Science.gov

Sample records for complex social networks

  1. Spatially Distributed Social Complex Networks

    NASA Astrophysics Data System (ADS)

    Frasco, Gerald F.; Sun, Jie; Rozenfeld, Hernán D.; ben-Avraham, Daniel

    2014-01-01

    We propose a bare-bones stochastic model that takes into account both the geographical distribution of people within a country and their complex network of connections. The model, which is designed to give rise to a scale-free network of social connections and to visually resemble the geographical spread seen in satellite pictures of the Earth at night, gives rise to a power-law distribution for the ranking of cities by population size (but for the largest cities) and reflects the notion that highly connected individuals tend to live in highly populated areas. It also yields some interesting insights regarding Gibrat's law for the rates of city growth (by population size), in partial support of the findings in a recent analysis of real data [Rozenfeld et al., Proc. Natl. Acad. Sci. U.S.A. 105, 18702 (2008).]. The model produces a nontrivial relation between city population and city population density and a superlinear relationship between social connectivity and city population, both of which seem quite in line with real data.

  2. Social networks as embedded complex adaptive systems.

    PubMed

    Benham-Hutchins, Marge; Clancy, Thomas R

    2010-09-01

    As systems evolve over time, their natural tendency is to become increasingly more complex. Studies in the field of complex systems have generated new perspectives on management in social organizations such as hospitals. Much of this research appears as a natural extension of the cross-disciplinary field of systems theory. This is the 15th in a series of articles applying complex systems science to the traditional management concepts of planning, organizing, directing, coordinating, and controlling. In this article, the authors discuss healthcare social networks as a hierarchy of embedded complex adaptive systems. The authors further examine the use of social network analysis tools as a means to understand complex communication patterns and reduce medical errors.

  3. Disease Surveillance on Complex Social Networks

    PubMed Central

    Herrera, Jose L.; Srinivasan, Ravi; Brownstein, John S.; Galvani, Alison P.; Meyers, Lauren Ancel

    2016-01-01

    As infectious disease surveillance systems expand to include digital, crowd-sourced, and social network data, public health agencies are gaining unprecedented access to high-resolution data and have an opportunity to selectively monitor informative individuals. Contact networks, which are the webs of interaction through which diseases spread, determine whether and when individuals become infected, and thus who might serve as early and accurate surveillance sensors. Here, we evaluate three strategies for selecting sensors—sampling the most connected, random, and friends of random individuals—in three complex social networks—a simple scale-free network, an empirical Venezuelan college student network, and an empirical Montreal wireless hotspot usage network. Across five different surveillance goals—early and accurate detection of epidemic emergence and peak, and general situational awareness—we find that the optimal choice of sensors depends on the public health goal, the underlying network and the reproduction number of the disease (R0). For diseases with a low R0, the most connected individuals provide the earliest and most accurate information about both the onset and peak of an outbreak. However, identifying network hubs is often impractical, and they can be misleading if monitored for general situational awareness, if the underlying network has significant community structure, or if R0 is high or unknown. Taking a theoretical approach, we also derive the optimal surveillance system for early outbreak detection but find that real-world identification of such sensors would be nearly impossible. By contrast, the friends-of-random strategy offers a more practical and robust alternative. It can be readily implemented without prior knowledge of the network, and by identifying sensors with higher than average, but not the highest, epidemiological risk, it provides reasonably early and accurate information. PMID:27415615

  4. The complexity of older adults' social support networks.

    PubMed

    Chaichanawirote, Uraiwan; Higgins, Patricia A

    2013-10-01

    The purpose of this study was to provide a detailed snapshot of the diversity of social support networks of 95 independent-living older adults (mean age = 76). Participants in the convenience sample were recruited from senior centers and a retirement community. Using the Arizona Social Support Interview Schedule and egocentric network analysis, participants' networks are described in terms of patterns, density, size of positive networks (available and utilized), size of negative networks (available and utilized), support need, and support satisfaction. Each participant and the identified members of his or her network were considered a complex adaptive system. Network boundary was 7 members; average network size was 6.22 members (SD = 1.50); network density was moderate (mean = 0.53, SD = 0.33); positive interaction networks were larger than negative networks; and overall, participants reported moderate support need (mean = 2.5, SD = 0.7) and high support satisfaction (mean = 5.9, SD = 1.0).

  5. Bidirectional selection between two classes in complex social networks.

    PubMed

    Zhou, Bin; He, Zhe; Jiang, Luo-Luo; Wang, Nian-Xin; Wang, Bing-Hong

    2014-12-19

    The bidirectional selection between two classes widely emerges in various social lives, such as commercial trading and mate choosing. Until now, the discussions on bidirectional selection in structured human society are quite limited. We demonstrated theoretically that the rate of successfully matching is affected greatly by individuals' neighborhoods in social networks, regardless of the type of networks. Furthermore, it is found that the high average degree of networks contributes to increasing rates of successful matches. The matching performance in different types of networks has been quantitatively investigated, revealing that the small-world networks reinforces the matching rate more than scale-free networks at given average degree. In addition, our analysis is consistent with the modeling result, which provides the theoretical understanding of underlying mechanisms of matching in complex networks.

  6. Evolution of Cooperation in Social Dilemmas on Complex Networks

    PubMed Central

    Iyer, Swami; Killingback, Timothy

    2016-01-01

    Cooperation in social dilemmas is essential for the functioning of systems at multiple levels of complexity, from the simplest biological organisms to the most sophisticated human societies. Cooperation, although widespread, is fundamentally challenging to explain evolutionarily, since natural selection typically favors selfish behavior which is not socially optimal. Here we study the evolution of cooperation in three exemplars of key social dilemmas, representing the prisoner’s dilemma, hawk-dove and coordination classes of games, in structured populations defined by complex networks. Using individual-based simulations of the games on model and empirical networks, we give a detailed comparative study of the effects of the structural properties of a network, such as its average degree, variance in degree distribution, clustering coefficient, and assortativity coefficient, on the promotion of cooperative behavior in all three classes of games. PMID:26928428

  7. Theory of rumour spreading in complex social networks

    NASA Astrophysics Data System (ADS)

    Nekovee, M.; Moreno, Y.; Bianconi, G.; Marsili, M.

    2007-01-01

    We introduce a general stochastic model for the spread of rumours, and derive mean-field equations that describe the dynamics of the model on complex social networks (in particular, those mediated by the Internet). We use analytical and numerical solutions of these equations to examine the threshold behaviour and dynamics of the model on several models of such networks: random graphs, uncorrelated scale-free networks and scale-free networks with assortative degree correlations. We show that in both homogeneous networks and random graphs the model exhibits a critical threshold in the rumour spreading rate below which a rumour cannot propagate in the system. In the case of scale-free networks, on the other hand, this threshold becomes vanishingly small in the limit of infinite system size. We find that the initial rate at which a rumour spreads is much higher in scale-free networks than in random graphs, and that the rate at which the spreading proceeds on scale-free networks is further increased when assortative degree correlations are introduced. The impact of degree correlations on the final fraction of nodes that ever hears a rumour, however, depends on the interplay between network topology and the rumour spreading rate. Our results show that scale-free social networks are prone to the spreading of rumours, just as they are to the spreading of infections. They are relevant to the spreading dynamics of chain emails, viral advertising and large-scale information dissemination algorithms on the Internet.

  8. Rumor spreading model with noise interference in complex social networks

    NASA Astrophysics Data System (ADS)

    Zhu, Liang; Wang, Youguo

    2017-03-01

    In this paper, a modified susceptible-infected-removed (SIR) model has been proposed to explore rumor diffusion on complex social networks. We take variation of connectivity into consideration and assume the variation as noise. On the basis of related literature on virus networks, the noise is described as standard Brownian motion while stochastic differential equations (SDE) have been derived to characterize dynamics of rumor diffusion both on homogeneous networks and heterogeneous networks. Then, theoretical analysis on homogeneous networks has been demonstrated to investigate the solution of SDE model and the steady state of rumor diffusion. Simulations both on Barabási-Albert (BA) network and Watts-Strogatz (WS) network display that the addition of noise accelerates rumor diffusion and expands diffusion size, meanwhile, the spreading speed on BA network is much faster than on WS network under the same noise intensity. In addition, there exists a rumor diffusion threshold in statistical average meaning on homogeneous network which is absent on heterogeneous network. Finally, we find a positive correlation between peak value of infected individuals and noise intensity while a negative correlation between rumor lifecycle and noise intensity overall.

  9. Layered Social Network Analysis Reveals Complex Relationships in Kindergarteners

    PubMed Central

    Golemiec, Mireille; Schneider, Jonathan; Boyce, W. Thomas; Bush, Nicole R.; Adler, Nancy; Levine, Joel D.

    2016-01-01

    The interplay between individuals forms building blocks for social structure. Here, we examine the structure of behavioral interactions among kindergarten classroom with a hierarchy-neutral approach to examine all possible underlying patterns in the formation of layered networks of “reciprocal” interactions. To understand how these layers are coordinated, we used a layered motif approach. Our dual layered motif analysis can therefore be thought of as the dynamics of smaller groups that tile to create the group structure, or alternatively they provide information on what the average child would do in a given local social environment. When we examine the regulated motifs in layered networks, we find that transitivity is at least partially involved in the formation of these layered network structures. We also found complex combinations of the expected reciprocal interactions. The mechanisms used to understand social networks of kindergarten children here are also applicable on a more general scale to any group of individuals where interactions and identities can be readily observed and scored. PMID:26973572

  10. The food environment is a complex social network.

    PubMed

    Brown, David R; Brewster, Luther G

    2015-05-01

    The lack of demonstrated impact of the South LA fast food ban suggests that the policy was too narrowly crafted. Healthy food deserts like South LA are simultaneously unhealthy food swamps; and face myriad interrelated social, economic, and environmental challenges. The food environment is a complex social network impacted by social, economic and political factors at the neighborhood, regional, national, and international levels. Banning one subtype of unhealthy food venue is not likely to limit the availability of unhealthy processed and packaged foods nor result in increased access to affordable healthy foods. Food deserts and food insecurity are symptoms of the interacting pathologies of poverty, distressed communities, and unhealthy global macroeconomic and industrial policies. Policies that seek to impact urban health disparities need to tackle root causes including poverty and the global production and distribution of cheap, addictive, unhealthy products that promote unhealthy lifestyles.

  11. Learning to Predict Social Influence in Complex Networks

    DTIC Science & Technology

    2012-03-29

    properties of the basic model, including how the degree distribution and the network size affect the mean time to reach consensus , have been extensively...including how the degree distribution and the network size af- fect the mean time to reach consensus [10, 12]. Several variants of the voter model are also...social network, given the network topology and the observed time -sequence data. The model allows time -delay in information diffusion under the

  12. Research on Critical Nodes Algorithm in Social Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Xue-Guang

    2017-01-01

    Discovering critical nodes in social networks has many important applications and has attracted more and more institutions and scholars. How to determine the K critical nodes with the most influence in a social network is a NP (define) problem. Considering the widespread community structure, this paper presents an algorithm for discovering critical nodes based on two information diffusion models and obtains each node's marginal contribution by using a Monte-Carlo method in social networks. The solution of the critical nodes problem is the K nodes with the highest marginal contributions. The feasibility and effectiveness of our method have been verified on two synthetic datasets and four real datasets.

  13. Impact of Social Punishment on Cooperative Behavior in Complex Networks

    PubMed Central

    Wang, Zhen; Xia, Cheng-Yi; Meloni, Sandro; Zhou, Chang-Song; Moreno, Yamir

    2013-01-01

    Social punishment is a mechanism by which cooperative individuals spend part of their resources to penalize defectors. In this paper, we study the evolution of cooperation in 2-person evolutionary games on networks when a mechanism for social punishment is introduced. Specifically, we introduce a new kind of role, punisher, which is aimed at reducing the earnings of defectors by applying to them a social fee. Results from numerical simulations show that different equilibria allowing the three strategies to coexist are possible as well as that social punishment further enhance the robustness of cooperation. Our results are confirmed for different network topologies and two evolutionary games. In addition, we analyze the microscopic mechanisms that give rise to the observed macroscopic behaviors in both homogeneous and heterogeneous networks. Our conclusions might provide additional insights for understanding the roots of cooperation in social systems. PMID:24162105

  14. Impact of Social Punishment on Cooperative Behavior in Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Xia, Cheng-Yi; Meloni, Sandro; Zhou, Chang-Song; Moreno, Yamir

    2013-10-01

    Social punishment is a mechanism by which cooperative individuals spend part of their resources to penalize defectors. In this paper, we study the evolution of cooperation in 2-person evolutionary games on networks when a mechanism for social punishment is introduced. Specifically, we introduce a new kind of role, punisher, which is aimed at reducing the earnings of defectors by applying to them a social fee. Results from numerical simulations show that different equilibria allowing the three strategies to coexist are possible as well as that social punishment further enhance the robustness of cooperation. Our results are confirmed for different network topologies and two evolutionary games. In addition, we analyze the microscopic mechanisms that give rise to the observed macroscopic behaviors in both homogeneous and heterogeneous networks. Our conclusions might provide additional insights for understanding the roots of cooperation in social systems.

  15. A Method for Group Extraction in Complex Social Networks

    NASA Astrophysics Data System (ADS)

    Bródka, Piotr; Musial, Katarzyna; Kazienko, Przemysław

    The extraction of social groups from social networks existing among employees in the company, its customers or users of various computer systems became one of the research areas of growing importance. Once we have discovered the groups, we can utilise them, in different kinds of recommender systems or in the analysis of the team structure and communication within a given population.

  16. Coevolving complex networks in the model of social interactions

    NASA Astrophysics Data System (ADS)

    Raducha, Tomasz; Gubiec, Tomasz

    2017-04-01

    We analyze Axelrod's model of social interactions on coevolving complex networks. We introduce four extensions with different mechanisms of edge rewiring. The models are intended to catch two kinds of interactions-preferential attachment, which can be observed in scientists or actors collaborations, and local rewiring, which can be observed in friendship formation in everyday relations. Numerical simulations show that proposed dynamics can lead to the power-law distribution of nodes' degree and high value of the clustering coefficient, while still retaining the small-world effect in three models. All models are characterized by two phase transitions of a different nature. In case of local rewiring we obtain order-disorder discontinuous phase transition even in the thermodynamic limit, while in case of long-distance switching discontinuity disappears in the thermodynamic limit, leaving one continuous phase transition. In addition, we discover a new and universal characteristic of the second transition point-an abrupt increase of the clustering coefficient, due to formation of many small complete subgraphs inside the network.

  17. Advertising and Irreversible Opinion Spreading in Complex Social Networks

    NASA Astrophysics Data System (ADS)

    Candia, Julián

    Irreversible opinion spreading phenomena are studied on small-world and scale-free networks by means of the magnetic Eden model, a nonequilibrium kinetic model for the growth of binary mixtures in contact with a thermal bath. In this model, the opinion of an individual is affected by those of their acquaintances, but opinion changes (analogous to spin flips in an Ising-like model) are not allowed. We focus on the influence of advertising, which is represented by external magnetic fields. The interplay and competition between temperature and fields lead to order-disorder transitions, which are found to also depend on the link density and the topology of the complex network substrate. The effects of advertising campaigns with variable duration, as well as the best cost-effective strategies to achieve consensus within different scenarios, are also discussed.

  18. Visual social network analysis: effective approach to model complex human social, behaviour & culture.

    PubMed

    Ahram, Tareq Z; Karwowski, Waldemar

    2012-01-01

    The advent and adoption of internet-based social networking has significantly altered our daily lives. The educational community has taken notice of the positive aspects of social networking such as creation of blogs and to support groups of system designers going through the same challenges and difficulties. This paper introduces a social networking framework for collaborative education, design and modeling of the next generation of smarter products and services. Human behaviour modeling in social networking application aims to ensure that human considerations for learners and designers have a prominent place in the integrated design and development of sustainable, smarter products throughout the total system lifecycle. Social networks blend self-directed learning and prescribed, existing information. The self-directed element creates interest within a learner and the ability to access existing information facilitates its transfer, and eventual retention of knowledge acquired.

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

    ERIC Educational Resources Information Center

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

    2012-01-01

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

  20. The Analysis of Social Networks.

    PubMed

    O'Malley, A James; Marsden, Peter V

    2008-12-01

    Many questions about the social organization of medicine and health services involve interdependencies among social actors that may be depicted by networks of relationships. Social network studies have been pursued for some time in social science disciplines, where numerous descriptive methods for analyzing them have been proposed. More recently, interest in the analysis of social network data has grown among statisticians, who have developed more elaborate models and methods for fitting them to network data. This article reviews fundamentals of, and recent innovations in, social network analysis using a physician influence network as an example. After introducing forms of network data, basic network statistics, and common descriptive measures, it describes two distinct types of statistical models for network data: individual-outcome models in which networks enter the construction of explanatory variables, and relational models in which the network itself is a multivariate dependent variable. Complexities in estimating both types of models arise due to the complex correlation structures among outcome measures.

  1. Semantic Networks and Social Networks

    ERIC Educational Resources Information Center

    Downes, Stephen

    2005-01-01

    Purpose: To illustrate the need for social network metadata within semantic metadata. Design/methodology/approach: Surveys properties of social networks and the semantic web, suggests that social network analysis applies to semantic content, argues that semantic content is more searchable if social network metadata is merged with semantic web…

  2. Emergent Complex Network Geometry

    PubMed Central

    Wu, Zhihao; Menichetti, Giulia; Rahmede, Christoph; Bianconi, Ginestra

    2015-01-01

    Networks are mathematical structures that are universally used to describe a large variety of complex systems such as the brain or the Internet. Characterizing the geometrical properties of these networks has become increasingly relevant for routing problems, inference and data mining. In real growing networks, topological, structural and geometrical properties emerge spontaneously from their dynamical rules. Nevertheless we still miss a model in which networks develop an emergent complex geometry. Here we show that a single two parameter network model, the growing geometrical network, can generate complex network geometries with non-trivial distribution of curvatures, combining exponential growth and small-world properties with finite spectral dimensionality. In one limit, the non-equilibrium dynamical rules of these networks can generate scale-free networks with clustering and communities, in another limit planar random geometries with non-trivial modularity. Finally we find that these properties of the geometrical growing networks are present in a large set of real networks describing biological, social and technological systems. PMID:25985280

  3. Synchronization in complex networks

    SciTech Connect

    Arenas, A.; Diaz-Guilera, A.; Moreno, Y.; Zhou, C.; Kurths, J.

    2007-12-12

    Synchronization processes in populations of locally interacting elements are in the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understand synchronization phenomena in natural systems take now advantage of the recent theory of complex networks. In this review, we report the advances in the comprehension of synchronization phenomena when oscillating elements are constrained to interact in a complex network topology. We also overview the new emergent features coming out from the interplay between the structure and the function of the underlying pattern of connections. Extensive numerical work as well as analytical approaches to the problem are presented. Finally, we review several applications of synchronization in complex networks to different disciplines: biological systems and neuroscience, engineering and computer science, and economy and social sciences.

  4. Predicting Human Preferences Using the Block Structure of Complex Social Networks

    PubMed Central

    Guimerà, Roger; Llorente, Alejandro; Moro, Esteban; Sales-Pardo, Marta

    2012-01-01

    With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a “new” computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups. PMID:22984533

  5. Predicting human preferences using the block structure of complex social networks.

    PubMed

    Guimerà, Roger; Llorente, Alejandro; Moro, Esteban; Sales-Pardo, Marta

    2012-01-01

    With ever-increasing available data, predicting individuals' preferences and helping them locate the most relevant information has become a pressing need. Understanding and predicting preferences is also important from a fundamental point of view, as part of what has been called a "new" computational social science. Here, we propose a novel approach based on stochastic block models, which have been developed by sociologists as plausible models of complex networks of social interactions. Our model is in the spirit of predicting individuals' preferences based on the preferences of others but, rather than fitting a particular model, we rely on a Bayesian approach that samples over the ensemble of all possible models. We show that our approach is considerably more accurate than leading recommender algorithms, with major relative improvements between 38% and 99% over industry-level algorithms. Besides, our approach sheds light on decision-making processes by identifying groups of individuals that have consistently similar preferences, and enabling the analysis of the characteristics of those groups.

  6. Organization of complex networks

    NASA Astrophysics Data System (ADS)

    Kitsak, Maksim

    Many large complex systems can be successfully analyzed using the language of graphs and networks. Interactions between the objects in a network are treated as links connecting nodes. This approach to understanding the structure of networks is an important step toward understanding the way corresponding complex systems function. Using the tools of statistical physics, we analyze the structure of networks as they are found in complex systems such as the Internet, the World Wide Web, and numerous industrial and social networks. In the first chapter we apply the concept of self-similarity to the study of transport properties in complex networks. Self-similar or fractal networks, unlike non-fractal networks, exhibit similarity on a range of scales. We find that these fractal networks have transport properties that differ from those of non-fractal networks. In non-fractal networks, transport flows primarily through the hubs. In fractal networks, the self-similar structure requires any transport to also flow through nodes that have only a few connections. We also study, in models and in real networks, the crossover from fractal to non-fractal networks that occurs when a small number of random interactions are added by means of scaling techniques. In the second chapter we use k-core techniques to study dynamic processes in networks. The k-core of a network is the network's largest component that, within itself, exhibits all nodes with at least k connections. We use this k-core analysis to estimate the relative leadership positions of firms in the Life Science (LS) and Information and Communication Technology (ICT) sectors of industry. We study the differences in the k-core structure between the LS and the ICT sectors. We find that the lead segment (highest k-core) of the LS sector, unlike that of the ICT sector, is remarkably stable over time: once a particular firm enters the lead segment, it is likely to remain there for many years. In the third chapter we study how

  7. Emergent Complex Behavior in Social Networks: Examples from the Ktunaxa Speech Community

    ERIC Educational Resources Information Center

    Horsethief, Christopher

    2012-01-01

    Language serves as a primary tool for structuring identity and loss of language represents the loss of that identity. This study utilizes a social network analysis of Ktunaxa speech community activities for evidence of internally generated revitalization efforts. These behaviors include instances of self-organized emergence. Such emergent behavior…

  8. School participation and social networks of children with complex communication needs, physical disabilities, and typically developing peers.

