiroshi; Matsuura, Yuki

    2014-03-01

    We study temporal change of community structure in a Japanese credit network formed by banks and listed firms through their financial relations over the last 30 years. The credit connectedness is regarded as a potenital source of systemic risk. Our network is a bipartite graph consisting of two species of nodes connected with bidirectional links. The direction of links is identified with that of risk flows and their weights are relative credit/loan with respect to the targets. In a partial credit network obtained only with the links pointing from firms toward banks, the city banks forms one major community in most of the time period to share risk when firms go wrong. On the other hand, a partial network only with the links from banks toward firms is decomposed into communities of similar size each of which has its own city bank, reflecting the main-bank system in Japan. Finally we take overlapping parts of the two community sets to find cores of the risk concentration in the credit network. This work was supported by JSPS KAKENHI Grant Number 22300080.

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

    OpenAIRE

    Smyth, N.; Siriwardhana, C.; Hotopf, M.; Hatch, S.L.

    2014-01-01

    Purpose 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. Methods Data is drawn from the...

  13. 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.

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

    Science.gov (United States)

    Yang, Jianmei; Yang, Huijie; Liao, Hao; Wang, Jiangtao; Zeng, Jinqun

    2015-01-01

    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. Ensemble-Based Algorithms to Detect Disjoint and Overlapping Communities in Networks

    CERN Document Server

    Chakraborty, Tanmoy; Subrahmanian, V S

    2016-01-01

    Given a set ${\\cal AL}$ of community detection algorithms and a graph $G$ as inputs, we propose two ensemble methods $\\mathtt{EnDisCO}$ and $\\mathtt{MeDOC}$ that (respectively) identify disjoint and overlapping communities in $G$. $\\mathtt{EnDisCO}$ transforms a graph into a latent feature space by leveraging multiple base solutions and discovers disjoint community structure. $\\mathtt{MeDOC}$ groups similar base communities into a meta-community and detects both disjoint and overlapping community structures. Experiments are conducted at different scales on both synthetically generated networks as well as on several real-world networks for which the underlying ground-truth community structure is available. Our extensive experiments show that both algorithms outperform state-of-the-art non-ensemble algorithms by a significant margin. Moreover, we compare $\\mathtt{EnDisCO}$ and $\\mathtt{MeDOC}$ with a recent ensemble method for disjoint community detection and show that our approaches achieve superior performanc...

  16. 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....

  17. Using Network Analysis to Understand Knowledge Mobilization in a Community-based Organization.

    Science.gov (United States)

    Gainforth, Heather L; Latimer-Cheung, Amy E; Moore, Spencer; Athanasopoulos, Peter; Martin Ginis, Kathleen A

    2015-06-01

    Knowledge mobilization (KM) has been described as putting research in the hands of research users. Network analysis is an empirical approach that has potential for examining the complex process of knowledge mobilization within community-based organizations (CBOs). Yet, conducting a network analysis in a CBO presents challenges. The purpose of this paper is to demonstrate the value and feasibility of using network analysis as a method for understanding knowledge mobilization within a CBO by (1) presenting challenges and solutions to conducting a network analysis in a CBO, (2) examining the feasibility of our methodology, and (3) demonstrating the utility of this methodology through an example of a network analysis conducted in a CBO engaging in knowledge mobilization activities. The final method used by the partnership team to conduct our network analysis of a CBO is described. An example of network analysis results of a CBO engaging in knowledge mobilization is presented. In total, 81 participants completed the network survey. All of the feasibility benchmarks set by the CBO were met. Results of the network analysis are highlighted and discussed as a means of identifying (1) prominent and influential individuals in the knowledge mobilization process and (2) areas for improvement in future knowledge mobilization initiatives. Findings demonstrate that network analysis can be feasibly used to provide a rich description of a CBO engaging in knowledge mobilization activities.

  18. Detection of gene communities in multi-networks reveals cancer drivers

    Science.gov (United States)

    Cantini, Laura; Medico, Enzo; Fortunato, Santo; Caselle, Michele

    2015-12-01

    We propose a new multi-network-based strategy to integrate different layers of genomic information and use them in a coordinate way to identify driving cancer genes. The multi-networks that we consider combine transcription factor co-targeting, microRNA co-targeting, protein-protein interaction and gene co-expression networks. The rationale behind this choice is that gene co-expression and protein-protein interactions require a tight coregulation of the partners and that such a fine tuned regulation can be obtained only combining both the transcriptional and post-transcriptional layers of regulation. To extract the relevant biological information from the multi-network we studied its partition into communities. To this end we applied a consensus clustering algorithm based on state of art community detection methods. Even if our procedure is valid in principle for any pathology in this work we concentrate on gastric, lung, pancreas and colorectal cancer and identified from the enrichment analysis of the multi-network communities a set of candidate driver cancer genes. Some of them were already known oncogenes while a few are new. The combination of the different layers of information allowed us to extract from the multi-network indications on the regulatory pattern and functional role of both the already known and the new candidate driver genes.