    PubMed

    Raghavendra, Parimala; Olsson, Catherine; Sampson, Janelle; McInerney, Rachael; Connell, Timothy

    2012-03-01

    The aim of the study was to describe and compare the school participation and social networks of children with physical disabilities and complex communication needs (Group CCN), children with physical disabilities only (Group PD), and children with typical development (Group TD). The 39 participants, 10-15 years of age, were observed for 4 hours at school. School staff and the parent and/or child provided information on children's social networks. A striking observation was that, while participants in Group TD continuously conversed and socialized with peers inside and outside classrooms; those in Group CCN rarely used aided AAC, were provided with limited communication opportunities at school, and had fewer acquaintances and friends. Findings warrant intervention at the participation level at school and in the community.

  9. Wayfinding in Social Networks

    NASA Astrophysics Data System (ADS)

    Liben-Nowell, David

    With the recent explosion of popularity of commercial social-networking sites like Facebook and MySpace, the size of social networks that can be studied scientifically has passed from the scale traditionally studied by sociologists and anthropologists to the scale of networks more typically studied by computer scientists. In this chapter, I will highlight a recent line of computational research into the modeling and analysis of the small-world phenomenon - the observation that typical pairs of people in a social network are connected by very short chains of intermediate friends - and the ability of members of a large social network to collectively find efficient routes to reach individuals in the network. I will survey several recent mathematical models of social networks that account for these phenomena, with an emphasis on both the provable properties of these social-network models and the empirical validation of the models against real large-scale social-network data.

  10. Impact of Social Reward on the Evolution of the Cooperation Behavior in Complex Networks.

    PubMed

    Wu, Yu'e; Chang, Shuhua; Zhang, Zhipeng; Deng, Zhenghong

    2017-01-23

    Social reward, as a significant mechanism explaining the evolution of cooperation, has attracted great attention both theoretically and experimentally. In this paper, we study the evolution of cooperation by proposing a reward model in network population, where a third strategy, reward, as an independent yet particular type of cooperation is introduced in 2-person evolutionary games. Specifically, a new kind of role corresponding to reward strategy, reward agents, is defined, which is aimed at increasing the income of cooperators by applying to them a social reward. Results from numerical simulations show that consideration of social reward greatly promotes the evolution of cooperation, which is confirmed for different network topologies and two evolutionary games. Moreover, we explore the microscopic mechanisms for the promotion of cooperation in the three-strategy model. As expected, the reward agents play a vital role in the formation of cooperative clusters, thus resisting the aggression of defectors. Our research might provide valuable insights into further exploring the nature of cooperation in the real world.

  11. Impact of Social Reward on the Evolution of the Cooperation Behavior in Complex Networks

    NASA Astrophysics Data System (ADS)

    Wu, Yu’E.; Chang, Shuhua; Zhang, Zhipeng; Deng, Zhenghong

    2017-01-01

    Social reward, as a significant mechanism explaining the evolution of cooperation, has attracted great attention both theoretically and experimentally. In this paper, we study the evolution of cooperation by proposing a reward model in network population, where a third strategy, reward, as an independent yet particular type of cooperation is introduced in 2-person evolutionary games. Specifically, a new kind of role corresponding to reward strategy, reward agents, is defined, which is aimed at increasing the income of cooperators by applying to them a social reward. Results from numerical simulations show that consideration of social reward greatly promotes the evolution of cooperation, which is confirmed for different network topologies and two evolutionary games. Moreover, we explore the microscopic mechanisms for the promotion of cooperation in the three-strategy model. As expected, the reward agents play a vital role in the formation of cooperative clusters, thus resisting the aggression of defectors. Our research might provide valuable insights into further exploring the nature of cooperation in the real world.

  12. Impact of Social Reward on the Evolution of the Cooperation Behavior in Complex Networks

    PubMed Central

    Wu, Yu’e; Chang, Shuhua; Zhang, Zhipeng; Deng, Zhenghong

    2017-01-01

    Social reward, as a significant mechanism explaining the evolution of cooperation, has attracted great attention both theoretically and experimentally. In this paper, we study the evolution of cooperation by proposing a reward model in network population, where a third strategy, reward, as an independent yet particular type of cooperation is introduced in 2-person evolutionary games. Specifically, a new kind of role corresponding to reward strategy, reward agents, is defined, which is aimed at increasing the income of cooperators by applying to them a social reward. Results from numerical simulations show that consideration of social reward greatly promotes the evolution of cooperation, which is confirmed for different network topologies and two evolutionary games. Moreover, we explore the microscopic mechanisms for the promotion of cooperation in the three-strategy model. As expected, the reward agents play a vital role in the formation of cooperative clusters, thus resisting the aggression of defectors. Our research might provide valuable insights into further exploring the nature of cooperation in the real world. PMID:28112276

  13. The model of microblog message diffusion based on complex social network

    NASA Astrophysics Data System (ADS)

    Zhang, Wei; Bai, Shu-Ying; Jin, Rui

    2014-05-01

    Microblog is a micromessage communication network in which users are the nodes and the followship between users are the edges. Sina Weibo is a typical case of these microblog service websites. As the enormous scale of nodes and complex links in the network, we choose a sample network crawled in Sina Weibo as the base of empirical analysis. The study starts with the analysis of its topological features, and brings in epidemiological SEIR model to explore the mode of message spreading throughout the microblog network. It is found that the network is obvious small-world and scale-free, which made it succeed in transferring messages and failed in resisting negative influence. In addition, the paper focuses on the rich nodes as they constitute a typical feature of Sina Weibo. It is also found that whether the message starts with a rich node will not account for its final coverage. Actually, the rich nodes always play the role of pivotal intermediaries who speed up the spreading and make the message known by much more people.

  14. New Markov-Shannon Entropy models to assess connectivity quality in complex networks: from molecular to cellular pathway, Parasite-Host, Neural, Industry, and Legal-Social networks.

    PubMed

    Riera-Fernández, Pablo; Munteanu, Cristian R; Escobar, Manuel; Prado-Prado, Francisco; Martín-Romalde, Raquel; Pereira, David; Villalba, Karen; Duardo-Sánchez, Aliuska; González-Díaz, Humberto

    2012-01-21

    Graph and Complex Network theory is expanding its application to different levels of matter organization such as molecular, biological, technological, and social networks. A network is a set of items, usually called nodes, with connections between them, which are called links or edges. There are many different experimental and/or theoretical methods to assign node-node links depending on the type of network we want to construct. Unfortunately, the use of a method for experimental reevaluation of the entire network is very expensive in terms of time and resources; thus the development of cheaper theoretical methods is of major importance. In addition, different methods to link nodes in the same type of network are not totally accurate in such a way that they do not always coincide. In this sense, the development of computational methods useful to evaluate connectivity quality in complex networks (a posteriori of network assemble) is a goal of major interest. In this work, we report for the first time a new method to calculate numerical quality scores S(L(ij)) for network links L(ij) (connectivity) based on the Markov-Shannon Entropy indices of order k-th (θ(k)) for network nodes. The algorithm may be summarized as follows: (i) first, the θ(k)(j) values are calculated for all j-th nodes in a complex network already constructed; (ii) A Linear Discriminant Analysis (LDA) is used to seek a linear equation that discriminates connected or linked (L(ij)=1) pairs of nodes experimentally confirmed from non-linked ones (L(ij)=0); (iii) the new model is validated with external series of pairs of nodes; (iv) the equation obtained is used to re-evaluate the connectivity quality of the network, connecting/disconnecting nodes based on the quality scores calculated with the new connectivity function. This method was used to study different types of large networks. The linear models obtained produced the following results in terms of overall accuracy for network reconstruction

  15. Modeling the effects of social impact on epidemic spreading in complex networks

    NASA Astrophysics Data System (ADS)

    Ni, Shunjiang; Weng, Wenguo; Zhang, Hui

    2011-11-01

    We investigate by mean-field analysis and extensive simulations the effects of social impact on epidemic spreading in various typical networks with two types of nodes: active nodes and passive nodes, of which the behavior patterns are modeled according to the social impact theory. In this study, nodes are not only the media to spread the virus, but also disseminate their opinions on the virus-whether there is a need for certain self-protection measures to be taken to reduce the risk of being infected. Our results indicate that the interaction between epidemic spreading and opinion dynamics can have significant influences on the spreading of infectious diseases and related applications, such as the implementation of prevention and control measures against the infectious diseases.

  16. Security of Complex Networks

    DTIC Science & Technology

    2010-02-18

    social network (DS), (8) network of American football games among colleges (AFC), (9) social network of friendships of a karate club (FKC), (10...Ax2 = 12 book karate football ’•■ electronic circuit dolphins a C. Elegans 102 Figure 8: For Universal scaling law for six real-world

  17. A novel mammalian social structure in Indo-Pacific bottlenose dolphins (Tursiops sp.): complex male alliances in an open social network

    PubMed Central

    Randić, Srđan; Connor, Richard C.; Sherwin, William B.; Krützen, Michael

    2012-01-01

    Terrestrial mammals with differentiated social relationships live in ‘semi-closed groups’ that occasionally accept new members emigrating from other groups. Bottlenose dolphins (Tursiops sp.) in Shark Bay, Western Australia, exhibit a fission–fusion grouping pattern with strongly differentiated relationships, including nested male alliances. Previous studies failed to detect a group membership ‘boundary’, suggesting that the dolphins live in an open social network. However, two alternative hypotheses have not been excluded. The community defence model posits that the dolphins live in a large semi-closed ‘chimpanzee-like’ community defended by males and predicts that a dominant alliance(s) will range over the entire community range. The mating season defence model predicts that alliances will defend mating-season territories or sets of females. Here, both models are tested and rejected: no alliances ranged over the entire community range and alliances showed extensive overlap in mating season ranges and consorted females. The Shark Bay dolphins, therefore, present a combination of traits that is unique among mammals: complex male alliances in an open social network. The open social network of dolphins is linked to their relatively low costs of locomotion. This reveals a surprising and previously unrecognized convergence between adaptations reducing travel costs and complex intergroup–alliance relationships in dolphins, elephants and humans. PMID:22456886

  18. A novel mammalian social structure in Indo-Pacific bottlenose dolphins (Tursiops sp.): complex male alliances in an open social network.

    PubMed

    Randić, Srđan; Connor, Richard C; Sherwin, William B; Krützen, Michael

    2012-08-07

    Terrestrial mammals with differentiated social relationships live in 'semi-closed groups' that occasionally accept new members emigrating from other groups. Bottlenose dolphins (Tursiops sp.) in Shark Bay, Western Australia, exhibit a fission-fusion grouping pattern with strongly differentiated relationships, including nested male alliances. Previous studies failed to detect a group membership 'boundary', suggesting that the dolphins live in an open social network. However, two alternative hypotheses have not been excluded. The community defence model posits that the dolphins live in a large semi-closed 'chimpanzee-like' community defended by males and predicts that a dominant alliance(s) will range over the entire community range. The mating season defence model predicts that alliances will defend mating-season territories or sets of females. Here, both models are tested and rejected: no alliances ranged over the entire community range and alliances showed extensive overlap in mating season ranges and consorted females. The Shark Bay dolphins, therefore, present a combination of traits that is unique among mammals: complex male alliances in an open social network. The open social network of dolphins is linked to their relatively low costs of locomotion. This reveals a surprising and previously unrecognized convergence between adaptations reducing travel costs and complex intergroup-alliance relationships in dolphins, elephants and humans.

  19. Language Networks as Complex Systems

    ERIC Educational Resources Information Center

    Lee, Max Kueiming; Ou, Sheue-Jen

    2008-01-01

    Starting in the late eighties, with a growing discontent with analytical methods in science and the growing power of computers, researchers began to study complex systems such as living organisms, evolution of genes, biological systems, brain neural networks, epidemics, ecology, economy, social networks, etc. In the early nineties, the research…

  20. Professional social networking.

    PubMed

    Rowley, Robert D

    2014-12-01

    We review the current state of social communication between healthcare professionals, the role of consumer social networking, and some emerging technologies to address the gaps. In particular, the review covers (1) the current state of loose social networking for continuing medical education (CME) and other broadcast information dissemination; (2) social networking for business promotion; (3) social networking for peer collaboration, including simple communication as well as more robust data-centered collaboration around patient care; and (4) engaging patients on social platforms, including integrating consumer-originated data into the mix of healthcare data. We will see how, as the nature of healthcare delivery moves from the institution-centric way of tradition to a more social and networked ambulatory pattern that we see emerging today, the nature of health IT has also moved from enterprise-centric systems to more socially networked, cloud-based options.

  1. Unravelling the size distribution of social groups with information theory in complex networks

    NASA Astrophysics Data System (ADS)

    Hernando, A.; Villuendas, D.; Vesperinas, C.; Abad, M.; Plastino, A.

    2010-07-01

    The minimization of Fisher’s information (MFI) approach of Frieden et al. [Phys. Rev. E 60, 48 (1999)] is applied to the study of size distributions in social groups on the basis of a recently established analogy between scale invariant systems and classical gases [Phys. A 389, 490 (2010)]. Going beyond the ideal gas scenario is seen to be tantamount to simulating the interactions taking place, for a competitive cluster growth process, in a scale-free ideal network - a non-correlated network with a connection-degree’s distribution that mimics the scale-free ideal gas density distribution. We use a scaling rule that allows one to classify the final cluster-size distributions using only one parameter that we call the competitiveness, which can be seen as a measure of the strength of the interactions. We find that both empirical city-size distributions and electoral results can be thus reproduced and classified according to this competitiveness-parameter, that also allow us to infer the maximum number of stable social relationships that one person can maintain, known as the Dunbar number, together with its standard deviation. We discuss the importance of this number in connection with the empirical phenomenon known as “six-degrees of separation”. Finally, we show that scaled city-size distributions of large countries follow, in general, the same universal distribution.

  2. Weak Links: Stabilizers of Complex Systems from Proteins to Social Networks

    NASA Astrophysics Data System (ADS)

    Csermely, Peter

    Why do women stabilize our societies? Why can we enjoy and understand Shakespeare? Why are fruitflies uniform? Why do omnivorous eating habits aid our survival? Why is Mona Lisa's smile beautiful? -- Is there any answer to these questions? This book shows that the statement: "weak links stabilize complex systems" holds the answers to all of the surprising questions above. The author (recipientof several distinguished science communication prizes) uses weak (low affinity, low probability) interactions as a thread to introduce a vast varietyof networks from proteins to ecosystems.

  3. Social media networking: blogging.

    PubMed

    Schneider, Andrew; Jackson, Rem; Baum, Neil

    2010-01-01

    Social media networking is not your teenager's social media. It is a powerful tool that will change the way you communicate with your patients. This article will review the impact of social media and how social media can be a valuable tool for your medical practice. This is the first of a three-part article on social media and will discuss the use of blogging for medical practices.

  4. Interpreting participatory Fuzzy Cognitive Maps as complex networks in the social-ecological systems of the Amazonian forests

    NASA Astrophysics Data System (ADS)

    Varela, Consuelo; Tarquis, Ana M.; Blanco-Gutiérrez, Irene; Estebe, Paloma; Toledo, Marisol; Martorano, Lucieta

    2015-04-01

    Social-ecological systems are linked complex systems that represent interconnected human and biophysical processes evolving and adapting across temporal and spatial scales. In the real world, social-ecological systems pose substantial challenges for modeling. In this regard, Fuzzy Cognitive Maps (FCMs) have proven to be a useful method for capturing the functioning of this type of systems. FCMs are a semi-quantitative type of cognitive map that represent a system composed of relevant factors and weighted links showing the strength and direction of cause-effects relationships among factors. Therefore, FCMs can be interpreted as complex system structures or complex networks. In this sense, recent research has applied complex network concepts for the analysis of FCMs that represent social-ecological systems. Key to FCM the tool is its potential to allow feedback loops and to include stakeholder knowledge in the construction of the tool. Also, previous research has demonstrated their potential to represent system dynamics and simulate the effects of changes in the system, such as policy interventions. For illustrating this analysis, we have developed a series of participatory FCM for the study of the ecological and human systems related to biodiversity conservation in two case studies of the Amazonian region, the Bolivia lowlands of Guarayos and the Brazil Tapajos National forest. The research is carried out in the context of the EU project ROBIN1 and it is based on the development of a series of stakeholder workshops to analyze the current state of the socio-ecological environment in the Amazonian forest, reflecting conflicts and challenges for biodiversity conservation and human development. Stakeholders included all relevant actors in the local case studies, namely farmers, environmental groups, producer organizations, local and provincial authorities and scientists. In both case studies we illustrate the use of complex networks concepts, such as the adjacency

  5. "Conjectural" links in complex networks

    NASA Astrophysics Data System (ADS)

    Snarskii, A. A.; Zorinets, D. I.; Lande, D. V.

    2016-11-01

    This paper introduces the concept of Conjectural Link for Complex Networks, in particular, social networks. Conjectural Link we understand as an implicit link, not available in the network, but supposed to be present, based on the characteristics of its topology. It is possible, for example, when in the formal description of the network some connections are skipped due to errors, deliberately hidden or withdrawn (e.g. in the case of partial destruction of the network). Introduced a parameter that allows ranking the Conjectural Link. The more this parameter - the more likely that this connection should be present in the network. This paper presents a method of recovery of partially destroyed Complex Networks using Conjectural Links finding. Presented two methods of finding the node pairs that are not linked directly to one another, but have a great possibility of Conjectural Link communication among themselves: a method based on the determination of the resistance between two nodes, and method based on the computation of the lengths of routes between two nodes. Several examples of real networks are reviewed and performed a comparison to know network links prediction methods, not intended to find the missing links in already formed networks.

  6. Oscillations of complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Xingang; Lai, Ying-Cheng; Lai, Choy Heng

    2006-12-01

    A complex network processing information or physical flows is usually characterized by a number of macroscopic quantities such as the diameter and the betweenness centrality. An issue of significant theoretical and practical interest is how such quantities respond to sudden changes caused by attacks or disturbances in recoverable networks, i.e., functions of the affected nodes are only temporarily disabled or partially limited. By introducing a model to address this issue, we find that, for a finite-capacity network, perturbations can cause the network to oscillate persistently in the sense that the characterizing quantities vary periodically or randomly with time. We provide a theoretical estimate of the critical capacity-parameter value for the onset of the network oscillation. The finding is expected to have broad implications as it suggests that complex networks may be structurally highly dynamic.

  7. [Social networks and medicine].

    PubMed

    Bastardot, F; Vollenweider, P; Marques-Vidal, P

    2015-11-04

    Social networks (social media or #SoMe) have entered medical practice within the last few years. These new media--like Twitter or Skype--enrich interactions among physicians (telemedicine), among physicians and patients (virtual consultations) and change the way of teaching medicine. They also entail new ethical, deontological and legal issues: the extension of the consultation area beyond the medical office and the access of information by third parties were recently debated. We develop here a review of some social networks with their characteristics, applications for medicine and limitations, and we offer some recommendations of good practice.

  8. Knowledge Discovery from Growing Social Networks

    DTIC Science & Technology

    2009-12-24

    undesirable things by blocking a limited number of links in a network. 2) An effective visualization method for understanding a complex network, in...representative information diffusion models. 4) An effective method for ranking influential nodes in complex social networks by estimating diffusion...trends, and market opportunities. We can now obtain digital traces of human social interaction with time stamp information in a wide variety of

  9. Online Advertising in Social Networks

    NASA Astrophysics Data System (ADS)

    Bagherjeiran, Abraham; Bhatt, Rushi P.; Parekh, Rajesh; Chaoji, Vineet

    Online social networks offer opportunities to analyze user behavior and social connectivity and leverage resulting insights for effective online advertising. This chapter focuses on the role of social network information in online display advertising.

  10. Online social support networks.

    PubMed

    Mehta, Neil; Atreja, Ashish

    2015-04-01

    Peer support groups have a long history and have been shown to improve health outcomes. With the increasing familiarity with online social networks like Facebook and ubiquitous access to the Internet, online social support networks are becoming popular. While studies have shown the benefit of these networks in providing emotional support or meeting informational needs, robust data on improving outcomes such as a decrease in health services utilization or reduction in adverse outcomes is lacking. These networks also pose unique challenges in the areas of patient privacy, funding models, quality of content, and research agendas. Addressing these concerns while creating patient-centred, patient-powered online support networks will help leverage these platforms to complement traditional healthcare delivery models in the current environment of value-based care.

  11. Dynamic and interacting complex networks

    NASA Astrophysics Data System (ADS)

    Dickison, Mark E.

    This thesis employs methods of statistical mechanics and numerical simulations to study some aspects of dynamic and interacting complex networks. The mapping of various social and physical phenomena to complex networks has been a rich field in the past few decades. Subjects as broad as petroleum engineering, scientific collaborations, and the structure of the internet have all been analyzed in a network physics context, with useful and universal results. In the first chapter we introduce basic concepts in networks, including the two types of network configurations that are studied and the statistical physics and epidemiological models that form the framework of the network research, as well as covering various previously-derived results in network theory that are used in the work in the following chapters. In the second chapter we introduce a model for dynamic networks, where the links or the strengths of the links change over time. We solve the model by mapping dynamic networks to the problem of directed percolation, where the direction corresponds to the time evolution of the network. We show that the dynamic network undergoes a percolation phase transition at a critical concentration pc, that decreases with the rate r at which the network links are changed. The behavior near criticality is universal and independent of r. We find that for dynamic random networks fundamental laws are changed: i) The size of the giant component at criticality scales with the network size N for all values of r, rather than as N2/3 in static network, ii) In the presence of a broad distribution of disorder, the optimal path length between two nodes in a dynamic network scales as N1/2, compared to N1/3 in a static network. The third chapter consists of a study of the effect of quarantine on the propagation of epidemics on an adaptive network of social contacts. For this purpose, we analyze the susceptible-infected-recovered model in the presence of quarantine, where susceptible

  12. The Social Network Classroom

    NASA Astrophysics Data System (ADS)

    Bunus, Peter

    Online social networking is an important part in the everyday life of college students. Despite the increasing popularity of online social networking among students and faculty members, its educational benefits are largely untested. This paper presents our experience in using social networking applications and video content distribution websites as a complement of traditional classroom education. In particular, the solution has been based on effective adaptation, extension and integration of Facebook, Twitter, Blogger YouTube and iTunes services for delivering educational material to students on mobile platforms like iPods and 3 rd generation mobile phones. The goals of the proposed educational platform, described in this paper, are to make the learning experience more engaging, to encourage collaborative work and knowledge sharing among students, and to provide an interactive platform for the educators to reach students and deliver lecture material in a totally new way.

  13. Compressively sensed complex networks.

    SciTech Connect

    Dunlavy, Daniel M.; Ray, Jaideep; Pinar, Ali

    2010-07-01

    The aim of this project is to develop low dimension parametric (deterministic) models of complex networks, to use compressive sensing (CS) and multiscale analysis to do so and to exploit the structure of complex networks (some are self-similar under coarsening). CS provides a new way of sampling and reconstructing networks. The approach is based on multiresolution decomposition of the adjacency matrix and its efficient sampling. It requires preprocessing of the adjacency matrix to make it 'blocky' which is the biggest (combinatorial) algorithm challenge. Current CS reconstruction algorithm makes no use of the structure of a graph, its very general (and so not very efficient/customized). Other model-based CS techniques exist, but not yet adapted to networks. Obvious starting point for future work is to increase the efficiency of reconstruction.

  14. Social networking and adolescents.

    PubMed

    Fuld, Gilbert L

    2009-04-01

    Online social networking is a 21st century innovation increasingly embraced by today's young people. It provides new opportunities for communication that expand an adolescent's world. Yet adults, often suspicious of new trends and technologies initially embraced by youth, often see these new environments as perilous places to visit. These fears have been accentuated by media hype, especially about sexual predators. How dangerous are they? Because the rush to go on these sites is a new phenomenon, research is as yet scant. This review explores current beliefs and knowledge about the dangers of social networking sites.

  15. Social Network Infiltration

    NASA Astrophysics Data System (ADS)

    Plait, Philip

    2008-05-01

    Social networks are websites (or software that distributes media online) where users can distribute content to either a list of friends on that site or to anyone who surfs onto their page, and where those friends can interact and discuss the content. By linking to friends online, the users’ personal content (pictures, songs, favorite movies, diaries, websites, and so on) is dynamically distributed, and can "become viral", that is, get spread rapidly as more people see it and spread it themselves. Social networks are immensely popular around the planet, especially with younger users. The biggest social networks are Facebook and MySpace; an IYA2009 user already exists on Facebook, and one will be created for MySpace (in fact, several NASA satellites such as GLAST and Swift already have successful MySpace pages). Twitter is another network where data distribution is more limited; it is more like a mini-blog, but is very popular. IYA2009 already has a Twitter page, and will be updated more often with relevant information. In this talk I will review the existing social networks, show people how and why they are useful, and give them the tools they need to contribute meaningfully to IYA's online reach.

  16. Epidemic processes in complex networks

    NASA Astrophysics Data System (ADS)

    Pastor-Satorras, Romualdo; Castellano, Claudio; Van Mieghem, Piet; Vespignani, Alessandro

    2015-07-01

    In recent years the research community has accumulated overwhelming evidence for the emergence of complex and heterogeneous connectivity patterns in a wide range of biological and sociotechnical systems. The complex properties of real-world networks have a profound impact on the behavior of equilibrium and nonequilibrium phenomena occurring in various systems, and the study of epidemic spreading is central to our understanding of the unfolding of dynamical processes in complex networks. The theoretical analysis of epidemic spreading in heterogeneous networks requires the development of novel analytical frameworks, and it has produced results of conceptual and practical relevance. A coherent and comprehensive review of the vast research activity concerning epidemic processes is presented, detailing the successful theoretical approaches as well as making their limits and assumptions clear. Physicists, mathematicians, epidemiologists, computer, and social scientists share a common interest in studying epidemic spreading and rely on similar models for the description of the diffusion of pathogens, knowledge, and innovation. For this reason, while focusing on the main results and the paradigmatic models in infectious disease modeling, the major results concerning generalized social contagion processes are also presented. Finally, the research activity at the forefront in the study of epidemic spreading in coevolving, coupled, and time-varying networks is reported.

  17. Complex Semantic Networks

    NASA Astrophysics Data System (ADS)

    Teixeira, G. M.; Aguiar, M. S. F.; Carvalho, C. F.; Dantas, D. R.; Cunha, M. V.; Morais, J. H. M.; Pereira, H. B. B.; Miranda, J. G. V.

    Verbal language is a dynamic mental process. Ideas emerge by means of the selection of words from subjective and individual characteristics throughout the oral discourse. The goal of this work is to characterize the complex network of word associations that emerge from an oral discourse from a discourse topic. Because of that, concepts of associative incidence and fidelity have been elaborated and represented the probability of occurrence of pairs of words in the same sentence in the whole oral discourse. Semantic network of words associations were constructed, where the words are represented as nodes and the edges are created when the incidence-fidelity index between pairs of words exceeds a numerical limit (0.001). Twelve oral discourses were studied. The networks generated from these oral discourses present a typical behavior of complex networks and their indices were calculated and their topologies characterized. The indices of these networks obtained from each incidence-fidelity limit exhibit a critical value in which the semantic network has maximum conceptual information and minimum residual associations. Semantic networks generated by this incidence-fidelity limit depict a pattern of hierarchical classes that represent the different contexts used in the oral discourse.

  18. Pleiotropy constrains the evolution of protein but not regulatory sequences in a transcription regulatory network influencing complex social behaviors

    PubMed Central

    Molodtsova, Daria; Harpur, Brock A.; Kent, Clement F.; Seevananthan, Kajendra; Zayed, Amro

    2014-01-01

    It is increasingly apparent that genes and networks that influence complex behavior are evolutionary conserved, which is paradoxical considering that behavior is labile over evolutionary timescales. How does adaptive change in behavior arise if behavior is controlled by conserved, pleiotropic, and likely evolutionary constrained genes? Pleiotropy and connectedness are known to constrain the general rate of protein evolution, prompting some to suggest that the evolution of complex traits, including behavior, is fuelled by regulatory sequence evolution. However, we seldom have data on the strength of selection on mutations in coding and regulatory sequences, and this hinders our ability to study how pleiotropy influences coding and regulatory sequence evolution. Here we use population genomics to estimate the strength of selection on coding and regulatory mutations for a transcriptional regulatory network that influences complex behavior of honey bees. We found that replacement mutations in highly connected transcription factors and target genes experience significantly stronger negative selection relative to weakly connected transcription factors and targets. Adaptively evolving proteins were significantly more likely to reside at the periphery of the regulatory network, while proteins with signs of negative selection were near the core of the network. Interestingly, connectedness and network structure had minimal influence on the strength of selection on putative regulatory sequences for both transcription factors and their targets. Our study indicates that adaptive evolution of complex behavior can arise because of positive selection on protein-coding mutations in peripheral genes, and on regulatory sequence mutations in both transcription factors and their targets throughout the network. PMID:25566318

  19. Social Networking Technologies

    DTIC Science & Technology

    2015-01-01

    pushed the communist party from power in Moldova in 2009. Many have also argued that social networking technology played a vital role in the Arab Spring...Constant Connection. New York, NY: HarperCollins, 2015. Cross-References: Arab Spring Barack Obama Facebook Katz v. United States MySpace

  20. Phagebook: The Social Network.

    PubMed

    Hynes, Alexander P; Moineau, Sylvain

    2017-03-16

    Much like social networks are used to connect with friends or relatives, bacteria communicate with relatives through quorum sensing. Viruses, though, were thought to be asocial-until now. Erez et al. (2017) reveal that viruses are also sharing information with relatives.

  1. Animal welfare: a social networks perspective.

    PubMed

    Kleinhappel, Tanja K; John, Elizabeth A; Pike, Thomas W; Wilkinson, Anna; Burman, Oliver H P

    2016-01-01

    Social network theory provides a useful tool to study complex social relationships in animals. The possibility to look beyond dyadic interactions by considering whole networks of social relationships allows researchers the opportunity to study social groups in more natural ways. As such, network-based analyses provide an informative way to investigate the factors influencing the social environment of group-living animals, and so has direct application to animal welfare. For example, animal groups in captivity are frequently disrupted by separations, reintroductions and/or mixing with unfamiliar individuals and this can lead to social stress and associated aggression. Social network analysis ofanimal groups can help identify the underlying causes of these socially-derived animal welfare concerns. In this review we discuss how this approach can be applied, and how it could be used to identify potential interventions and solutions in the area of animal welfare.

  2. Promoting Social Network Awareness: A Social Network Monitoring System

    ERIC Educational Resources Information Center

    Cadima, Rita; Ferreira, Carlos; Monguet, Josep; Ojeda, Jordi; Fernandez, Joaquin

    2010-01-01

    To increase communication and collaboration opportunities, members of a community must be aware of the social networks that exist within that community. This paper describes a social network monitoring system--the KIWI system--that enables users to register their interactions and visualize their social networks. The system was implemented in a…

  3. Contagion on complex networks with persuasion.

    PubMed

    Huang, Wei-Min; Zhang, Li-Jie; Xu, Xin-Jian; Fu, Xinchu

    2016-03-31

    The threshold model has been widely adopted as a classic model for studying contagion processes on social networks. We consider asymmetric individual interactions in social networks and introduce a persuasion mechanism into the threshold model. Specifically, we study a combination of adoption and persuasion in cascading processes on complex networks. It is found that with the introduction of the persuasion mechanism, the system may become more vulnerable to global cascades, and the effects of persuasion tend to be more significant in heterogeneous networks than those in homogeneous networks: a comparison between heterogeneous and homogeneous networks shows that under weak persuasion, heterogeneous networks tend to be more robust against random shocks than homogeneous networks; whereas under strong persuasion, homogeneous networks are more stable. Finally, we study the effects of adoption and persuasion threshold heterogeneity on systemic stability. Though both heterogeneities give rise to global cascades, the adoption heterogeneity has an overwhelmingly stronger impact than the persuasion heterogeneity when the network connectivity is sufficiently dense.

  4. Contagion on complex networks with persuasion

    NASA Astrophysics Data System (ADS)

    Huang, Wei-Min; Zhang, Li-Jie; Xu, Xin-Jian; Fu, Xinchu

    2016-03-01

    The threshold model has been widely adopted as a classic model for studying contagion processes on social networks. We consider asymmetric individual interactions in social networks and introduce a persuasion mechanism into the threshold model. Specifically, we study a combination of adoption and persuasion in cascading processes on complex networks. It is found that with the introduction of the persuasion mechanism, the system may become more vulnerable to global cascades, and the effects of persuasion tend to be more significant in heterogeneous networks than those in homogeneous networks: a comparison between heterogeneous and homogeneous networks shows that under weak persuasion, heterogeneous networks tend to be more robust against random shocks than homogeneous networks; whereas under strong persuasion, homogeneous networks are more stable. Finally, we study the effects of adoption and persuasion threshold heterogeneity on systemic stability. Though both heterogeneities give rise to global cascades, the adoption heterogeneity has an overwhelmingly stronger impact than the persuasion heterogeneity when the network connectivity is sufficiently dense.

  5. Online social networking for radiology.

    PubMed

    Auffermann, William F; Chetlen, Alison L; Colucci, Andrew T; DeQuesada, Ivan M; Grajo, Joseph R; Heller, Matthew T; Nowitzki, Kristina M; Sherry, Steven J; Tillack, Allison A

    2015-01-01

    Online social networking services have changed the way we interact as a society and offer many opportunities to improve the way we practice radiology and medicine in general. This article begins with an introduction to social networking. Next, the latest advances in online social networking are reviewed, and areas where radiologists and clinicians may benefit from these new tools are discussed. This article concludes with several steps that the interested reader can take to become more involved in online social networking.

  6. Complex Networks and Socioeconomic Applications

    NASA Astrophysics Data System (ADS)

    Almendral, Juan A.; López, Luis; Mendes, Jose F.; Sanjuán, Miguel A. F.

    2003-04-01

    The study and characterization of complex systems is a fruitful research area nowadays. Special attention has been paid recently to complex networks, where graph and network analysis plays an important role since they reduce a given system to a simpler problem. Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders because the global influence each actor has within the network is completely determined by the hierarchical level occupied. The effect on the efficiency caused by a change in a traditional hierarchical topology is also analyzed. In particular, by introducing the possibility of communication on the same level of the hierarchy.

  7. Sociality influences cultural complexity

    PubMed Central

    Muthukrishna, Michael; Shulman, Ben W.; Vasilescu, Vlad; Henrich, Joseph

    2014-01-01

    Archaeological and ethnohistorical evidence suggests a link between a population's size and structure, and the diversity or sophistication of its toolkits or technologies. Addressing these patterns, several evolutionary models predict that both the size and social interconnectedness of populations can contribute to the complexity of its cultural repertoire. Some models also predict that a sudden loss of sociality or of population will result in subsequent losses of useful skills/technologies. Here, we test these predictions with two experiments that permit learners to access either one or five models (teachers). Experiment 1 demonstrates that naive participants who could observe five models, integrate this information and generate increasingly effective skills (using an image editing tool) over 10 laboratory generations, whereas those with access to only one model show no improvement. Experiment 2, which began with a generation of trained experts, shows how learners with access to only one model lose skills (in knot-tying) more rapidly than those with access to five models. In the final generation of both experiments, all participants with access to five models demonstrate superior skills to those with access to only one model. These results support theoretical predictions linking sociality to cumulative cultural evolution. PMID:24225461

  8. Collaboration in social networks

    PubMed Central

    Dall’Asta, Luca; Marsili, Matteo; Pin, Paolo

    2012-01-01

    The very notion of social network implies that linked individuals interact repeatedly with each other. This notion allows them not only to learn successful strategies and adapt to them, but also to condition their own behavior on the behavior of others, in a strategic forward looking manner. Game theory of repeated games shows that these circumstances are conducive to the emergence of collaboration in simple games of two players. We investigate the extension of this concept to the case where players are engaged in a local contribution game and show that rationality and credibility of threats identify a class of Nash equilibria—that we call “collaborative equilibria”—that have a precise interpretation in terms of subgraphs of the social network. For large network games, the number of such equilibria is exponentially large in the number of players. When incentives to defect are small, equilibria are supported by local structures whereas when incentives exceed a threshold they acquire a nonlocal nature, which requires a “critical mass” of more than a given fraction of the players to collaborate. Therefore, when incentives are high, an individual deviation typically causes the collapse of collaboration across the whole system. At the same time, higher incentives to defect typically support equilibria with a higher density of collaborators. The resulting picture conforms with several results in sociology and in the experimental literature on game theory, such as the prevalence of collaboration in denser groups and in the structural hubs of sparse networks. PMID:22383559

  9. Modeling Social Network Topologies in Elementary Schools

    PubMed Central

    Huerta-Quintanilla, Rodrigo; Canto-Lugo, Efrain; Viga-de Alva, Dolores

    2013-01-01

    Complex networks are used to describe interactions in many real world systems, including economic, biological and social systems. An analysis was done of inter-student friendship, enmity and kinship relationships at three elementary schools by building social networks of these relationships and studying their properties. Friendship network measurements were similar between schools and produced a Poisson topology with a high clustering index. Enmity network measurements were also similar between schools and produced a power law topology. Spatial confinement and the sense of belonging to a social group played vital roles in shaping these networks. Two models were developed which generate complex friendship and enmity networks that reproduce the properties observed at the three studied elementary schools. PMID:23408976

  10. Modeling social network topologies in elementary schools.

    PubMed

    Huerta-Quintanilla, Rodrigo; Canto-Lugo, Efrain; Viga-de Alva, Dolores

    2013-01-01

    Complex networks are used to describe interactions in many real world systems, including economic, biological and social systems. An analysis was done of inter-student friendship, enmity and kinship relationships at three elementary schools by building social networks of these relationships and studying their properties. Friendship network measurements were similar between schools and produced a Poisson topology with a high clustering index. Enmity network measurements were also similar between schools and produced a power law topology. Spatial confinement and the sense of belonging to a social group played vital roles in shaping these networks. Two models were developed which generate complex friendship and enmity networks that reproduce the properties observed at the three studied elementary schools.

  11. Higher-order organization of complex networks

    PubMed Central

    Benson, Austin R.; Gleich, David F.; Leskovec, Jure

    2016-01-01

    Networks are a fundamental tool for understanding and modeling complex systems in physics, biology, neuroscience, engineering, and social science. Many networks are known to exhibit rich, lower-order connectivity patterns that can be captured at the level of individual nodes and edges. However, higher-order organization of complex networks—at the level of small network subgraphs—remains largely unknown. Here, we develop a generalized framework for clustering networks on the basis of higher-order connectivity patterns. This framework provides mathematical guarantees on the optimality of obtained clusters and scales to networks with billions of edges. The framework reveals higher-order organization in a number of networks, including information propagation units in neuronal networks and hub structure in transportation networks. Results show that networks exhibit rich higher-order organizational structures that are exposed by clustering based on higher-order connectivity patterns. PMID:27387949

  12. Interests diffusion in social networks

    NASA Astrophysics Data System (ADS)

    D'Agostino, Gregorio; D'Antonio, Fulvio; De Nicola, Antonio; Tucci, Salvatore

    2015-10-01

    We provide a model for diffusion of interests in Social Networks (SNs). We demonstrate that the topology of the SN plays a crucial role in the dynamics of the individual interests. Understanding cultural phenomena on SNs and exploiting the implicit knowledge about their members is attracting the interest of different research communities both from the academic and the business side. The community of complexity science is devoting significant efforts to define laws, models, and theories, which, based on acquired knowledge, are able to predict future observations (e.g. success of a product). In the mean time, the semantic web community aims at engineering a new generation of advanced services by defining constructs, models and methods, adding a semantic layer to SNs. In this context, a leapfrog is expected to come from a hybrid approach merging the disciplines above. Along this line, this work focuses on the propagation of individual interests in social networks. The proposed framework consists of the following main components: a method to gather information about the members of the social networks; methods to perform some semantic analysis of the Domain of Interest; a procedure to infer members' interests; and an interests evolution theory to predict how the interests propagate in the network. As a result, one achieves an analytic tool to measure individual features, such as members' susceptibilities and authorities. Although the approach applies to any type of social network, here it is has been tested against the computer science research community. The DBLP (Digital Bibliography and Library Project) database has been elected as test-case since it provides the most comprehensive list of scientific production in this field.

  13. Applications of Social Network Analysis

    NASA Astrophysics Data System (ADS)

    Thilagam, P. Santhi

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

  14. Articulation points in complex networks

    PubMed Central

    Tian, Liang; Bashan, Amir; Shi, Da-Ning; Liu, Yang-Yu

    2017-01-01

    An articulation point in a network is a node whose removal disconnects the network. Those nodes play key roles in ensuring connectivity of many real-world networks, from infrastructure networks to protein interaction networks and terrorist communication networks. Despite their fundamental importance, a general framework of studying articulation points in complex networks is lacking. Here we develop analytical tools to study key issues pertinent to articulation points, such as the expected number of them and the network vulnerability against their removal, in an arbitrary complex network. We find that a greedy articulation point removal process provides us a different perspective on the organizational principles of complex networks. Moreover, this process results in a rich phase diagram with two fundamentally different types of percolation transitions. Our results shed light on the design of more resilient infrastructure networks and the effective destruction of terrorist communication networks. PMID:28139697

  15. Articulation points in complex networks

    NASA Astrophysics Data System (ADS)

    Tian, Liang; Bashan, Amir; Shi, Da-Ning; Liu, Yang-Yu

    2017-01-01

    An articulation point in a network is a node whose removal disconnects the network. Those nodes play key roles in ensuring connectivity of many real-world networks, from infrastructure networks to protein interaction networks and terrorist communication networks. Despite their fundamental importance, a general framework of studying articulation points in complex networks is lacking. Here we develop analytical tools to study key issues pertinent to articulation points, such as the expected number of them and the network vulnerability against their removal, in an arbitrary complex network. We find that a greedy articulation point removal process provides us a different perspective on the organizational principles of complex networks. Moreover, this process results in a rich phase diagram with two fundamentally different types of percolation transitions. Our results shed light on the design of more resilient infrastructure networks and the effective destruction of terrorist communication networks.

  16. Happiness is assortative in online social networks.

    PubMed

    Bollen, Johan; Gonçalves, Bruno; Ruan, Guangchen; Mao, Huina

    2011-01-01

    Online social networking communities may exhibit highly complex and adaptive collective behaviors. Since emotions play such an important role in human decision making, how online networks modulate human collective mood states has become a matter of considerable interest. In spite of the increasing societal importance of online social networks, it is unknown whether assortative mixing of psychological states takes place in situations where social ties are mediated solely by online networking services in the absence of physical contact. Here, we show that the general happiness, or subjective well-being (SWB), of Twitter users, as measured from a 6-month record of their individual tweets, is indeed assortative across the Twitter social network. Our results imply that online social networks may be equally subject to the social mechanisms that cause assortative mixing in real social networks and that such assortative mixing takes place at the level of SWB. Given the increasing prevalence of online social networks, their propensity to connect users with similar levels of SWB may be an important factor in how positive and negative sentiments are maintained and spread through human society. Future research may focus on how event-specific mood states can propagate and influence user behavior in "real life."