  19. Topological properties and community detection of venture capital network: Evidence from China

    Science.gov (United States)

    Jin, Yonghong; Zhang, Qi; Li, Sai-Ping

    2016-01-01

    Financial networks have been extensively studied as examples of real world complex networks. Based on the data from Chinese GEM and SME board, we establish a venture capital (VC) network to study the statistical properties, topological properties and community structure of the Chinese venture capital network. The result shows that there are no dominant venture capital firms in China which act as hubs in the VC network, and multi-company syndication is not popular in China, meaning that the relationships among venture capital companies are weak. The network is robust under either random or intentional attack, and possesses small world property. We also find from its community structure that, venture capital companies are more concentrated in developed districts but the links within the same district are scarce as compared to the links between different developed districts, indicating that venture capital companies are more willing to syndicate with companies in other developed districts. Furthermore, venture capital companies which invest in the same industry have closer relations within their communities than those which do not invest in the same industry.

  20. 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.

  1. Inferring meaningful communities from topology-constrained correlation networks.

    Directory of Open Access Journals (Sweden)

    Jose Sergio Hleap

    Full Text Available Community structure detection is an important tool in graph analysis. This can be done, among other ways, by solving for the partition set which optimizes the modularity scores [Formula: see text]. Here it is shown that topological constraints in correlation graphs induce over-fragmentation of community structures. A refinement step to this optimization based on Linear Discriminant Analysis (LDA and a statistical test for significance is proposed. In structured simulation constrained by topology, this novel approach performs better than the optimization of modularity alone. This method was also tested with two empirical datasets: the Roll-Call voting in the 110th US Senate constrained by geographic adjacency, and a biological dataset of 135 protein structures constrained by inter-residue contacts. The former dataset showed sub-structures in the communities that revealed a regional bias in the votes which transcend party affiliations. This is an interesting pattern given that the 110th Legislature was assumed to be a highly polarized government. The [Formula: see text]-amylase catalytic domain dataset (biological dataset was analyzed with and without topological constraints (inter-residue contacts. The results without topological constraints showed differences with the topology constrained one, but the LDA filtering did not change the outcome of the latter. This suggests that the LDA filtering is a robust way to solve the possible over-fragmentation when present, and that this method will not affect the results where there is no evidence of over-fragmentation.

  2. 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

  3. 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

  4. Network clustering and community detection using modulus of families of loops

    Science.gov (United States)

    Shakeri, Heman; Poggi-Corradini, Pietro; Albin, Nathan; Scoglio, Caterina

    2017-01-01

    We study the structure of loops in networks using the notion of modulus of loop families. We introduce an alternate measure of network clustering by quantifying the richness of families of (simple) loops. Modulus tries to minimize the expected overlap among loops by spreading the expected link usage optimally. We propose weighting networks using these expected link usages to improve classical community detection algorithms. We show that the proposed method enhances the performance of certain algorithms, such as spectral partitioning and modularity maximization heuristics, on standard benchmarks.

  5. 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.

  6. Moderation is best: effects of grazing intensity on plant--flower visitor networks in Mediterranean communities.

    Science.gov (United States)

    Lazaro, Amparo; Tscheulin, Thomas; Devalez, Jelle; Nakas, Georgios; Stefanaki, Anastasia; Hanlidou, Effie; Petanidou, Theodora

    2016-04-01

    The structure of pollination networks is an important indicator of ecosystem stability and functioning. Livestock grazing is a frequent land use practice that directly affects the abundance and diversity of flowers and pollinators and, therefore, may indirectly affect the structure of pollination networks. We studied how grazing intensity affected the structure of plant-flower visitor networks along a wide range of grazing intensities by sheep and goats, using data from 11 Mediterranean plant-flower visitor communities from Lesvos Island, Greece. We hypothesized that intermediate grazing might result in higher diversity as predicted by the Intermediate Disturbance Hypothesis, which could in turn confer more stability to the networks. Indeed, we found that networks at intermediate grazing intensities were larger, more generalized, more modular, and contained more diverse and even interactions. Despite general responses at the network level, the number of interactions and selectiveness of particular flower visitor and plant taxa in the networks responded differently to grazing intensity, presumably as a consequence of variation in the abundance of different taxa with grazing. Our results highlight the benefit of maintaining moderate levels of livestock grazing by sheep and goats to preserve the complexity and biodiversity of the rich Mediterranean communities, which have a long history of grazing by these domestic animals.