  17. Underage Children and Social Networking

    ERIC Educational Resources Information Center

    Weeden, Shalynn; Cooke, Bethany; McVey, Michael

    2013-01-01

    Despite minimum age requirements for joining popular social networking services such as Facebook, many students misrepresent their real ages and join as active participants in the networks. This descriptive study examines the use of social networking services (SNSs) by children under the age of 13. The researchers surveyed a sample of 199…

  18. Correlation dimension of complex networks.

    PubMed

    Lacasa, Lucas; Gómez-Gardeñes, Jesús

    2013-04-19

    We propose a new measure to characterize the dimension of complex networks based on the ergodic theory of dynamical systems. This measure is derived from the correlation sum of a trajectory generated by a random walker navigating the network, and extends the classical Grassberger-Procaccia algorithm to the context of complex networks. The method is validated with reliable results for both synthetic networks and real-world networks such as the world air-transportation network or urban networks, and provides a computationally fast way for estimating the dimensionality of networks which only relies on the local information provided by the walkers.

  19. Spreading dynamics in complex networks

    NASA Astrophysics Data System (ADS)

    Pei, Sen; Makse, Hernán A.

    2013-12-01

    Searching for influential spreaders in complex networks is an issue of great significance for applications across various domains, ranging from epidemic control, innovation diffusion, viral marketing, and social movement to idea propagation. In this paper, we first display some of the most important theoretical models that describe spreading processes, and then discuss the problem of locating both the individual and multiple influential spreaders respectively. Recent approaches in these two topics are presented. For the identification of privileged single spreaders, we summarize several widely used centralities, such as degree, betweenness centrality, PageRank, k-shell, etc. We investigate the empirical diffusion data in a large scale online social community—LiveJournal. With this extensive dataset, we find that various measures can convey very distinct information of nodes. Of all the users in the LiveJournal social network, only a small fraction of them are involved in spreading. For the spreading processes in LiveJournal, while degree can locate nodes participating in information diffusion with higher probability, k-shell is more effective in finding nodes with a large influence. Our results should provide useful information for designing efficient spreading strategies in reality.

  20. Controlling complex networks with conformity behavior

    NASA Astrophysics Data System (ADS)

    Wang, Xu-Wen; Nie, Sen; Wang, Wen-Xu; Wang, Bing-Hong

    2015-09-01

    Controlling complex networks accompanied by common conformity behavior is a fundamental problem in social and physical science. Conformity behavior that individuals tend to follow the majority in their neighborhood is common in human society and animal communities. Despite recent progress in understanding controllability of complex networks, the existent controllability theories cannot be directly applied to networks associated with conformity. Here we propose a simple model to incorporate conformity-based decision making into the evolution of a network system, which allows us to employ the exact controllability theory to explore the controllability of such systems. We offer rigorous theoretical results of controllability for representative regular networks. We also explore real networks in different fields and some typical model networks, finding some interesting results that are different from the predictions of structural and exact controllability theory in the absence of conformity. We finally present an example of steering a real social network to some target states to further validate our controllability theory and tools. Our work offers a more realistic understanding of network controllability with conformity behavior and can have potential applications in networked evolutionary games, opinion dynamics and many other complex networked systems.

  1. Fundamental structures of dynamic social networks.

    PubMed

    Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

    2016-09-06

    Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision.

  2. Fundamental structures of dynamic social networks

    PubMed Central

    Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

    2016-01-01

    Social systems are in a constant state of flux, with dynamics spanning from minute-by-minute changes to patterns present on the timescale of years. Accurate models of social dynamics are important for understanding the spreading of influence or diseases, formation of friendships, and the productivity of teams. Although there has been much progress on understanding complex networks over the past decade, little is known about the regularities governing the microdynamics of social networks. Here, we explore the dynamic social network of a densely-connected population of ∼1,000 individuals and their interactions in the network of real-world person-to-person proximity measured via Bluetooth, as well as their telecommunication networks, online social media contacts, geolocation, and demographic data. These high-resolution data allow us to observe social groups directly, rendering community detection unnecessary. Starting from 5-min time slices, we uncover dynamic social structures expressed on multiple timescales. On the hourly timescale, we find that gatherings are fluid, with members coming and going, but organized via a stable core of individuals. Each core represents a social context. Cores exhibit a pattern of recurring meetings across weeks and months, each with varying degrees of regularity. Taken together, these findings provide a powerful simplification of the social network, where cores represent fundamental structures expressed with strong temporal and spatial regularity. Using this framework, we explore the complex interplay between social and geospatial behavior, documenting how the formation of cores is preceded by coordination behavior in the communication networks and demonstrating that social behavior can be predicted with high precision. PMID:27555584

  3. A Sensemaking Approach to Visual Analytics of Attribute-Rich Social Networks

    ERIC Educational Resources Information Center

    Gou, Liang

    2012-01-01

    Social networks have become more complex, in particular considering the fact that elements in social networks are not only abstract topological nodes and links, but contain rich social attributes and reflecting diverse social relationships. For example, in a co-authorship social network in a scientific community, nodes in the social network, which…

  4. Graph distance for complex networks

    NASA Astrophysics Data System (ADS)

    Shimada, Yutaka; Hirata, Yoshito; Ikeguchi, Tohru; Aihara, Kazuyuki

    2016-10-01

    Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.

  5. Graph distance for complex networks

    PubMed Central

    Shimada, Yutaka; Hirata, Yoshito; Ikeguchi, Tohru; Aihara, Kazuyuki

    2016-01-01

    Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions. PMID:27725690

  6. Churn in Social Networks

    NASA Astrophysics Data System (ADS)

    Karnstedt, Marcel; Hennessy, Tara; Chan, Jeffrey; Basuchowdhuri, Partha; Hayes, Conor; Strufe, Thorsten

    In the past, churn has been identified as an issue across most industry sectors. In its most general sense it refers to the rate of loss of customers from a company's customer base. There is a simple reason for the attention churn attracts: churning customers mean a loss of revenue. Emerging from business spaces like telecommunications (telcom) and broadcast providers, where churn is a major issue, it is also regarded as a crucial problem in many other businesses, such as online games creators, but also online social networks and discussion sites. Companies aim at identifying the risk of churn in its early stages, as it is usually much cheaper to retain a customer than to try to win him or her back. If this risk can be accurately predicted, marketing departments can target customers efficiently with tailored incentives to prevent them from leaving.

  7. Measure of robustness for complex networks

    NASA Astrophysics Data System (ADS)

    Youssef, Mina Nabil

    Critical infrastructures are repeatedly attacked by external triggers causing tremendous amount of damages. Any infrastructure can be studied using the powerful theory of complex networks. A complex network is composed of extremely large number of different elements that exchange commodities providing significant services. The main functions of complex networks can be damaged by different types of attacks and failures that degrade the network performance. These attacks and failures are considered as disturbing dynamics, such as the spread of viruses in computer networks, the spread of epidemics in social networks, and the cascading failures in power grids. Depending on the network structure and the attack strength, every network differently suffers damages and performance degradation. Hence, quantifying the robustness of complex networks becomes an essential task. In this dissertation, new metrics are introduced to measure the robustness of technological and social networks with respect to the spread of epidemics, and the robustness of power grids with respect to cascading failures. First, we introduce a new metric called the Viral Conductance (VCSIS ) to assess the robustness of networks with respect to the spread of epidemics that are modeled through the susceptible/infected/susceptible (SIS) epidemic approach. In contrast to assessing the robustness of networks based on a classical metric, the epidemic threshold, the new metric integrates the fraction of infected nodes at steady state for all possible effective infection strengths. Through examples, VCSIS provides more insights about the robustness of networks than the epidemic threshold. In addition, both the paradoxical robustness of Barabasi-Albert preferential attachment networks and the effect of the topology on the steady state infection are studied, to show the importance of quantifying the robustness of networks. Second, a new metric VCSIR is introduced to assess the robustness of networks with respect

  8. A Social Networks in Education

    ERIC Educational Resources Information Center

    Klimova, Blanka; Poulova, Petra

    2015-01-01

    At present social networks are becoming important in all areas of human activities. They are simply part and parcel of everyday life. They are mostly used for advertising, but they have already found their way into education. The future potential of social networks is high as it can be seen from their statistics on a daily, monthly or yearly…

  9. Social Networking Goes to School

    ERIC Educational Resources Information Center

    Davis, Michelle R.

    2010-01-01

    Just a few years ago, social networking meant little more to educators than the headache of determining whether to penalize students for inappropriate activities captured on Facebook or MySpace. Now, teachers and students have an array of social-networking sites and tools--from Ning to VoiceThread and Second Life--to draw on for such serious uses…

  10. Information Processing in Social Insect Networks

    PubMed Central

    Waters, James S.; Fewell, Jennifer H.

    2012-01-01

    Investigating local-scale interactions within a network makes it possible to test hypotheses about the mechanisms of global network connectivity and to ask whether there are general rules underlying network function across systems. Here we use motif analysis to determine whether the interactions within social insect colonies resemble the patterns exhibited by other animal associations or if they exhibit characteristics of biological regulatory systems. Colonies exhibit a predominance of feed-forward interaction motifs, in contrast to the densely interconnected clique patterns that characterize human interaction and animal social networks. The regulatory motif signature supports the hypothesis that social insect colonies are shaped by selection for network patterns that integrate colony functionality at the group rather than individual level, and demonstrates the utility of this approach for analysis of selection effects on complex systems across biological levels of organization. PMID:22815740

  11. Exact controllability of complex networks

    PubMed Central

    Yuan, Zhengzhong; Zhao, Chen; Di, Zengru; Wang, Wen-Xu; Lai, Ying-Cheng

    2013-01-01

    Controlling complex networks is of paramount importance in science and engineering. Despite the recent development of structural controllability theory, we continue to lack a framework to control undirected complex networks, especially given link weights. Here we introduce an exact controllability paradigm based on the maximum multiplicity to identify the minimum set of driver nodes required to achieve full control of networks with arbitrary structures and link-weight distributions. The framework reproduces the structural controllability of directed networks characterized by structural matrices. We explore the controllability of a large number of real and model networks, finding that dense networks with identical weights are difficult to be controlled. An efficient and accurate tool is offered to assess the controllability of large sparse and dense networks. The exact controllability framework enables a comprehensive understanding of the impact of network properties on controllability, a fundamental problem towards our ultimate control of complex systems. PMID:24025746

  12. Complex networks analysis of language complexity

    NASA Astrophysics Data System (ADS)

    Amancio, Diego R.; Aluisio, Sandra M.; Oliveira, Osvaldo N., Jr.; Costa, Luciano da F.

    2012-12-01

    Methods from statistical physics, such as those involving complex networks, have been increasingly used in the quantitative analysis of linguistic phenomena. In this paper, we represented pieces of text with different levels of simplification in co-occurrence networks and found that topological regularity correlated negatively with textual complexity. Furthermore, in less complex texts the distance between concepts, represented as nodes, tended to decrease. The complex networks metrics were treated with multivariate pattern recognition techniques, which allowed us to distinguish between original texts and their simplified versions. For each original text, two simplified versions were generated manually with increasing number of simplification operations. As expected, distinction was easier for the strongly simplified versions, where the most relevant metrics were node strength, shortest paths and diversity. Also, the discrimination of complex texts was improved with higher hierarchical network metrics, thus pointing to the usefulness of considering wider contexts around the concepts. Though the accuracy rate in the distinction was not as high as in methods using deep linguistic knowledge, the complex network approach is still useful for a rapid screening of texts whenever assessing complexity is essential to guarantee accessibility to readers with limited reading ability.

  13. The regulation of social recognition, social communication and aggression: vasopressin in the social behavior neural network.

    PubMed

    Albers, H Elliott

    2012-03-01

    Neuropeptides in the arginine vasotocin/arginine vasopressin (AVT/AVP) family play a major role in the regulation of social behavior by their actions in the brain. In mammals, AVP is found within a circuit of recriprocally connected limbic structures that form the social behavior neural network. This review examines the role played by AVP within this network in controlling social processes that are critical for the formation and maintenance of social relationships: social recognition, social communication and aggression. Studies in a number of mammalian species indicate that AVP and AVP V1a receptors are ideally suited to regulate the expression of social processes because of their plasticity in response to factors that influence social behavior. The pattern of AVP innervation and V1a receptors across the social behavior neural network may determine the potential range and intensity of social responses that individuals display in different social situations. Although fundamental information on how social behavior is wired in the brain is still lacking, it is clear that different social behaviors can be influenced by the actions of AVP in the same region of the network and that AVP can act within multiple regions of this network to regulate the expression of individual social behaviors. The existing data suggest that AVP can influence social behavior by modulating the interpretation of sensory information, by influencing decision making and by triggering complex motor outputs. This article is part of a Special Issue entitled Oxytocin, Vasopressin, and Social Behavior.

  14. Social network supported process recommender system.

    PubMed

    Ye, Yanming; Yin, Jianwei; Xu, Yueshen

    2014-01-01

    Process recommendation technologies have gained more and more attention in the field of intelligent business process modeling to assist the process modeling. However, most of the existing technologies only use the process structure analysis and do not take the social features of processes into account, while the process modeling is complex and comprehensive in most situations. This paper studies the feasibility of social network research technologies on process recommendation and builds a social network system of processes based on the features similarities. Then, three process matching degree measurements are presented and the system implementation is discussed subsequently. Finally, experimental evaluations and future works are introduced.

  15. The price of complexity in financial networks

    NASA Astrophysics Data System (ADS)

    Battiston, Stefano; Caldarelli, Guido; May, Robert M.; Roukny, Tarik; Stiglitz, Joseph E.

    2016-09-01

    Financial institutions form multilayer networks by engaging in contracts with each other and by holding exposures to common assets. As a result, the default probability of one institution depends on the default probability of all of the other institutions in the network. Here, we show how small errors on the knowledge of the network of contracts can lead to large errors in the probability of systemic defaults. From the point of view of financial regulators, our findings show that the complexity of financial networks may decrease the ability to mitigate systemic risk, and thus it may increase the social cost of financial crises.

  16. The price of complexity in financial networks

    PubMed Central

    May, Robert M.; Roukny, Tarik; Stiglitz, Joseph E.

    2016-01-01

    Financial institutions form multilayer networks by engaging in contracts with each other and by holding exposures to common assets. As a result, the default probability of one institution depends on the default probability of all of the other institutions in the network. Here, we show how small errors on the knowledge of the network of contracts can lead to large errors in the probability of systemic defaults. From the point of view of financial regulators, our findings show that the complexity of financial networks may decrease the ability to mitigate systemic risk, and thus it may increase the social cost of financial crises. PMID:27555583

  17. The price of complexity in financial networks.

    PubMed

    Battiston, Stefano; Caldarelli, Guido; May, Robert M; Roukny, Tarik; Stiglitz, Joseph E

    2016-09-06

    Financial institutions form multilayer networks by engaging in contracts with each other and by holding exposures to common assets. As a result, the default probability of one institution depends on the default probability of all of the other institutions in the network. Here, we show how small errors on the knowledge of the network of contracts can lead to large errors in the probability of systemic defaults. From the point of view of financial regulators, our findings show that the complexity of financial networks may decrease the ability to mitigate systemic risk, and thus it may increase the social cost of financial crises.

  18. Complex Dynamics in Information Sharing Networks

    NASA Astrophysics Data System (ADS)

    Cronin, Bruce

    This study examines the roll-out of an electronic knowledge base in a medium-sized professional services firm over a six year period. The efficiency of such implementation is a key business problem in IT systems of this type. Data from usage logs provides the basis for analysis of the dynamic evolution of social networks around the depository during this time. The adoption pattern follows an "s-curve" and usage exhibits something of a power law distribution, both attributable to network effects, and network position is associated with organisational performance on a number of indicators. But periodicity in usage is evident and the usage distribution displays an exponential cut-off. Further analysis provides some evidence of mathematical complexity in the periodicity. Some implications of complex patterns in social network data for research and management are discussed. The study provides a case study demonstrating the utility of the broad methodological approach.

  19. Effective Augmentation of Complex Networks

    PubMed Central

    Wang, Jinjian; Yu, Xinghuo; Stone, Lewi

    2016-01-01

    Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., conserving structural controllability. Our “effective augmentation” algorithm takes advantage of clusters intrinsic to the network topology, and permits rapidly and efficient augmentation of a large number of nodes in one time-step. “Effective augmentation” is shown to work successfully on a wide range of model and real networks. The method has numerous applications (e.g. study of biological, social, power and technological networks) and potentially of significant practical and economic value. PMID:27165120

  20. Effective Augmentation of Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Jinjian; Yu, Xinghuo; Stone, Lewi

    2016-05-01

    Networks science plays an enormous role in many aspects of modern society from distributing electrical power across nations to spreading information and social networking amongst global populations. While modern networks constantly change in size, few studies have sought methods for the difficult task of optimising this growth. Here we study theoretical requirements for augmenting networks by adding source or sink nodes, without requiring additional driver-nodes to accommodate the change i.e., conserving structural controllability. Our “effective augmentation” algorithm takes advantage of clusters intrinsic to the network topology, and permits rapidly and efficient augmentation of a large number of nodes in one time-step. “Effective augmentation” is shown to work successfully on a wide range of model and real networks. The method has numerous applications (e.g. study of biological, social, power and technological networks) and potentially of significant practical and economic value.

  1. Combinatorial Laplacian and entropy of simplicial complexes associated with complex networks

    NASA Astrophysics Data System (ADS)

    Maletić, S.; Rajković, M.

    2012-09-01

    Simplicial complexes represent useful and accurate models of complex networks and complex systems in general. We explore the properties of spectra of combinatorial Laplacian operator of simplicial complexes and show its relationship with connectivity properties of the Q-vector and with connectivities of cliques in the simplicial clique complex. We demonstrate the need for higher order analysis in complex networks and compare the results with ordinary graph spectra. Methods and results are obtained using social network of the Zachary karate club.

  2. Statistically Validated Networks in Bipartite Complex Systems

    PubMed Central

    Tumminello, Michele; Miccichè, Salvatore; Lillo, Fabrizio; Piilo, Jyrki; Mantegna, Rosario N.

    2011-01-01

    Many complex systems present an intrinsic bipartite structure where elements of one set link to elements of the second set. In these complex systems, such as the system of actors and movies, elements of one set are qualitatively different than elements of the other set. The properties of these complex systems are typically investigated by constructing and analyzing a projected network on one of the two sets (for example the actor network or the movie network). Complex systems are often very heterogeneous in the number of relationships that the elements of one set establish with the elements of the other set, and this heterogeneity makes it very difficult to discriminate links of the projected network that are just reflecting system's heterogeneity from links relevant to unveil the properties of the system. Here we introduce an unsupervised method to statistically validate each link of a projected network against a null hypothesis that takes into account system heterogeneity. We apply the method to a biological, an economic and a social complex system. The method we propose is able to detect network structures which are very informative about the organization and specialization of the investigated systems, and identifies those relationships between elements of the projected network that cannot be explained simply by system heterogeneity. We also show that our method applies to bipartite systems in which different relationships might have different qualitative nature, generating statistically validated networks in which such difference is preserved. PMID:21483858

  3. Error and attack tolerance of complex networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; Jeong, Hawoong; Barabási, Albert-László

    2000-07-01

    Many complex systems display a surprising degree of tolerance against errors. For example, relatively simple organisms grow, persist and reproduce despite drastic pharmaceutical or environmental interventions, an error tolerance attributed to the robustness of the underlying metabolic network. Complex communication networks display a surprising degree of robustness: although key components regularly malfunction, local failures rarely lead to the loss of the global information-carrying ability of the network. The stability of these and other complex systems is often attributed to the redundant wiring of the functional web defined by the systems' components. Here we demonstrate that error tolerance is not shared by all redundant systems: it is displayed only by a class of inhomogeneously wired networks, called scale-free networks, which include the World-Wide Web, the Internet, social networks and cells. We find that such networks display an unexpected degree of robustness, the ability of their nodes to communicate being unaffected even by unrealistically high failure rates. However, error tolerance comes at a high price in that these networks are extremely vulnerable to attacks (that is, to the selection and removal of a few nodes that play a vital role in maintaining the network's connectivity). Such error tolerance and attack vulnerability are generic properties of communication networks.

  4. Social Disadvantage and Network Turnover

    PubMed Central

    2015-01-01

    Objectives. Research shows that socially disadvantaged groups—especially African Americans and people of low socioeconomic status (SES)—experience more unstable social environments. I argue that this causes higher rates of turnover within their personal social networks. This is a particularly important issue among disadvantaged older adults, who may benefit from stable networks. This article, therefore, examines whether social disadvantage is related to various aspects of personal network change. Method. Social network change was assessed using longitudinal egocentric network data from the National Social Life, Health, and Aging Project, a study of older adults conducted between 2005 and 2011. Data collection in Wave 2 included a technique for comparing respondents’ confidant network rosters between waves. Rates of network losses, deaths, and additions were modeled using multivariate Poisson regression. Results. African Americans and low-SES individuals lost more confidants—especially due to death—than did whites and college-educated respondents. African Americans also added more confidants than whites. However, neither African Americans nor low-SES individuals were able to match confidant losses with new additions to the extent that others did, resulting in higher levels of confidant network shrinkage. These trends are partly, but not entirely, explained by disadvantaged individuals’ poorer health and their greater risk of widowhood or marital dissolution. Discussion. Additional work is needed to shed light on the role played by race- and class-based segregation on group differences in social network turnover. Social gerontologists should examine the role these differences play in explaining the link between social disadvantage and important outcomes in later life, such as health decline. PMID:24997286

  5. Social networks and neurological illness.

    PubMed

    Dhand, Amar; Luke, Douglas A; Lang, Catherine E; Lee, Jin-Moo

    2016-10-01

    Every patient is embedded in a social network of interpersonal connections that influence health outcomes. Neurologists routinely need to engage with a patient's family and friends due to the nature of the illness and its social sequelae. Social isolation is a potent determinant of poor health and neurobiological changes, and its effects can be comparable to those of traditional risk factors. It would seem reasonable, therefore, to map and follow the personal networks of neurology patients. This approach reveals influential people, their habits, and linkage patterns that could facilitate or limit health behaviours. Personal network information can be particularly valuable to enhance risk factor management, medication adherence, and functional recovery. Here, we propose an agenda for research and clinical practice that includes mapping the networks of patients with diverse neurological disorders, evaluating the impact of the networks on patient outcomes, and testing network interventions.

  6. Introduction to Social Network Analysis

    NASA Astrophysics Data System (ADS)

    Zaphiris, Panayiotis; Ang, Chee Siang

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

  7. Line graphs as social networks

    NASA Astrophysics Data System (ADS)

    Krawczyk, M. J.; Muchnik, L.; Mańka-Krasoń, A.; Kułakowski, K.

    2011-07-01

    It was demonstrated recently that the line graphs are clustered and assortative. These topological features are known to characterize some social networks [M.E.J. Newman, Y. Park, Why social networks are different from other types of networks, Phys. Rev. E 68 (2003) 036122]; it was argued that this similarity reveals their cliquey character. In the model proposed here, a social network is the line graph of an initial network of families, communities, interest groups, school classes and small companies. These groups play the role of nodes, and individuals are represented by links between these nodes. The picture is supported by the data on the LiveJournal network of about 8×10 6 people.

  8. The Possibilities of Network Sociality

    NASA Astrophysics Data System (ADS)

    Willson, Michele

    Technologically networked social forms are broad, extensive and in demand. The rapid development and growth of web 2.0, or the social web, is evidence of the need and indeed hunger for social connectivity: people are searching for many and varied ways of enacting being-together. However, the ways in which we think of, research and write about network(ed) sociality are relatively recent and arguably restricted, warranting further critique and development. This article attempts to do several things: it raises questions about the types of sociality enacted in contemporary techno-society; critically explores the notion of the networked individual and the focus on the individual evident in much of the technology and sociality literature and asks questions about the place of the social in these discussions. It argues for a more well-balanced and multilevelled approach to questions of sociality in networked societies. The article starts from the position that possibilities enabled/afforded by the technologies we have in place have an effect upon the ways in which we understand being in the world together and our possible actions and futures. These possibilities are more than simply supplementary; in many ways they are transformative. The ways in which we grapple with these questions reveals as much about our understandings of sociality as it does about the technologies themselves.

  9. Using Social Network Graphs as Visualization Tools to Influence Peer Selection Decision-Making Strategies to Access Information about Complex Socioscientific Issues

    ERIC Educational Resources Information Center

    Yoon, Susan A.

    2011-01-01

    This study extends previous research that explores how visualization affordances that computational tools provide and social network analyses that account for individual- and group-level dynamic processes can work in conjunction to improve learning outcomes. The study's main hypothesis is that when social network graphs are used in instruction,…

  10. Social networks and the development of social skills in cowbirds.

    PubMed

    White, David J; Gersick, Andrew S; Snyder-Mackler, Noah

    2012-07-05

    The complex interrelationships among individuals within social environments can exert selection pressures on social skills: those behaviours and cognitive processes that allow animals to manipulate and out-reproduce others. Social complexity can also have a developmental effect on social skills by providing individuals with opportunities to hone their skills by dealing with the challenges posed in within-group interactions. We examined how social skills develop in captive, adult male brown-headed cowbirds (Molothrus ater) that were exposed to differing levels of 'social complexity' across a 2-year experiment. After each year, subjects housed in groups with dynamic social structure (where many individuals entered and exited the groups during the year) outcompeted birds who had been housed in static groups. Exposure to dynamic structure subsequently led to substantial changes to the social networks of the home conditions during the breeding season. Static groups were characterized by a predictable relationship between singing and reproductive success that was stable across years. In dynamic conditions, however, males showed significant variability in their dominance status, their courting and even in their mating success. Reproductive success of males varied dramatically across years and was responsive to social learning in adulthood, and socially dynamic environments 'trained' individuals to be better competitors, even at an age when the development of many traits important for breeding (like song quality) had ended.

  11. Forman curvature for complex networks

    NASA Astrophysics Data System (ADS)

    Sreejith, R. P.; Mohanraj, Karthikeyan; Jost, Jürgen; Saucan, Emil; Samal, Areejit

    2016-06-01

    We adapt Forman’s discretization of Ricci curvature to the case of undirected networks, both weighted and unweighted, and investigate the measure in a variety of model and real-world networks. We find that most nodes and edges in model and real networks have a negative curvature. Furthermore, the distribution of Forman curvature of nodes and edges is narrow in random and small-world networks, while the distribution is broad in scale-free and real-world networks. In most networks, Forman curvature is found to display significant negative correlation with degree and centrality measures. However, Forman curvature is uncorrelated with clustering coefficient in most networks. Importantly, we find that both model and real networks are vulnerable to targeted deletion of nodes with highly negative Forman curvature. Our results suggest that Forman curvature can be employed to gain novel insights on the organization of complex networks.

  12. Hyperbolic geometry of complex networks.

    PubMed

    Krioukov, Dmitri; Papadopoulos, Fragkiskos; Kitsak, Maksim; Vahdat, Amin; Boguñá, Marián

    2010-09-01

    We develop a geometric framework to study the structure and function of complex networks. We assume that hyperbolic geometry underlies these networks, and we show that with this assumption, heterogeneous degree distributions and strong clustering in complex networks emerge naturally as simple reflections of the negative curvature and metric property of the underlying hyperbolic geometry. Conversely, we show that if a network has some metric structure, and if the network degree distribution is heterogeneous, then the network has an effective hyperbolic geometry underneath. We then establish a mapping between our geometric framework and statistical mechanics of complex networks. This mapping interprets edges in a network as noninteracting fermions whose energies are hyperbolic distances between nodes, while the auxiliary fields coupled to edges are linear functions of these energies or distances. The geometric network ensemble subsumes the standard configuration model and classical random graphs as two limiting cases with degenerate geometric structures. Finally, we show that targeted transport processes without global topology knowledge, made possible by our geometric framework, are maximally efficient, according to all efficiency measures, in networks with strongest heterogeneity and clustering, and that this efficiency is remarkably robust with respect to even catastrophic disturbances and damages to the network structure.

  13. Online Identities and Social Networking

    NASA Astrophysics Data System (ADS)

    Maheswaran, Muthucumaru; Ali, Bader; Ozguven, Hatice; Lord, Julien

    Online identities play a critical role in the social web that is taking shape on the Internet. Despite many technical proposals for creating and managing online identities, none has received widespread acceptance. Design and implementation of online identities that are socially acceptable on the Internet remains an open problem. This chapter discusses the interplay between online identities and social networking. Online social networks (OSNs) are growing at a rapid pace and has millions of members in them. While the recent trend is to create explicit OSNs such as Facebook and MySpace, we also have implicit OSNs such as interaction graphs created by email and instant messaging services. Explicit OSNs allow users to create profiles and use them to project their identities on the web. There are many interesting identity related issues in the context of social networking including how OSNs help and hinder the definition of online identities.

  14. Review of Public Safety in Viewpoint of Complex Networks

    NASA Astrophysics Data System (ADS)

    Chengcheng, Gai; Wenguo, Weng; Hongyong, Yuan

    2010-05-01

    In this paper, a brief review of public safety in viewpoint of complex networks is presented. Public safety incidents are divided into four categories: natural disasters, industry accidents, public health and social security, in which the complex network approaches and theories are need. We review how the complex network methods was developed and used in the studies of the three kinds of public safety incidents. The typical public safety incidents studied by the complex network methods in this paper are introduced, including the natural disaster chains, blackouts on electric power grids and epidemic spreading. Finally, we look ahead to the application prospects of the complex network theory on public safety.

  15. Review of Public Safety in Viewpoint of Complex Networks

    SciTech Connect

    Gai Chengcheng; Weng Wenguo; Yuan Hongyong

    2010-05-21

    In this paper, a brief review of public safety in viewpoint of complex networks is presented. Public safety incidents are divided into four categories: natural disasters, industry accidents, public health and social security, in which the complex network approaches and theories are need. We review how the complex network methods was developed and used in the studies of the three kinds of public safety incidents. The typical public safety incidents studied by the complex network methods in this paper are introduced, including the natural disaster chains, blackouts on electric power grids and epidemic spreading. Finally, we look ahead to the application prospects of the complex network theory on public safety.

  16. Quantization Effects on Complex Networks

    PubMed Central

    Wang, Ying; Wang, Lin; Yang, Wen; Wang, Xiaofan

    2016-01-01

    Weights of edges in many complex networks we constructed are quantized values of the real weights. To what extent does the quantization affect the properties of a network? In this work, quantization effects on network properties are investigated based on the spectrum of the corresponding Laplacian. In contrast to the intuition that larger quantization level always implies a better approximation of the quantized network to the original one, we find a ubiquitous periodic jumping phenomenon with peak-value decreasing in a power-law relationship in all the real-world weighted networks that we investigated. We supply theoretical analysis on the critical quantization level and the power laws. PMID:27226049

  17. Measurement of Online Social Networks

    ERIC Educational Resources Information Center

    Gjoka, Mina

    2010-01-01

    In recent years, the popularity of online social networks (OSN) has risen to unprecedented levels, with the most popular ones having hundreds of millions of users. This success has generated interest within the networking community and has given rise to a number of measurement and characterization studies, which provide a first step towards their…

  18. Analysis of complex networks using aggressive abstraction.

    SciTech Connect

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

    2008-10-01

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

  19. The physics of communicability in complex networks

    NASA Astrophysics Data System (ADS)

    Estrada, Ernesto; Hatano, Naomichi; Benzi, Michele

    2012-05-01

    A fundamental problem in the study of complex networks is to provide quantitative measures of correlation and information flow between different parts of a system. To this end, several notions of communicability have been introduced and applied to a wide variety of real-world networks in recent years. Several such communicability functions are reviewed in this paper. It is emphasized that communication and correlation in networks can take place through many more routes than the shortest paths, a fact that may not have been sufficiently appreciated in previously proposed correlation measures. In contrast to these, the communicability measures reviewed in this paper are defined by taking into account all possible routes between two nodes, assigning smaller weights to longer ones. This point of view naturally leads to the definition of communicability in terms of matrix functions, such as the exponential, resolvent, and hyperbolic functions, in which the matrix argument is either the adjacency matrix or the graph Laplacian associated with the network. Considerable insight on communicability can be gained by modeling a network as a system of oscillators and deriving physical interpretations, both classical and quantum-mechanical, of various communicability functions. Applications of communicability measures to the analysis of complex systems are illustrated on a variety of biological, physical and social networks. The last part of the paper is devoted to a review of the notion of locality in complex networks and to computational aspects that by exploiting sparsity can greatly reduce the computational efforts for the calculation of communicability functions for large networks.

  20. Social networks and the development of social skills in cowbirds

    PubMed Central

    White, David J.; Gersick, Andrew S.; Snyder-Mackler, Noah

    2012-01-01

    The complex interrelationships among individuals within social environments can exert selection pressures on social skills: those behaviours and cognitive processes that allow animals to manipulate and out-reproduce others. Social complexity can also have a developmental effect on social skills by providing individuals with opportunities to hone their skills by dealing with the challenges posed in within-group interactions. We examined how social skills develop in captive, adult male brown-headed cowbirds (Molothrus ater) that were exposed to differing levels of ‘social complexity’ across a 2-year experiment. After each year, subjects housed in groups with dynamic social structure (where many individuals entered and exited the groups during the year) outcompeted birds who had been housed in static groups. Exposure to dynamic structure subsequently led to substantial changes to the social networks of the home conditions during the breeding season. Static groups were characterized by a predictable relationship between singing and reproductive success that was stable across years. In dynamic conditions, however, males showed significant variability in their dominance status, their courting and even in their mating success. Reproductive success of males varied dramatically across years and was responsive to social learning in adulthood, and socially dynamic environments ‘trained’ individuals to be better competitors, even at an age when the development of many traits important for breeding (like song quality) had ended. PMID:22641827

  1. Complex Cooperative Networks from Evolutionary Preferential Attachment

    PubMed Central

    Poncela, Julia; Gómez-Gardeñes, Jesús; Floría, Luis M.; Sánchez, Angel; Moreno, Yamir

    2008-01-01

    In spite of its relevance to the origin of complex networks, the interplay between form and function and its role during network formation remains largely unexplored. While recent studies introduce dynamics by considering rewiring processes of a pre-existent network, we study network growth and formation by proposing an evolutionary preferential attachment model, its main feature being that the capacity of a node to attract new links depends on a dynamical variable governed in turn by the node interactions. As a specific example, we focus on the problem of the emergence of cooperation by analyzing the formation of a social network with interactions given by the Prisoner's Dilemma. The resulting networks show many features of real systems, such as scale-free degree distributions, cooperative behavior and hierarchical clustering. Interestingly, results such as the cooperators being located mostly on nodes of intermediate degree are very different from the observations of cooperative behavior on static networks. The evolutionary preferential attachment mechanism points to an evolutionary origin of scale-free networks and may help understand similar feedback problems in the dynamics of complex networks by appropriately choosing the game describing the interaction of nodes. PMID:18560601

  2. Assessing group interaction with social language network analysis.

    SciTech Connect

    Pennebaker, James; Scholand, Andrew Joseph; Tausczik, Yla R.

    2010-04-01

    In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

  3. Assessing Group Interaction with Social Language Network Analysis

    NASA Astrophysics Data System (ADS)

    Scholand, Andrew J.; Tausczik, Yla R.; Pennebaker, James W.

    In this paper we discuss a new methodology, social language network analysis (SLNA), that combines tools from social language processing and network analysis to assess socially situated working relationships within a group. Specifically, SLNA aims to identify and characterize the nature of working relationships by processing artifacts generated with computer-mediated communication systems, such as instant message texts or emails. Because social language processing is able to identify psychological, social, and emotional processes that individuals are not able to fully mask, social language network analysis can clarify and highlight complex interdependencies between group members, even when these relationships are latent or unrecognized.

  4. Identifying community structure in complex networks

    NASA Astrophysics Data System (ADS)

    Shao, Chenxi; Duan, Yubing

    2015-07-01

    A wide variety of applications could be formulated to resolve the problem of finding all communities from a given network, ranging from social and biological network analysis to web mining and searching. In this study, we propose the concept of virtual attractive strength between each pair of node in networks, and then give the definition of community structure based on the proposed attractive strength. Furthermore, we present a community detection method by moving vertices to the clusters that produce the largest attractive strengths to them until the division of network reaches unchanged. Experimental results on synthetic and real networks indicate that the proposed approach has favorite effectiveness and fast convergence speed, which provides an efficient method for exploring and analyzing complex systems.

  5. Competitive cluster growth in complex networks

    NASA Astrophysics Data System (ADS)

    Moreira, André A.; Paula, Demétrius R.; Costa Filho, Raimundo N.; Andrade, José S., Jr.

    2006-06-01

    In this work we propose an idealized model for competitive cluster growth in complex networks. Each cluster can be thought of as a fraction of a community that shares some common opinion. Our results show that the cluster size distribution depends on the particular choice for the topology of the network of contacts among the agents. As an application, we show that the cluster size distributions obtained when the growth process is performed on hierarchical networks, e.g., the Apollonian network, have a scaling form similar to what has been observed for the distribution of a number of votes in an electoral process. We suggest that this similarity may be due to the fact that social networks involved in the electoral process may also possess an underlining hierarchical structure.

  6. Assortative model for social networks

    NASA Astrophysics Data System (ADS)

    Catanzaro, Michele; Caldarelli, Guido; Pietronero, Luciano

    2004-09-01

    In this Brief Report we present a version of a network growth model, generalized in order to describe the behavior of social networks. The case of study considered is the preprint archive at cul.arxiv.org. Each node corresponds to a scientist, and a link is present whenever two authors wrote a paper together. This graph is a nice example of degree-assortative network, that is, to say a network where sites with similar degree are connected to each other. The model presented is one of the few able to reproduce such behavior, giving some insight on the microscopic dynamics at the basis of the graph structure.

  7. Kleinberg Complex Networks

    DTIC Science & Technology

    2014-10-21

    networks. With economists Larry Blume and David Easley, and computer scientists Jon Kleinberg and Éva Tardos, we investigated so-called threshold...and Nicole Immorlica [13], we investigated Schelling’s segregation model, a famous model of residential segregation that has been observed, in...approximation ratio achieves the first provable improvement upon the approximation ratio of the famous algorithm introduced by Christofides in 1976 (which

  8. Hierarchy measure for complex networks.

    PubMed

    Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

    2012-01-01

    Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure.

  9. Hierarchy Measure for Complex Networks

    PubMed Central

    Mones, Enys; Vicsek, Lilla; Vicsek, Tamás

    2012-01-01

    Nature, technology and society are full of complexity arising from the intricate web of the interactions among the units of the related systems (e.g., proteins, computers, people). Consequently, one of the most successful recent approaches to capturing the fundamental features of the structure and dynamics of complex systems has been the investigation of the networks associated with the above units (nodes) together with their relations (edges). Most complex systems have an inherently hierarchical organization and, correspondingly, the networks behind them also exhibit hierarchical features. Indeed, several papers have been devoted to describing this essential aspect of networks, however, without resulting in a widely accepted, converging concept concerning the quantitative characterization of the level of their hierarchy. Here we develop an approach and propose a quantity (measure) which is simple enough to be widely applicable, reveals a number of universal features of the organization of real-world networks and, as we demonstrate, is capable of capturing the essential features of the structure and the degree of hierarchy in a complex network. The measure we introduce is based on a generalization of the m-reach centrality, which we first extend to directed/partially directed graphs. Then, we define the global reaching centrality (GRC), which is the difference between the maximum and the average value of the generalized reach centralities over the network. We investigate the behavior of the GRC considering both a synthetic model with an adjustable level of hierarchy and real networks. Results for real networks show that our hierarchy measure is related to the controllability of the given system. We also propose a visualization procedure for large complex networks that can be used to obtain an overall qualitative picture about the nature of their hierarchical structure. PMID:22470477

  10. Maximizing information exchange between complex networks

    NASA Astrophysics Data System (ADS)

    West, Bruce J.; Geneston, Elvis L.; Grigolini, Paolo

    2008-10-01

    modern research overarching all of the traditional scientific disciplines. The transportation networks of planes, highways and railroads; the economic networks of global finance and stock markets; the social networks of terrorism, governments, businesses and churches; the physical networks of telephones, the Internet, earthquakes and global warming and the biological networks of gene regulation, the human body, clusters of neurons and food webs, share a number of apparently universal properties as the networks become increasingly complex. Ubiquitous aspects of such complex networks are the appearance of non-stationary and non-ergodic statistical processes and inverse power-law statistical distributions. Herein we review the traditional dynamical and phase-space methods for modeling such networks as their complexity increases and focus on the limitations of these procedures in explaining complex networks. Of course we will not be able to review the entire nascent field of network science, so we limit ourselves to a review of how certain complexity barriers have been surmounted using newly applied theoretical concepts such as aging, renewal, non-ergodic statistics and the fractional calculus. One emphasis of this review is information transport between complex networks, which requires a fundamental change in perception that we express as a transition from the familiar stochastic resonance to the new concept of complexity matching.

  11. Social networks and environmental outcomes.

    PubMed

    Barnes, Michele L; Lynham, John; Kalberg, Kolter; Leung, PingSun

    2016-06-07

    Social networks can profoundly affect human behavior, which is the primary force driving environmental change. However, empirical evidence linking microlevel social interactions to large-scale environmental outcomes has remained scarce. Here, we leverage comprehensive data on information-sharing networks among large-scale commercial tuna fishers to examine how social networks relate to shark bycatch, a global environmental issue. We demonstrate that the tendency for fishers to primarily share information within their ethnic group creates segregated networks that are strongly correlated with shark bycatch. However, some fishers share information across ethnic lines, and examinations of their bycatch rates show that network contacts are more strongly related to fishing behaviors than ethnicity. Our findings indicate that social networks are tied to actions that can directly impact marine ecosystems, and that biases toward within-group ties may impede the diffusion of sustainable behaviors. Importantly, our analysis suggests that enhanced communication channels across segregated fisher groups could have prevented the incidental catch of over 46,000 sharks between 2008 and 2012 in a single commercial fishery.

  12. Rumor evolution in social networks

    NASA Astrophysics Data System (ADS)

    Zhang, Yichao; Zhou, Shi; Zhang, Zhongzhi; Guan, Jihong; Zhou, Shuigeng

    2013-03-01

    The social network is a main tunnel of rumor spreading. Previous studies concentrated on a static rumor spreading. The content of the rumor is invariable during the whole spreading process. Indeed, the rumor evolves constantly in its spreading process, which grows shorter, more concise, more easily grasped, and told. In an early psychological experiment, researchers found about 70% of details in a rumor were lost in the first six mouth-to-mouth transmissions. Based on these observations, we investigate rumor spreading on social networks, where the content of the rumor is modified by the individuals with a certain probability. In the scenario, they have two choices, to forward or to modify. As a forwarder, an individual disseminates the rumor directly to their neighbors. As a modifier, conversely, an individual revises the rumor before spreading it out. When the rumor spreads on the social networks, for instance, scale-free networks and small-world networks, the majority of individuals actually are infected by the multirevised version of the rumor, if the modifiers dominate the networks. The individuals with more social connections have a higher probability to receive the original rumor. Our observation indicates that the original rumor may lose its influence in the spreading process. Similarly, a true information may turn out to be a rumor as well. Our result suggests the rumor evolution should not be a negligible question, which may provide a better understanding of the generation and destruction of a rumor.