  7. Comparing patient and provider perceptions of home- and community-based services: social network analysis as a service integration metric.

    Science.gov (United States)

    Ryan, David P; Puri, Manveen; Liu, Barbara A

    2013-01-01

    Integrated home- and community-based services (HCBS) for frail seniors require a unique style of teamwork and collaboration. In four case studies, patient perceptions of teamwork and collaboration among their HCBS care providers are compared with those of the providers themselves using network analysis. The degree of coherence between these perceived networks are examined using network analytics, and network visualizations are discussed. The value of network analysis in research on HCBS is considered.

  8. 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.

  9. Constructing ecological interaction networks by correlation analysis: hints from community sampling

    Directory of Open Access Journals (Sweden)

    WenJun Zhang

    2011-09-01

    Full Text Available A set of methodology for constructing ecological interaction networks by correlation analysis of community sampling data was presented in this study. Nearly 30 data sets at different levels of taxa for different sampling seasons and locations were used to construct networks and find network properties. I defined the network constructed by Pearson linear correlation is the linear network, and the network constructed by quasi-linear correlation measure (e.g., Spearman correlation is the quasi-linear network. Two taxa with statistically significant linear or quasi-linear correlation are determined to interact. The quasi-linear network is more general than linear network.The results reveled that correlation distributions of Pearson linear correlation and partial linear correlation constructed networks are unimodal functions and most of them are short-head (mostly negative correlations and long-tailed (mostly positive correlations. Spearman correlation distributions are either long-head and short-tailed unimodal functions or monotonically increasing functions. It was found that both mean partial linear correlation and mean Pearson linear correlation were approximately 0. The proportion of positive (partial linear correlations declined significantly with the increase in taxa. The mean (partial linear correlation declined significantly with the increase of taxa. More than 90% of network interactions are positive interactions. The average connectance was 9.8% (9.3% for (partial linear correlation constructed network. The parameter λ in power low distribution (L(x=x-λ increased as the decline of taxon level (from functional group to species for the partial linear correlation constructed network. λ is in average 0.8 to 0.9. The number of (positive interactions increased with the number of taxa for both linear and partial linear correlations constructed networks. The addition of a taxon would result in an increase of 0.4 (0.3 interactions (positive

  10. 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

    Funding bodies are increasingly recognizing the need to provide graduates and researchers with access to short intensive courses in a variety of disciplines, in order both to improve the general skills base and to provide solid foundations on which researchers may build their careers. In response...... 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...

  11. Modularity functions maximization with nonnegative relaxation facilitates community detection in networks

    CERN Document Server

    Jiang, Jonathan Q

    2011-01-01

    We show here that the problem of maximizing a family of quantitative functions, encompassing both the modularity (Q-measure) and modularity density (D-measure), for community detection can be uniformly understood as a combinatoric optimization involving the trace of a matrix called modularity Laplacian. Instead of using traditional spectral relaxation, we apply additional nonnegative constraint into this graph clustering problem and design efficient algorithms to optimize the new objective. With the explicit nonnegative constraint, our solutions are very close to the ideal community indicator matrix and can directly assign nodes into communities. The near-orthogonal columns of the solution can be reformulated as the posterior probability of corresponding node belonging to each community. Therefore, the proposed method can be exploited to identify the fuzzy or overlapping communities and thus facilitates the understanding of the intrinsic structure of networks. Experimental results show that our new algorithm ...

  12. 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 activitie

  13. 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

  14. 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…

  15. 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

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

    NARCIS (Netherlands)

    Türkes, Okan; Scholten, Hans; 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 automatica

  17. 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…

  18. Do Institutional Social Networks Work? Fostering a Sense of Community and Enhancing Learning

    Science.gov (United States)

    Hatzipanagos, Stylianos; John, Bernadette A.

    2017-01-01

    In this paper we report on the evaluation of an institutional social network (KINSHIP) whose aims were to foster an improved sense of community, enhance communication and serve as a space to model digital professionalism for students at King's College London, UK. Our evaluation focused on a pilot where students' needs with regard to the provision…

  19. Ad-hoc transient communities in Learning Networks Connecting and supporting the learner

    NARCIS (Netherlands)

    Brouns, Francis

    2009-01-01

    Brouns, F. (2009). Ad-hoc transient communities in Learning Networks Connecting and supporting the learner. Presentation given for Korean delegation of Chonnam National University and Dankook University (researchers dr. Jeeheon Ryu and dr. Minjeong Kim and a Group of PhD and Master students). August

  20. 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…