  13. Social networks and environmental outcomes

    PubMed Central

    Kalberg, Kolter; Leung, PingSun

    2016-01-01

    Social networks can profoundly affect human behavior, which is the primary force driving environmental change. However, empirical evidence linking microlevel social interactions to large-scale environmental outcomes has remained scarce. Here, we leverage comprehensive data on information-sharing networks among large-scale commercial tuna fishers to examine how social networks relate to shark bycatch, a global environmental issue. We demonstrate that the tendency for fishers to primarily share information within their ethnic group creates segregated networks that are strongly correlated with shark bycatch. However, some fishers share information across ethnic lines, and examinations of their bycatch rates show that network contacts are more strongly related to fishing behaviors than ethnicity. Our findings indicate that social networks are tied to actions that can directly impact marine ecosystems, and that biases toward within-group ties may impede the diffusion of sustainable behaviors. Importantly, our analysis suggests that enhanced communication channels across segregated fisher groups could have prevented the incidental catch of over 46,000 sharks between 2008 and 2012 in a single commercial fishery. PMID:27217551

  14. Control efficacy of complex networks

    PubMed Central

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-01-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks. PMID:27324438

  15. Control efficacy of complex networks

    NASA Astrophysics Data System (ADS)

    Gao, Xin-Dong; Wang, Wen-Xu; Lai, Ying-Cheng

    2016-06-01

    Controlling complex networks has become a forefront research area in network science and engineering. Recent efforts have led to theoretical frameworks of controllability to fully control a network through steering a minimum set of driver nodes. However, in realistic situations not every node is accessible or can be externally driven, raising the fundamental issue of control efficacy: if driving signals are applied to an arbitrary subset of nodes, how many other nodes can be controlled? We develop a framework to determine the control efficacy for undirected networks of arbitrary topology. Mathematically, based on non-singular transformation, we prove a theorem to determine rigorously the control efficacy of the network and to identify the nodes that can be controlled for any given driver nodes. Physically, we develop the picture of diffusion that views the control process as a signal diffused from input signals to the set of controllable nodes. The combination of mathematical theory and physical reasoning allows us not only to determine the control efficacy for model complex networks and a large number of empirical networks, but also to uncover phenomena in network control, e.g., hub nodes in general possess lower control centrality than an average node in undirected networks.

  16. Information communication on complex networks

    NASA Astrophysics Data System (ADS)

    Igarashi, Akito; Kawamoto, Hiroki; Maruyama, Takahiro; Morioka, Atsushi; Naganuma, Yuki

    2013-02-01

    Since communication networks such as the Internet, which is regarded as a complex network, have recently become a huge scale and a lot of data pass through them, the improvement of packet routing strategies for transport is one of the most significant themes in the study of computer networks. It is especially important to find routing strategies which can bear as many traffic as possible without congestion in complex networks. First, using neural networks, we introduce a strategy for packet routing on complex networks, where path lengths and queue lengths in nodes are taken into account within a framework of statistical physics. Secondly, instead of using shortest paths, we propose efficient paths which avoid hubs, nodes with a great many degrees, on scale-free networks with a weight of each node. We improve the heuristic algorithm proposed by Danila et. al. which optimizes step by step routing properties on congestion by using the information of betweenness, the probability of paths passing through a node in all optimal paths which are defined according to a rule, and mitigates the congestion. We confirm the new heuristic algorithm which balances traffic on networks by achieving minimization of the maximum betweenness in much smaller number of iteration steps. Finally, We model virus spreading and data transfer on peer-to-peer (P2P) networks. Using mean-field approximation, we obtain an analytical formulation and emulate virus spreading on the network and compare the results with those of simulation. Moreover, we investigate the mitigation of information traffic congestion in the P2P networks.

  17. Networks in Social Policy Problems

    NASA Astrophysics Data System (ADS)

    Vedres, Balázs; Scotti, Marco

    2012-08-01

    1. Introduction M. Scotti and B. Vedres; Part I. Information, Collaboration, Innovation: The Creative Power of Networks: 2. Dissemination of health information within social networks C. Dhanjal, S. Blanchemanche, S. Clemençon, A. Rona-Tas and F. Rossi; 3. Scientific teams and networks change the face of knowledge creation S. Wuchty, J. Spiro, B. F. Jones and B. Uzzi; 4. Structural folds: the innovative potential of overlapping groups B. Vedres and D. Stark; 5. Team formation and performance on nanoHub: a network selection challenge in scientific communities D. Margolin, K. Ognyanova, M. Huang, Y. Huang and N. Contractor; Part II. Influence, Capture, Corruption: Networks Perspectives on Policy Institutions: 6. Modes of coordination of collective action: what actors in policy making? M. Diani; 7. Why skewed distributions of pay for executives is the cause of much grief: puzzles and few answers so far B. Kogut and J.-S. Yang; 8. Networks of institutional capture: a case of business in the State apparatus E. Lazega and L. Mounier; 9. The social and institutional structure of corruption: some typical network configurations of corruption transactions in Hungary Z. Szántó, I. J. Tóth and S. Varga; Part III. Crisis, Extinction, World System Change: Network Dynamics on a Large Scale: 10. How creative elements help the recovery of networks after crisis: lessons from biology A. Mihalik, A. S. Kaposi, I. A. Kovács, T. Nánási, R. Palotai, Á. Rák, M. S. Szalay-Beko and P. Csermely; 11. Networks and globalization policies D. R. White; 12. Network science in ecology: the structure of ecological communities and the biodiversity question A. Bodini, S. Allesina and C. Bondavalli; 13. Supply security in the European natural gas pipeline network M. Scotti and B. Vedres; 14. Conclusions and outlook A.-L. Barabási; Index.

  18. Privacy Amplification with Social Networks

    NASA Astrophysics Data System (ADS)

    Nagaraja, Shishir

    There are a number of scenarios where users wishing to communicate, share a weak secret. Often, they are also part of a common social network. Connections (edges) from the social network are represented as shared link keys between participants (vertices). We propose mechanisms that utilise the graph topology of such a network, to increase the entropy of weak pre-shared secrets. Our proposal is based on using random walks to identify a chain of common acquaintances between Alice and Bob, each of which contribute entropy to the final key. Our mechanisms exploit one-wayness and convergence properties of Markovian random walks to, firstly, maximize the set of potential entropy contributors, and second, to resist any contribution from dubious sources such as Sybill sub-networks.

  19. Navigating Social Networks

    ERIC Educational Resources Information Center

    Hamblin, DeAnna; Bartlett, Marilyn J.

    2013-01-01

    The authors note that when it comes to balancing free speech and schools' responsibilities, the online world is largely uncharted waters. Questions remain about the rights of both students and teachers in the world of social media. Although the lower courts have ruled that students' freedom of speech rights offer them some protection for…

  20. Measuring multiple evolution mechanisms of complex networks

    PubMed Central

    Zhang, Qian-Ming; Xu, Xiao-Ke; Zhu, Yu-Xiao; Zhou, Tao

    2015-01-01

    Numerous concise models such as preferential attachment have been put forward to reveal the evolution mechanisms of real-world networks, which show that real-world networks are usually jointly driven by a hybrid mechanism of multiplex features instead of a single pure mechanism. To get an accurate simulation for real networks, some researchers proposed a few hybrid models by mixing multiple evolution mechanisms. Nevertheless, how a hybrid mechanism of multiplex features jointly influence the network evolution is not very clear. In this study, we introduce two methods (link prediction and likelihood analysis) to measure multiple evolution mechanisms of complex networks. Through tremendous experiments on artificial networks, which can be controlled to follow multiple mechanisms with different weights, we find the method based on likelihood analysis performs much better and gives very accurate estimations. At last, we apply this method to some real-world networks which are from different domains (including technology networks and social networks) and different countries (e.g., USA and China), to see how popularity and clustering co-evolve. We find most of them are affected by both popularity and clustering, but with quite different weights. PMID:26065382

  1. Multiscale vulnerability of complex networks.

    PubMed

    Boccaletti, Stefano; Buldú, Javier; Criado, Regino; Flores, Julio; Latora, Vito; Pello, Javier; Romance, Miguel

    2007-12-01

    We present a novel approach to quantify the vulnerability of a complex network, i.e., the capacity of a graph to maintain its functional performance under random damages or malicious attacks. The proposed measure represents a multiscale evaluation of vulnerability, and makes use of combined powers of the links' betweenness. We show that the proposed approach is able to properly describe some cases for which earlier measures of vulnerability fail. The relevant applications of our method for technological network design are outlined.

  2. Porous Soil as Complex Network

    NASA Astrophysics Data System (ADS)

    Benito, R. M.; Santiago, A.; Cárdenas, J. P.; Tarquis, A. M.; Borondo, F.; Losada, J. C.

    2009-04-01

    We present a complex network model based on a heterogeneous preferential attachment scheme [1,2] to quantify the structure of porous soils [3]. Under this perspective pores are represented by nodes and the space for the flow of fluids between them are represented by links. Pore properties such as position and size are described by fixed states in a metric space, while an affinity function is introduced to bias the attachment probabilities of links according to these properties. We perform an analytical and numerical study of the degree distributions in the soil model and show that under reasonable conditions all the model variants yield a multiscaling behavior in the connectivity degrees, leaving a empirically testable signature of heterogeneity in the topology of pore networks. References [1] A. Santiago and R. M. Benito, "Emergence of multiscaling in heterogeneous complex networks". Int. J. Mod. Phys. C 18, 1591 (2007). [2] A. Santiago and R. M. Benito, "An extended formalism for preferential attachment in heterogeneous complex networks". Europhys. Lett. 82, 58004 (2008). [3] A. Santiago, R. M. Benito, J. P. Cárdenas, J. C. Losada, A. M. Tarquis and F. Borondo, "Multiscaling of porous soils as heterogeneous complex networks". Nonl. Proc. Geophys. 15, 1-10 (2008).

  3. Complex Networks in Psychological Models

    NASA Astrophysics Data System (ADS)

    Wedemann, R. S.; Carvalho, L. S. A. V. D.; Donangelo, R.

    We develop schematic, self-organizing, neural-network models to describe mechanisms associated with mental processes, by a neurocomputational substrate. These models are examples of real world complex networks with interesting general topological structures. Considering dopaminergic signal-to-noise neuronal modulation in the central nervous system, we propose neural network models to explain development of cortical map structure and dynamics of memory access, and unify different mental processes into a single neurocomputational substrate. Based on our neural network models, neurotic behavior may be understood as an associative memory process in the brain, and the linguistic, symbolic associative process involved in psychoanalytic working-through can be mapped onto a corresponding process of reconfiguration of the neural network. The models are illustrated through computer simulations, where we varied dopaminergic modulation and observed the self-organizing emergent patterns at the resulting semantic map, interpreting them as different manifestations of mental functioning, from psychotic through to normal and neurotic behavior, and creativity.

  4. Robustness Elasticity in Complex Networks

    PubMed Central

    Matisziw, Timothy C.; Grubesic, Tony H.; Guo, Junyu

    2012-01-01

    Network robustness refers to a network’s resilience to stress or damage. Given that most networks are inherently dynamic, with changing topology, loads, and operational states, their robustness is also likely subject to change. However, in most analyses of network structure, it is assumed that interaction among nodes has no effect on robustness. To investigate the hypothesis that network robustness is not sensitive or elastic to the level of interaction (or flow) among network nodes, this paper explores the impacts of network disruption, namely arc deletion, over a temporal sequence of observed nodal interactions for a large Internet backbone system. In particular, a mathematical programming approach is used to identify exact bounds on robustness to arc deletion for each epoch of nodal interaction. Elasticity of the identified bounds relative to the magnitude of arc deletion is assessed. Results indicate that system robustness can be highly elastic to spatial and temporal variations in nodal interactions within complex systems. Further, the presence of this elasticity provides evidence that a failure to account for nodal interaction can confound characterizations of complex networked systems. PMID:22808060

  5. Controlling centrality in complex networks

    PubMed Central

    Nicosia, V.; Criado, R.; Romance, M.; Russo, G.; Latora, V.

    2012-01-01

    Spectral centrality measures allow to identify influential individuals in social groups, to rank Web pages by popularity, and even to determine the impact of scientific researches. The centrality score of a node within a network crucially depends on the entire pattern of connections, so that the usual approach is to compute node centralities once the network structure is assigned. We face here with the inverse problem, that is, we study how to modify the centrality scores of the nodes by acting on the structure of a given network. We show that there exist particular subsets of nodes, called controlling sets, which can assign any prescribed set of centrality values to all the nodes of a graph, by cooperatively tuning the weights of their out-going links. We found that many large networks from the real world have surprisingly small controlling sets, containing even less than 5 – 10% of the nodes. PMID:22355732

  6. From biological and social network metaphors to coupled bio-social wireless networks

    PubMed Central

    Barrett, Christopher L.; Eubank, Stephen; Anil Kumar, V.S.; Marathe, Madhav V.

    2010-01-01

    Biological and social analogies have been long applied to complex systems. Inspiration has been drawn from biological solutions to solve problems in engineering products and systems, ranging from Velcro to camouflage to robotics to adaptive and learning computing methods. In this paper, we present an overview of recent advances in understanding biological systems as networks and use this understanding to design and analyse wireless communication networks. We expand on two applications, namely cognitive sensing and control and wireless epidemiology. We discuss how our work in these two applications is motivated by biological metaphors. We believe that recent advances in computing and communications coupled with advances in health and social sciences raise the possibility of studying coupled bio-social communication networks. We argue that we can better utilise the advances in our understanding of one class of networks to better our understanding of the other. PMID:21643462

  7. Purity homophily in social networks.

    PubMed

    Dehghani, Morteza; Johnson, Kate; Hoover, Joe; Sagi, Eyal; Garten, Justin; Parmar, Niki Jitendra; Vaisey, Stephen; Iliev, Rumen; Graham, Jesse

    2016-03-01

    Does sharing moral values encourage people to connect and form communities? The importance of moral homophily (love of same) has been recognized by social scientists, but the types of moral similarities that drive this phenomenon are still unknown. Using both large-scale, observational social-media analyses and behavioral lab experiments, the authors investigated which types of moral similarities influence tie formations. Analysis of a corpus of over 700,000 tweets revealed that the distance between 2 people in a social-network can be predicted based on differences in the moral purity content-but not other moral content-of their messages. The authors replicated this finding by experimentally manipulating perceived moral difference (Study 2) and similarity (Study 3) in the lab and demonstrating that purity differences play a significant role in social distancing. These results indicate that social network processes reflect moral selection, and both online and offline differences in moral purity concerns are particularly predictive of social distance. This research is an attempt to study morality indirectly using an observational big-data study complemented with 2 confirmatory behavioral experiments carried out using traditional social-psychology methodology.

  8. Social inheritance can explain the structure of animal social networks.

    PubMed

    Ilany, Amiyaal; Akçay, Erol

    2016-06-28

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance.

  9. Social inheritance can explain the structure of animal social networks

    PubMed Central

    Ilany, Amiyaal; Akçay, Erol

    2016-01-01

    The social network structure of animal populations has major implications for survival, reproductive success, sexual selection and pathogen transmission of individuals. But as of yet, no general theory of social network structure exists that can explain the diversity of social networks observed in nature, and serve as a null model for detecting species and population-specific factors. Here we propose a simple and generally applicable model of social network structure. We consider the emergence of network structure as a result of social inheritance, in which newborns are likely to bond with maternal contacts, and via forming bonds randomly. We compare model output with data from several species, showing that it can generate networks with properties such as those observed in real social systems. Our model demonstrates that important observed properties of social networks, including heritability of network position or assortative associations, can be understood as consequences of social inheritance. PMID:27352101

  10. Privacy and Social Networking Sites

    ERIC Educational Resources Information Center

    Timm, Dianne M.; Duven, Carolyn J.

    2008-01-01

    College students are relying on the Internet to make connections with other people every day. As the Internet has developed and grown, so have the capabilities for interaction. Social networking sites, a group of Web sites that provide people with the opportunity to create an online profile and to share that profile with others, are a part of…

  11. Social Networking: Keeping It Clean

    ERIC Educational Resources Information Center

    Waters, John K.

    2011-01-01

    The need to maintain an unpolluted learning environment is no easy task for schools and districts that have incorporated social networking sites into their educational life. The staff and teachers at Blaine High School in Minnesota's Anoka-Hennepin District 11 had been considering the pros and cons of establishing a school Facebook page when the…

  12. Blockmodeling techniques for complex networks

    NASA Astrophysics Data System (ADS)

    Ball, Brian Joseph

    The class of network models known as stochastic blockmodels has recently been gaining popularity. In this dissertation, we present new work that uses blockmodels to answer questions about networks. We create a blockmodel based on the idea of link communities, which naturally gives rise to overlapping vertex communities. We derive a fast and accurate algorithm to fit the model to networks. This model can be related to another blockmodel, which allows the method to efficiently find nonoverlapping communities as well. We then create a heuristic based on the link community model whose use is to find the correct number of communities in a network. The heuristic is based on intuitive corrections to likelihood ratio tests. It does a good job finding the correct number of communities in both real networks and synthetic networks generated from the link communities model. Two commonly studied types of networks are citation networks, where research papers cite other papers, and coauthorship networks, where authors are connected if they've written a paper together. We study a multi-modal network from a large dataset of Physics publications that is the combination of the two, allowing for directed links between papers as citations, and an undirected edge between a scientist and a paper if they helped to write it. This allows for new insights on the relation between social interaction and scientific production. We also have the publication dates of papers, which lets us track our measures over time. Finally, we create a stochastic model for ranking vertices in a semi-directed network. The probability of connection between two vertices depends on the difference of their ranks. When this model is fit to high school friendship networks, the ranks appear to correspond with a measure of social status. Students have reciprocated and some unreciprocated edges with other students of closely similar rank that correspond to true friendship, and claim an aspirational friendship with a much

  13. Characterizing the Community Structure of Complex Networks

    PubMed Central

    Lancichinetti, Andrea; Kivelä, Mikko; Saramäki, Jari; Fortunato, Santo

    2010-01-01

    Background Community structure is one of the key properties of complex networks and plays a crucial role in their topology and function. While an impressive amount of work has been done on the issue of community detection, very little attention has been so far devoted to the investigation of communities in real networks. Methodology/Principal Findings We present a systematic empirical analysis of the statistical properties of communities in large information, communication, technological, biological, and social networks. We find that the mesoscopic organization of networks of the same category is remarkably similar. This is reflected in several characteristics of community structure, which can be used as “fingerprints” of specific network categories. While community size distributions are always broad, certain categories of networks consist mainly of tree-like communities, while others have denser modules. Average path lengths within communities initially grow logarithmically with community size, but the growth saturates or slows down for communities larger than a characteristic size. This behaviour is related to the presence of hubs within communities, whose roles differ across categories. Also the community embeddedness of nodes, measured in terms of the fraction of links within their communities, has a characteristic distribution for each category. Conclusions/Significance Our findings, verified by the use of two fundamentally different community detection methods, allow for a classification of real networks and pave the way to a realistic modelling of networks' evolution. PMID:20711338

  14. Social structure of Facebook networks

    NASA Astrophysics Data System (ADS)

    Traud, Amanda L.; Mucha, Peter J.; Porter, Mason A.

    2012-08-01

    We study the social structure of Facebook “friendship” networks at one hundred American colleges and universities at a single point in time, and we examine the roles of user attributes-gender, class year, major, high school, and residence-at these institutions. We investigate the influence of common attributes at the dyad level in terms of assortativity coefficients and regression models. We then examine larger-scale groupings by detecting communities algorithmically and comparing them to network partitions based on user characteristics. We thereby examine the relative importance of different characteristics at different institutions, finding for example that common high school is more important to the social organization of large institutions and that the importance of common major varies significantly between institutions. Our calculations illustrate how microscopic and macroscopic perspectives give complementary insights on the social organization at universities and suggest future studies to investigate such phenomena further.

  15. Target recovery in complex networks

    NASA Astrophysics Data System (ADS)

    Sun, Weiman; Zeng, An

    2017-01-01

    The invulnerability of complex networks is an important issue which has been widely analyzed in different fields. A lot of works have been done to measure and improve the stability of complex networks when being attacked. Recently, how to recover networks after attack was intensively studied. The existing methods are mainly designed to recover the overall functionality of networks, yet in many real cases the recovery of important nodes should be given priority, to which we refer target recovery. For example, when the cold wave paralyses the railway networks, target recovery means to repair those stations or railways such that the transport capacity of densely-populated cities can be recovered as fast as possible. In this paper, we first compare the impact of attacks on the whole network and target nodes respectively, and then study the efficiency of traditional recovery methods that are proposed based on global centrality metrics. Furthermore, based on target centrality metrics, we introduce a local betweenness recovery method and we find it has better performance than the traditional methods. We finally propose a hybrid recovery method which includes local betweenness metric and local closeness metric. The performance of the hybrid method is shown to be similar to that of the greedy algorithm.

  16. Reactive immunization on complex networks

    NASA Astrophysics Data System (ADS)

    Alfinito, Eleonora; Beccaria, Matteo; Fachechi, Alberto; Macorini, Guido

    2017-01-01

    Epidemic spreading on complex networks depends on the topological structure as well as on the dynamical properties of the infection itself. Generally speaking, highly connected individuals play the role of hubs and are crucial to channel information across the network. On the other hand, static topological quantities measuring the connectivity structure are independent of the dynamical mechanisms of the infection. A natural question is therefore how to improve the topological analysis by some kind of dynamical information that may be extracted from the ongoing infection itself. In this spirit, we propose a novel vaccination scheme that exploits information from the details of the infection pattern at the moment when the vaccination strategy is applied. Numerical simulations of the infection process show that the proposed immunization strategy is effective and robust on a wide class of complex networks.

  17. Quantum physics and complex networks

    NASA Astrophysics Data System (ADS)

    Biamonte, Jacob

    2014-03-01

    There is a widely used and successful theory of ``chemical reaction networks,'' which provides a framework describing systems governed by mass action kinetics. Computer science and population biology use the same ideas under a different name: ``stochastic Petri nets.'' But if we look at these theories from the perspective of quantum theory, they turn out to involve creation and annihilation operators, coherent states and other well-known ideas--yet in a context where probabilities replace amplitudes. I will explain this connection as part of a detailed analogy between quantum mechanics and stochastic mechanics which we've produced several results on recently, including the recent analytical results uniting quantum physics and complex networks. Our general idea is about merging concepts from quantum physics and complex network theory to provide a bidirectional bridge between both disciplines. Support is acknowledged from the Foundational Questions Institute (FQXi) and the Compagnia di San Paolo Foundation.

  18. Masculinity, Educational Achievement and Social Status: A Social Network Analysis

    ERIC Educational Resources Information Center

    Lusher, Dean

    2011-01-01

    This study utilises a quantitative case study social network approach to explore the connection between masculinity and scholastic achievement in two secondary, all-boys schools in Australia. In both schools two social networks representing social status are explored: the "friendship" network as a measure of status that includes…

  19. The complex network of musical tastes

    NASA Astrophysics Data System (ADS)

    Buldú, Javier M.; Cano, P.; Koppenberger, M.; Almendral, Juan A.; Boccaletti, S.

    2007-06-01

    We present an empirical study of the evolution of a social network constructed under the influence of musical tastes. The network is obtained thanks to the selfless effort of a broad community of users who share playlists of their favourite songs with other users. When two songs co-occur in a playlist a link is created between them, leading to a complex network where songs are the fundamental nodes. In this representation, songs in the same playlist could belong to different musical genres, but they are prone to be linked by a certain musical taste (e.g. if songs A and B co-occur in several playlists, an user who likes A will probably like also B). Indeed, playlist collections such as the one under study are the basic material that feeds some commercial music recommendation engines. Since playlists have an input date, we are able to evaluate the topology of this particular complex network from scratch, observing how its characteristic parameters evolve in time. We compare our results with those obtained from an artificial network defined by means of a null model. This comparison yields some insight on the evolution and structure of such a network, which could be used as ground data for the development of proper models. Finally, we gather information that can be useful for the development of music recommendation engines and give some hints about how top-hits appear.

  20. Degree correlations in signed social networks

    NASA Astrophysics Data System (ADS)

    Ciotti, Valerio; Bianconi, Ginestra; Capocci, Andrea; Colaiori, Francesca; Panzarasa, Pietro

    2015-03-01

    We investigate degree correlations in two online social networks where users are connected through different types of links. We find that, while subnetworks in which links have a positive connotation, such as endorsement and trust, are characterized by assortative mixing by degree, networks in which links have a negative connotation, such as disapproval and distrust, are characterized by disassortative patterns. We introduce a class of simple theoretical models to analyze the interplay between network topology and the superimposed structure based on the sign of links. Results uncover the conditions that underpin the emergence of the patterns observed in the data, namely the assortativity of positive subnetworks and the disassortativity of negative ones. We discuss the implications of our study for the analysis of signed complex networks.

  1. The Kuramoto model in complex networks

    NASA Astrophysics Data System (ADS)

    Rodrigues, Francisco A.; Peron, Thomas K. DM.; Ji, Peng; Kurths, Jürgen

    2016-01-01

    Synchronization of an ensemble of oscillators is an emergent phenomenon present in several complex systems, ranging from social and physical to biological and technological systems. The most successful approach to describe how coherent behavior emerges in these complex systems is given by the paradigmatic Kuramoto model. This model has been traditionally studied in complete graphs. However, besides being intrinsically dynamical, complex systems present very heterogeneous structure, which can be represented as complex networks. This report is dedicated to review main contributions in the field of synchronization in networks of Kuramoto oscillators. In particular, we provide an overview of the impact of network patterns on the local and global dynamics of coupled phase oscillators. We cover many relevant topics, which encompass a description of the most used analytical approaches and the analysis of several numerical results. Furthermore, we discuss recent developments on variations of the Kuramoto model in networks, including the presence of noise and inertia. The rich potential for applications is discussed for special fields in engineering, neuroscience, physics and Earth science. Finally, we conclude by discussing problems that remain open after the last decade of intensive research on the Kuramoto model and point out some promising directions for future research.

  2. Social Networking: It's Not What You Think

    NASA Technical Reports Server (NTRS)

    Jones, Kevin D.

    2010-01-01

    This slide presentation reviews some of the current uses of the social networking sites available on the internet. It list some of the skills that are now considered obsolete and reviews the major social networking sites.

  3. Multilevel Complex Networks and Systems

    NASA Astrophysics Data System (ADS)

    Caldarelli, Guido

    2014-03-01

    Network theory has been a powerful tool to model isolated complex systems. However, the classical approach does not take into account the interactions often present among different systems. Hence, the scientific community is nowadays concentrating the efforts on the foundations of new mathematical tools for understanding what happens when multiple networks interact. The case of economic and financial networks represents a paramount example of multilevel networks. In the case of trade, trade among countries the different levels can be described by the different granularity of the trading relations. Indeed, we have now data from the scale of consumers to that of the country level. In the case of financial institutions, we have a variety of levels at the same scale. For example one bank can appear in the interbank networks, ownership network and cds networks in which the same institution can take place. In both cases the systemically important vertices need to be determined by different procedures of centrality definition and community detection. In this talk I will present some specific cases of study related to these topics and present the regularities found. Acknowledged support from EU FET Project ``Multiplex'' 317532.

  4. Physical controllability of complex networks

    NASA Astrophysics Data System (ADS)

    Wang, Le-Zhi; Chen, Yu-Zhong; Wang, Wen-Xu; Lai, Ying-Cheng

    2017-01-01

    A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control.

  5. Physical controllability of complex networks

    PubMed Central

    Wang, Le-Zhi; Chen, Yu-Zhong; Wang, Wen-Xu; Lai, Ying-Cheng

    2017-01-01

    A challenging problem in network science is to control complex networks. In existing frameworks of structural or exact controllability, the ability to steer a complex network toward any desired state is measured by the minimum number of required driver nodes. However, if we implement actual control by imposing input signals on the minimum set of driver nodes, an unexpected phenomenon arises: due to computational or experimental error there is a great probability that convergence to the final state cannot be achieved. In fact, the associated control cost can become unbearably large, effectively preventing actual control from being realized physically. The difficulty is particularly severe when the network is deemed controllable with a small number of drivers. Here we develop a physical controllability framework based on the probability of achieving actual control. Using a recently identified fundamental chain structure underlying the control energy, we offer strategies to turn physically uncontrollable networks into physically controllable ones by imposing slightly augmented set of input signals on properly chosen nodes. Our findings indicate that, although full control can be theoretically guaranteed by the prevailing structural controllability theory, it is necessary to balance the number of driver nodes and control cost to achieve physical control. PMID:28074900

  6. Markovian dynamics on complex reaction networks

    NASA Astrophysics Data System (ADS)

    Goutsias, J.; Jenkinson, G.

    2013-08-01

    Complex networks, comprised of individual elements that interact with each other through reaction channels, are ubiquitous across many scientific and engineering disciplines. Examples include biochemical, pharmacokinetic, epidemiological, ecological, social, neural, and multi-agent networks. A common approach to modeling such networks is by a master equation that governs the dynamic evolution of the joint probability mass function of the underlying population process and naturally leads to Markovian dynamics for such process. Due however to the nonlinear nature of most reactions and the large size of the underlying state-spaces, computation and analysis of the resulting stochastic population dynamics is a difficult task. This review article provides a coherent and comprehensive coverage of recently developed approaches and methods to tackle this problem. After reviewing a general framework for modeling Markovian reaction networks and giving specific examples, the authors present numerical and computational techniques capable of evaluating or approximating the solution of the master equation, discuss a recently developed approach for studying the stationary behavior of Markovian reaction networks using a potential energy landscape perspective, and provide an introduction to the emerging theory of thermodynamic analysis of such networks. Three representative problems of opinion formation, transcription regulation, and neural network dynamics are used as illustrative examples.

  7. Collaboration in the School Social Network

    ERIC Educational Resources Information Center

    Schultz-Jones, Barbara

    2009-01-01

    Social networks are fundamental to all people. Their social network describes how they are connected to others: close relationships, peripheral relationships, and those relationships that help connect them to other people, events, or things. As information specialists, school librarians develop a multidimensional social network that enables them…

  8. Organizational Application of Social Networking Information Technologies

    ERIC Educational Resources Information Center

    Reppert, Jeffrey R.

    2012-01-01

    The focus of this qualitative research study using the Delphi method is to provide a framework for leaders to develop their own social networks. By exploring concerns in four areas, leaders may be able to better plan, implement, and manage social networking systems in organizations. The areas addressed are: (a) social networking using…

  9. Challenges of Health Games in the Social Network Environment.

    PubMed

    Paredes, Hugo; Pinho, Anabela; Zagalo, Nelson

    2012-04-01

    Virtual communities and their benefits have been widely exploited to support patients, caregivers, families, and healthcare providers. The complexity of the social organization evolved the concept of virtual community to social networks, exploring the establishment of ties and relations between people. These technological platforms provide a way to keep up with one's connections network, through a set of communication and interaction tools. Games, as social interactive technologies, have great potential, ensuring a supportive community and thereby reducing social isolation. Serious social health games bring forward several research challenges. This article examines the potential benefits of the triad "health-serious games-social networks" and discusses some research challenges and opportunities of the liaison of serious health games and social networks.

  10. Googling social interactions: web search engine based social network construction.

    PubMed

    Lee, Sang Hoon; Kim, Pan-Jun; Ahn, Yong-Yeol; Jeong, Hawoong

    2010-07-21

    Social network analysis has long been an untiring topic of sociology. However, until the era of information technology, the availability of data, mainly collected by the traditional method of personal survey, was highly limited and prevented large-scale analysis. Recently, the exploding amount of automatically generated data has completely changed the pattern of research. For instance, the enormous amount of data from so-called high-throughput biological experiments has introduced a systematic or network viewpoint to traditional biology. Then, is "high-throughput" sociological data generation possible? Google, which has become one of the most influential symbols of the new Internet paradigm within the last ten years, might provide torrents of data sources for such study in this (now and forthcoming) digital era. We investigate social networks between people by extracting information on the Web and introduce new tools of analysis of such networks in the context of statistical physics of complex systems or socio-physics. As a concrete and illustrative example, the members of the 109th United States Senate are analyzed and it is demonstrated that the methods of construction and analysis are applicable to various other weighted networks.

  11. Preferential urn model and nongrowing complex networks.

    PubMed

    Ohkubo, Jun; Yasuda, Muneki; Tanaka, Kazuyuki

    2005-12-01

    A preferential urn model, which is based on the concept "the rich get richer," is proposed. From a relationship between a nongrowing model for complex networks and the preferential urn model in regard to degree distributions, it is revealed that a fitness parameter in the nongrowing model is interpreted as an inverse local temperature in the preferential urn model. Furthermore, it is clarified that the preferential urn model with randomness generates a fat-tailed occupation distribution; the concept of the local temperature enables us to understand the fat-tailed occupation distribution intuitively. Since the preferential urn model is a simple stochastic model, it can be applied to research on not only the nongrowing complex networks, but also many other fields such as econophysics and social sciences.

  12. Role models for complex networks

    NASA Astrophysics Data System (ADS)

    Reichardt, J.; White, D. R.

    2007-11-01

    We present a framework for automatically decomposing (“block-modeling”) the functional classes of agents within a complex network. These classes are represented by the nodes of an image graph (“block model”) depicting the main patterns of connectivity and thus functional roles in the network. Using a first principles approach, we derive a measure for the fit of a network to any given image graph allowing objective hypothesis testing. From the properties of an optimal fit, we derive how to find the best fitting image graph directly from the network and present a criterion to avoid overfitting. The method can handle both two-mode and one-mode data, directed and undirected as well as weighted networks and allows for different types of links to be dealt with simultaneously. It is non-parametric and computationally efficient. The concepts of structural equivalence and modularity are found as special cases of our approach. We apply our method to the world trade network and analyze the roles individual countries play in the global economy.

  13. Using learning networks to understand complex systems: a case study of biological, geophysical and social research in the Amazon.

    PubMed

    Barlow, Jos; Ewers, Robert M; Anderson, Liana; Aragao, Luiz E O C; Baker, Tim R; Boyd, Emily; Feldpausch, Ted R; Gloor, Emanuel; Hall, Anthony; Malhi, Yadvinder; Milliken, William; Mulligan, Mark; Parry, Luke; Pennington, Toby; Peres, Carlos A; Phillips, Oliver L; Roman-Cuesta, Rosa Maria; Tobias, Joseph A; Gardner, Toby A

    2011-05-01

    Developing high-quality scientific research will be most effective if research communities with diverse skills and interests are able to share information and knowledge, are aware of the major challenges across disciplines, and can exploit economies of scale to provide robust answers and better inform policy. We evaluate opportunities and challenges facing the development of a more interactive research environment by developing an interdisciplinary synthesis of research on a single geographic region. We focus on the Amazon as it is of enormous regional and global environmental importance and faces a highly uncertain future. To take stock of existing knowledge and provide a framework for analysis we present a set of mini-reviews from fourteen different areas of research, encompassing taxonomy, biodiversity, biogeography, vegetation dynamics, landscape ecology, earth-atmosphere interactions, ecosystem processes, fire, deforestation dynamics, hydrology, hunting, conservation planning, livelihoods, and payments for ecosystem services. Each review highlights the current state of knowledge and identifies research priorities, including major challenges and opportunities. We show that while substantial progress is being made across many areas of scientific research, our understanding of specific issues is often dependent on knowledge from other disciplines. Accelerating the acquisition of reliable and contextualized knowledge about the fate of complex pristine and modified ecosystems is partly dependent on our ability to exploit economies of scale in shared resources and technical expertise, recognise and make explicit interconnections and feedbacks among sub-disciplines, increase the temporal and spatial scale of existing studies, and improve the dissemination of scientific findings to policy makers and society at large. Enhancing interaction among research efforts is vital if we are to make the most of limited funds and overcome the challenges posed by addressing large

  14. Statistical physics of complex networks

    NASA Astrophysics Data System (ADS)

    Xie, Huafeng

    We live in a connected world. It is of great practical importance and intellectual appeal to understand the networks surrounding us. In this work we study ranking of the nodes in complex networks. In large networks such as World Wide Web (WWW) and citation networks of scientific literature, searching by keywords is a common practice to retrieve useful information. On the WWW, apart from the contents of webpages, the topology of the network itself can be a rich source of information about their relative importance and relevancy to the search query. It is the effective utilization of this topological information [50] which advanced the Google search engine to its present position of the most popular tool on the WWW. The World-Wide Web (WWW) is characterized by a strong community structure in which communities of webpages are densely interconnected by hyperlinks. We study how such network architecture affects the average Google ranking of individual webpages in the community. Using a mean-field approximation, we quantify how the average Google rank of community's webpages depends on the degree to which it is isolated from the rest of the world in both incoming and outgoing directions, and alpha -- the only intrinsic parameter of Google's PageRank algorithm. We proceed with numerical study of simulated networks and empirical study of several internal web-communities within two US universities. The predictions of our mean-field treatment were qualitatively verified in those real-life networks. Furthermore, the value alpha = 0.15 used by Google seems to be optimized for the degree of isolation of communities as they exist in the actual WWW. We then extend Google's PageRank algorithm to citation networks of scientific literature. Unlike hyperlinks, citations cannot be updated after the point of publication. This results in strong aging characteristics of citation networks that affect the performance of the PageRank algorithm. To rectify this we modify the Page

  15. Social network models predict movement and connectivity in ecological landscapes

    USGS Publications Warehouse

    Fletcher, R.J.; Acevedo, M.A.; Reichert, Brian E.; Pias, Kyle E.; Kitchens, W.M.

    2011-01-01

    Network analysis is on the rise across scientific disciplines because of its ability to reveal complex, and often emergent, patterns and dynamics. Nonetheless, a growing concern in network analysis is the use of limited data for constructing networks. This concern is strikingly relevant to ecology and conservation biology, where network analysis is used to infer connectivity across landscapes. In this context, movement among patches is the crucial parameter for interpreting connectivity but because of the difficulty of collecting reliable movement data, most network analysis proceeds with only indirect information on movement across landscapes rather than using observed movement to construct networks. Statistical models developed for social networks provide promising alternatives for landscape network construction because they can leverage limited movement information to predict linkages. Using two mark-recapture datasets on individual movement and connectivity across landscapes, we test whether commonly used network constructions for interpreting connectivity can predict actual linkages and network structure, and we contrast these approaches to social network models. We find that currently applied network constructions for assessing connectivity consistently, and substantially, overpredict actual connectivity, resulting in considerable overestimation of metapopulation lifetime. Furthermore, social network models provide accurate predictions of network structure, and can do so with remarkably limited data on movement. Social network models offer a flexible and powerful way for not only understanding the factors influencing connectivity but also for providing more reliable estimates of connectivity and metapopulation persistence in the face of limited data.

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

    PubMed

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

    2015-03-02

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

  17. Congestion phenomena on complex networks.

    PubMed

    De Martino, Daniele; Dall'asta, Luca; Bianconi, Ginestra; Marsili, Matteo

    2009-01-01

    We define a minimal model of traffic flows in complex networks in order to study the trade-off between topological-based and traffic-based routing strategies. The resulting collective behavior is obtained analytically for an ensemble of uncorrelated networks and summarized in a rich phase diagram presenting second-order as well as first-order phase transitions between a free-flow phase and a congested phase. We find that traffic control improves global performance, enlarging the free-flow region in parameter space only in heterogeneous networks. Traffic control introduces nonlinear effects and, beyond a critical strength, may trigger the appearance of a congested phase in a discontinuous manner. The model also reproduces the crossover in the scaling of traffic fluctuations empirically observed on the Internet.

  18. Mathematical modelling of complex contagion on clustered networks

    NASA Astrophysics Data System (ADS)

    O'sullivan, David J.; O'Keeffe, Gary; Fennell, Peter; Gleeson, James

    2015-09-01

    The spreading of behavior, such as the adoption of a new innovation, is influenced bythe structure of social networks that interconnect the population. In the experiments of Centola (Science, 2010), adoption of new behavior was shown to spread further and faster across clustered-lattice networks than across corresponding random networks. This implies that the “complex contagion” effects of social reinforcement are important in such diffusion, in contrast to “simple” contagion models of disease-spread which predict that epidemics would grow more efficiently on random networks than on clustered networks. To accurately model complex contagion on clustered networks remains a challenge because the usual assumptions (e.g. of mean-field theory) regarding tree-like networks are invalidated by the presence of triangles in the network; the triangles are, however, crucial to the social reinforcement mechanism, which posits an increased probability of a person adopting behavior that has been adopted by two or more neighbors. In this paper we modify the analytical approach that was introduced by Hebert-Dufresne et al. (Phys. Rev. E, 2010), to study disease-spread on clustered networks. We show how the approximation method can be adapted to a complex contagion model, and confirm the accuracy of the method with numerical simulations. The analytical results of the model enable us to quantify the level of social reinforcement that is required to observe—as in Centola’s experiments—faster diffusion on clustered topologies than on random networks.

  19. Text Mining in Social Networks

    NASA Astrophysics Data System (ADS)

    Aggarwal, Charu C.; Wang, Haixun

    Social networks are rich in various kinds of contents such as text and multimedia. The ability to apply text mining algorithms effectively in the context of text data is critical for a wide variety of applications. Social networks require text mining algorithms for a wide variety of applications such as keyword search, classification, and clustering. While search and classification are well known applications for a wide variety of scenarios, social networks have a much richer structure both in terms of text and links. Much of the work in the area uses either purely the text content or purely the linkage structure. However, many recent algorithms use a combination of linkage and content information for mining purposes. In many cases, it turns out that the use of a combination of linkage and content information provides much more effective results than a system which is based purely on either of the two. This paper provides a survey of such algorithms, and the advantages observed by using such algorithms in different scenarios. We also present avenues for future research in this area.

  20. Control Capacity in Complex Networks

    NASA Astrophysics Data System (ADS)

    Jia, Tao; Liu, Yang-Yu; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2012-02-01

    By combining tools from control theory and network science, an efficient methodology was proposed to identify the minimum sets of driver nodes, whose time-dependent control can guide the whole network to any desired final state. Yet, this minimum driver set (MDS) is usually not unique, but one can often achieve multiple potential control configurations with the same number of driver nodes. Given that some nodes may appear in some MDSs but not in other, a crucial question remain unanswered: what is the role of individual node in controlling a complex system? We first classify a node as critical, redundant, or ordinary if it appears in all, no, or some MDSs. Then we introduce the concept of control capacity as a measure of the frequency that a node is in the MDSs, which quantifies the importance of a given node in maintaining Controllability. To avoid impractical enumeration of all MDSs, we propose an algorithm that uniformly samples the MDS. We use it to explore the control capacity of nodes in complex networks and study how it is related to other characteristics of the network topology.

  1. Social network determinants of depression

    PubMed Central

    Rosenquist, JN; Fowler, JH; Christakis, NA

    2013-01-01

    The etiology of depression has long been thought to include social environmental factors. To quantitatively explore the novel possibility of person-to-person spread and network-level determination of depressive symptoms, analyses were performed on a densely interconnected social network of 12 067 people assessed repeatedly over 32 years as part of the Framingham Heart Study. Longitudinal statistical models were used to examine whether depressive symptoms in one person were associated with similar scores in friends, co-workers, siblings, spouses and neighbors. Depressive symptoms were assessed using CES-D scores that were available for subjects in three waves measured between 1983 and 2001. Results showed both low and high CES-D scores (and classification as being depressed) in a given period were strongly correlated with such scores in one's friends and neighbors. This association extended up to three degrees of separation (to one's friends’ friends’ friends). Female friends appear to be especially influential in the spread of depression from one person to another. The results are robust to multiple network simulation and estimation methods, suggesting that network phenomena appear relevant to the epidemiology of depression and would benefit from further study. PMID:20231839

  2. Community dynamics in social networks

    NASA Astrophysics Data System (ADS)

    Palla, Gergely; Barabási, Albert-László; Vicsek, Tamás

    2007-06-01

    We study the statistical properties of community dynamics in large social networks, where the evolving communities are obtained from subsequent snapshots of the modular structure. Such cohesive groups of people can grow by recruiting new members, or contract by loosing members; two (or more) groups may merge into a single community, while a large enough social group can split into several smaller ones; new communities are born and old ones may disappear. We find significant difference between the behaviour of smaller collaborative or friendship circles and larger communities, eg. institutions. Social groups containing only a few members persist longer on average when the fluctuations of the members is small. In contrast, we find that the condition for stability for large communities is continuous changes in their membership, allowing for the possibility that after some time practically all members are exchanged.

  3. Benford’s Distribution in Complex Networks

    NASA Astrophysics Data System (ADS)

    Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K.

    2016-10-01

    Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford’s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford’s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford’s law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford’s distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford’s law for scalefree networks but only for very narrow range of their parameters.

  4. Benford’s Distribution in Complex Networks

    PubMed Central

    Morzy, Mikołaj; Kajdanowicz, Tomasz; Szymański, Bolesław K.

    2016-01-01

    Many collections of numbers do not have a uniform distribution of the leading digit, but conform to a very particular pattern known as Benford’s distribution. This distribution has been found in numerous areas such as accounting data, voting registers, census data, and even in natural phenomena. Recently it has been reported that Benford’s law applies to online social networks. Here we introduce a set of rigorous tests for adherence to Benford’s law and apply it to verification of this claim, extending the scope of the experiment to various complex networks and to artificial networks created by several popular generative models. Our findings are that neither for real nor for artificial networks there is sufficient evidence for common conformity of network structural properties with Benford’s distribution. We find very weak evidence suggesting that three measures, degree centrality, betweenness centrality and local clustering coefficient, could adhere to Benford’s law for scalefree networks but only for very narrow range of their parameters. PMID:27748398

  5. Robustness surfaces of complex networks

    NASA Astrophysics Data System (ADS)

    Manzano, Marc; Sahneh, Faryad; Scoglio, Caterina; Calle, Eusebi; Marzo, Jose Luis

    2014-09-01

    Despite the robustness of complex networks has been extensively studied in the last decade, there still lacks a unifying framework able to embrace all the proposed metrics. In the literature there are two open issues related to this gap: (a) how to dimension several metrics to allow their summation and (b) how to weight each of the metrics. In this work we propose a solution for the two aforementioned problems by defining the R*-value and introducing the concept of robustness surface (Ω). The rationale of our proposal is to make use of Principal Component Analysis (PCA). We firstly adjust to 1 the initial robustness of a network. Secondly, we find the most informative robustness metric under a specific failure scenario. Then, we repeat the process for several percentage of failures and different realizations of the failure process. Lastly, we join these values to form the robustness surface, which allows the visual assessment of network robustness variability. Results show that a network presents different robustness surfaces (i.e., dissimilar shapes) depending on the failure scenario and the set of metrics. In addition, the robustness surface allows the robustness of different networks to be compared.

  6. Linking social complexity and vocal complexity: a parid perspective

    PubMed Central

    Krams, Indrikis; Krama, Tatjana; Freeberg, Todd M.; Kullberg, Cecilia; Lucas, Jeffrey R.

    2012-01-01

    The Paridae family (chickadees, tits and titmice) is an interesting avian group in that species vary in important aspects of their social structure and many species have large and complex vocal repertoires. For this reason, parids represent an important set of species for testing the social complexity hypothesis for vocal communication—the notion that as groups increase in social complexity, there is a need for increased vocal complexity. Here, we describe the hypothesis and some of the early evidence that supported the hypothesis. Next, we review literature on social complexity and on vocal complexity in parids, and describe some of the studies that have made explicit tests of the social complexity hypothesis in one parid—Carolina chickadees, Poecile carolinensis. We conclude with a discussion, primarily from a parid perspective, of the benefits and costs of grouping and of physiological factors that might mediate the relationship between social complexity and changes in signalling behaviour. PMID:22641826

  7. Social Insects: A Model System for Network Dynamics

    NASA Astrophysics Data System (ADS)

    Charbonneau, Daniel; Blonder, Benjamin; Dornhaus, Anna

    Social insect colonies (ants, bees, wasps, and termites) show sophisticated collective problem-solving in the face of variable constraints. Individuals exchange information and materials such as food. The resulting network structure and dynamics can inform us about the mechanisms by which the insects achieve particular collective behaviors and these can be transposed to man-made and social networks. We discuss how network analysis can answer important questions about social insects, such as how effective task allocation or information flow is realized. We put forward the idea that network analysis methods are under-utilized in social insect research, and that they can provide novel ways to view the complexity of collective behavior, particularly if network dynamics are taken into account. To illustrate this, we present an example of network tasks performed by ant workers, linked by instances of workers switching from one task to another. We show how temporal network analysis can propose and test new hypotheses on mechanisms of task allocation, and how adding temporal elements to static networks can drastically change results. We discuss the benefits of using social insects as models for complex systems in general. There are multiple opportunities emergent technologies and analysis methods in facilitating research on social insect network. The potential for interdisciplinary work could significantly advance diverse fields such as behavioral ecology, computer sciences, and engineering.

  8. "Hidden" social networks in behavior change interventions.

    PubMed

    Hunter, Ruth F; McAneney, Helen; Davis, Michael; Tully, Mark A; Valente, Thomas W; Kee, Frank

    2015-03-01

    We investigated whether "hidden" (or unobserved) social networks were evident in a 2011 physical activity behavior change intervention in Belfast, Northern Ireland. Results showed evidence of unobserved social networks in the intervention and illustrated how the network evolved over short periods and affected behavior. Behavior change interventions should account for the interaction among participants (i.e., social networks) and how such interactions affect intervention outcome.

  9. Detecting Change in Longitudinal Social Networks

    DTIC Science & Technology

    2011-01-01

    marketing campaigns and media on social behavior. Initial Construct populations, social and knowledge networks, can be hypothetical or real (Carley...patent data bases, phone-networks, email- based-networks, social- media networks and more. Page 6 of 37 Current methods of change detection in...CUSUM C Sta measured fo o be successf Average Bet ct either incre or each socia g increases in the data for fective for ch ork. tistic Over Tim

  10. Topological implications of negative curvature for biological and social networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; DasGupta, Bhaskar; Mobasheri, Nasim

    2014-03-01

    Network measures that reflect the most salient properties of complex large-scale networks are in high demand in the network research community. In this paper we adapt a combinatorial measure of negative curvature (also called hyperbolicity) to parametrized finite networks, and show that a variety of biological and social networks are hyperbolic. This hyperbolicity property has strong implications on the higher-order connectivity and other topological properties of these networks. Specifically, we derive and prove bounds on the distance among shortest or approximately shortest paths in hyperbolic networks. We describe two implications of these bounds to crosstalk in biological networks, and to the existence of central, influential neighborhoods in both biological and social networks.

  11. Community evolution mining and analysis in social network

    NASA Astrophysics Data System (ADS)

    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.

  12. The Social Classroom: Integrating Social Network Use in Education

    ERIC Educational Resources Information Center

    Mallia, Gorg, Ed.

    2014-01-01

    As technology is being integrated into educational processes, teachers are searching for new ways to enhance student motivation and learning. Through shared experiences and the results of empirical research, educators can ease social networking sites into instructional usage. "The Social Classroom: Integrating Social Network Use in…

  13. Social Media and Social Networking Applications for Teaching and Learning

    ERIC Educational Resources Information Center

    Yeo, Michelle Mei Ling

    2014-01-01

    This paper aims to better understand the experiences of the youth and the educators with the tapping of social media like YouTube videos and the social networking application of Facebook for teaching and learning. This paper is interested in appropriating the benefits of leveraging of social media and networking applications like YouTube and…

  14. Individual choices in dynamic networks: an experiment on social preferences.

    PubMed

    van Dolder, Dennie; Buskens, Vincent

    2014-01-01

    Game-theoretic models of network formation typically assume that people create relations so as to maximize their own outcome in the network. Recent experiments on network formation suggest that the assumption of self-interest might be unwarranted and that social preferences, such as altruism and inequality aversion, play a role in the formation of social networks. We developed an experiment to systematically investigate whether people show preferences for outcomes of others during network formation. We find that such preferences play a role when network decisions degenerate to simple two-person decision tasks. In more complex environments, however, we find little evidence for social preferences as a significant decision criterion. Furthermore, we find some evidence for farsighted behavior in network formation.

  15. Individual Choices in Dynamic Networks: An Experiment on Social Preferences

    PubMed Central

    van Dolder, Dennie; Buskens, Vincent

    2014-01-01

    Game-theoretic models of network formation typically assume that people create relations so as to maximize their own outcome in the network. Recent experiments on network formation suggest that the assumption of self-interest might be unwarranted and that social preferences, such as altruism and inequality aversion, play a role in the formation of social networks. We developed an experiment to systematically investigate whether people show preferences for outcomes of others during network formation. We find that such preferences play a role when network decisions degenerate to simple two-person decision tasks. In more complex environments, however, we find little evidence for social preferences as a significant decision criterion. Furthermore, we find some evidence for farsighted behavior in network formation. PMID:24732665

  16. Will Learning Social Inclusion Assist Rural Networks

    ERIC Educational Resources Information Center

    Marchant, Jillian

    2013-01-01

    Current research on social networks in some rural communities reports continuing demise despite efforts to build resilient communities. Several factors are identified as contributing to social decline including globalisation and rural social characteristics. Particular rural social characteristics, such as strong social bonds among members of…

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

    PubMed

    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.

  18. Anomalous Transport in Complex Networks

    NASA Astrophysics Data System (ADS)

    Lopez, Eduardo; Buldyrev, Sergey; Havlin, Shlomo; Stanley, H. Eugene

    2005-03-01

    To study transport properties of complex networks, we analyze the equivalent conductance G between two arbitrarily chosen nodes of random scale-free networks with degree distribution P(k)˜k^-λ in which each link has the same unit resistance. We predict a broad range of values of G, with a power-law tail distribution φSF(G)˜G^-gG, where gG=2λ-1, and confirm our predictions by simulations. The power-law tail in φSF(G) leads to large values of G, thereby significantly improving the transport in scale-free networks, compared to Erdos-R'enyi random graphs where the tail of the conductivity distribution decays exponentially. Based on a simple physical ``transport backbone'' picture we show that the conductances are well approximated by ckAkB/(kA+kB) for any pair of nodes A and B with degrees kA and kB. Thus, a single parameter c characterizes transport on scale-free networks.

  19. Location Privacy Protection on Social Networks

    NASA Astrophysics Data System (ADS)

    Zhan, Justin; Fang, Xing

    Location information is considered as private in many scenarios. Protecting location information on mobile ad-hoc networks has attracted much research in past years. However, location information protection on social networks has not been paid much attention. In this paper, we present a novel location privacy protection approach on the basis of user messages in social networks. Our approach grants flexibility to users by offering them multiple protecting options. To the best of our knowledge, this is the first attempt to protect social network users' location information via text messages. We propose five algorithms for location privacy protection on social networks.

  20. Socioecological regime shifts in the setting of complex social interactions

    NASA Astrophysics Data System (ADS)

    Sugiarto, Hendrik Santoso; Chung, Ning Ning; Lai, Choy Heng; Chew, Lock Yue

    2015-06-01

    The coupling between social and ecological system has become more ubiquitous and predominant in the current era. The strong interaction between these systems can bring about regime shifts which in the extreme can lead to the collapse of social cooperation and the extinction of ecological resources. In this paper, we study the occurrence of such regime shifts in the context of a coupled social-ecological system where social cooperation is established by means of sanction that punishes local selfish act and promotes norms that prescribe nonexcessive resource extraction. In particular, we investigate the role of social networks on social-ecological regimes shift and the corresponding hysteresis effects caused by the local ostracism mechanism under different social and ecological parameters. Our results show that a lowering of network degree reduces the hysteresis effect and also alters the tipping point, which is duly verified by our numerical results and analytical estimation. Interestingly, the hysteresis effect is found to be stronger in scale-free network in comparison with random network even when both networks have the same average degree. These results provide deeper insights into the resilience of these systems, and can have important implications on the management of coupled social-ecological systems with complex social interactions.

  1. Good Communication: The Other Social Network for Successful IT Organizations

    ERIC Educational Resources Information Center

    Trubitt, Lisa; Overholtzer, Jeff

    2009-01-01

    Social networks of the electronic variety have become thoroughly embedded in contemporary culture. People have woven these networks into their daily routines, using Facebook, Twitter, LinkedIn, online gaming environments, and other tools to build and maintain complex webs of professional and personal relationships. Chief Information Officers…

  2. Aging, frailty and complex networks.

    PubMed

    Mitnitski, A B; Rutenberg, A D; Farrell, S; Rockwood, K

    2017-03-02

    When people age their mortality rate increases exponentially, following Gompertz's law. Even so, individuals do not die from old age. Instead, they accumulate age-related illnesses and conditions and so become increasingly vulnerable to death from various external and internal stressors. As a measure of such vulnerability, frailty can be quantified using the frailty index (FI). Larger values of the FI are strongly associated with mortality and other adverse health outcomes. This association, and the insensitivity of the FI to the particular health variables that are included in its construction, makes it a powerful, convenient, and increasingly popular integrative health measure. Still, little is known about why the FI works so well. Our group has recently developed a theoretical network model of health deficits to better understand how changes in health are captured by the FI. In our model, health-related variables are represented by the nodes of a complex network. The network has a scale-free shape or "topology": a few nodes have many connections with other nodes, whereas most nodes have few connections. These nodes can be in two states, either damaged or undamaged. Transitions between damaged and non-damaged states are governed by the stochastic environment of individual nodes. Changes in the degree of damage of connected nodes change the local environment and make further damage more likely. Our model shows how age-dependent acceleration of the FI and of mortality emerges, even without specifying an age-damage relationship or any other time-dependent parameter. We have also used our model to assess how informative individual deficits are with respect to mortality. We find that the information is larger for nodes that are well connected than for nodes that are not. The model supports the idea that aging occurs as an emergent phenomenon, and not as a result of age-specific programming. Instead, aging reflects how damage propagates through a complex network of

  3. Social Network Theory and Educational Change

    ERIC Educational Resources Information Center

    Daly, Alan J., Ed.

    2010-01-01

    "Social Network Theory and Educational Change" offers a provocative and fascinating exploration of how social networks in schools can impede or facilitate the work of education reform. Drawing on the work of leading scholars, the book comprises a series of studies examining networks among teachers and school leaders, contrasting formal…

  4. Emergence of communities and diversity in social networks.

    PubMed

    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.

  5. Emergence of communities and diversity in social networks

    PubMed Central

    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

  6. Social Networks and Mourning: A Comparative Approach.

    ERIC Educational Resources Information Center

    Rubin, Nissan

    1990-01-01

    Suggests using social network theory to explain varieties of mourning behavior in different societies. Compares participation in funeral ceremonies of members of different social circles in American society and Israeli kibbutz. Concludes that results demonstrated validity of concepts deriving from social network analysis in study of bereavement,…

  7. Egocentric Social Network Analysis of Pathological Gambling

    PubMed Central

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

    2012-01-01

    Aims To apply social network analysis (SNA) to investigate whether frequency and severity of gambling problems were associated with different network characteristics among friends, family, and co-workers. is an innovative way to look at relationships among individuals; the current study was the first to our knowledge to apply SNA to gambling behaviors. Design Egocentric social network analysis was used to formally characterize the relationships between social network characteristics and gambling pathology. Setting Laboratory-based questionnaire and interview administration. Participants Forty frequent gamblers (22 non-pathological gamblers, 18 pathological gamblers) were recruited from the community. Findings The SNA revealed significant social network compositional differences between the two groups: pathological gamblers (PGs) had more gamblers, smokers, and drinkers in their social networks than did nonpathological gamblers (NPGs). PGs had more individuals in their network with whom they personally gambled, smoked, and drank with than those with who were NPG. Network ties were closer to individuals in their networks who gambled, smoked, and drank more frequently. Associations between gambling severity and structural network characteristics were not significant. Conclusions Pathological gambling is associated with compositional but not structural differences in social networks. Pathological gamblers differ from non-pathological gamblers in the number of gamblers, smokers, and drinkers in their social networks. Homophily within the networks also indicates that gamblers tend to be closer with other gamblers. This homophily may serve to reinforce addictive behaviors, and may suggest avenues for future study or intervention. PMID:23072641

  8. Bayesian Networks for Social Modeling

    SciTech Connect

    Whitney, Paul D.; White, Amanda M.; Walsh, Stephen J.; Dalton, Angela C.; Brothers, Alan J.

    2011-03-28

    This paper describes a body of work developed over the past five years. The work addresses the use of Bayesian network (BN) models for representing and predicting social/organizational behaviors. The topics covered include model construction, validation, and use. These topics show the bulk of the lifetime of such model, beginning with construction, moving to validation and other aspects of model ‘critiquing’, and finally demonstrating how the modeling approach might be used to inform policy analysis. To conclude, we discuss limitations of using BN for this activity and suggest remedies to address those limitations. The primary benefits of using a well-developed computational, mathematical, and statistical modeling structure, such as BN, are 1) there are significant computational, theoretical and capability bases on which to build 2) ability to empirically critique the model, and potentially evaluate competing models for a social/behavioral phenomena.

  9. Improved community model for social networks based on social mobility

    NASA Astrophysics Data System (ADS)

    Lu, Zhe-Ming; Wu, Zhen; Luo, Hao; Wang, Hao-Xian

    2015-07-01

    This paper proposes an improved community model for social networks based on social mobility. The relationship between the group distribution and the community size is investigated in terms of communication rate and turnover rate. The degree distributions, clustering coefficients, average distances and diameters of networks are analyzed. Experimental results demonstrate that the proposed model possesses the small-world property and can reproduce social networks effectively and efficiently.

  10. Detecting social transmission in networks.

    PubMed

    Hoppitt, William; Boogert, Neeltje J; Laland, Kevin N

    2010-04-21

    In recent years researchers have drawn attention to a need for new methods with which to identify the spread of behavioural innovations through social transmission in animal populations. Network-based analyses seek to recognise diffusions mediated by social learning by detecting a correspondence between patterns of association and the flow of information through groups. Here we introduce a new order of acquisition diffusion analysis (OADA) and develop established time of acquisition diffusion analysis (TADA) methods further. Through simulation we compare the merits of these and other approaches, demonstrating that OADA and TADA have greater power and lower Type I error rates than available alternatives, and specifying when each approach should be deployed. We illustrate the new methods by applying them to reanalyse an established dataset corresponding to the diffusion of foraging innovations in starlings, where OADA and TADA detect social transmission that hitherto had been missed. The methods are potentially widely applicable by researchers wishing to detect social learning in natural and captive populations of animals, and to facilitate this we provide code to implement OADA and TADA in the statistical package R.

  11. Psychology and social networks: a dynamic network theory perspective.

    PubMed

    Westaby, James D; Pfaff, Danielle L; Redding, Nicholas

    2014-04-01

    Research on social networks has grown exponentially in recent years. However, despite its relevance, the field of psychology has been relatively slow to explain the underlying goal pursuit and resistance processes influencing social networks in the first place. In this vein, this article aims to demonstrate how a dynamic network theory perspective explains the way in which social networks influence these processes and related outcomes, such as goal achievement, performance, learning, and emotional contagion at the interpersonal level of analysis. The theory integrates goal pursuit, motivation, and conflict conceptualizations from psychology with social network concepts from sociology and organizational science to provide a taxonomy of social network role behaviors, such as goal striving, system supporting, goal preventing, system negating, and observing. This theoretical perspective provides psychologists with new tools to map social networks (e.g., dynamic network charts), which can help inform the development of change interventions. Implications for social, industrial-organizational, and counseling psychology as well as conflict resolution are discussed, and new opportunities for research are highlighted, such as those related to dynamic network intelligence (also known as cognitive accuracy), levels of analysis, methodological/ethical issues, and the need to theoretically broaden the study of social networking and social media behavior. (PsycINFO Database Record (c) 2014 APA, all rights reserved).

  12. Advances in the Theory of Complex Networks

    NASA Astrophysics Data System (ADS)

    Peruani, Fernando

    An exhaustive and comprehensive review on the theory of complex networks would imply nowadays a titanic task, and it would result in a lengthy work containing plenty of technical details of arguable relevance. Instead, this chapter addresses very briefly the ABC of complex network theory, visiting only the hallmarks of the theoretical founding, to finally focus on two of the most interesting and promising current research problems: the study of dynamical processes on transportation networks and the identification of communities in complex networks.

  13. The Correlation Fractal Dimension of Complex Networks

    NASA Astrophysics Data System (ADS)

    Wang, Xingyuan; Liu, Zhenzhen; Wang, Mogei

    2013-05-01

    The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.

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

    DTIC Science & Technology

    2015-12-01

    use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations and selectively target key elements...data to improve SC. 14. SUBJECT TERMS social network analysis, dark networks, light networks, dim networks, security cooperation, Southeast Asia...task may already exist. Recently, the use of social network analysis (SNA) has allowed the military to map dark networks of terrorist organizations

  15. Evolution of individual versus social learning on social networks.

    PubMed

    Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

    2015-03-06

    A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak.

  16. Social networks in cardiovascular disease management.

    PubMed

    Shaya, Fadia T; Yan, Xia; Farshid, Maryam; Barakat, Samer; Jung, Miah; Low, Sara; Fedder, Donald

    2010-12-01

    Cardiovascular disease remains the leading cause of death in the USA. Social networks have a positive association with obesity, smoking cessation and weight loss. This article summarizes studies evaluating the impact of social networks on the management of cardiovascular disease. The 35 studies included in the article describe the impact of social networks on a decreased incidence of cardiovascular disease, depression and mortality. In addition, having a large-sized social network is also associated with better outcomes and improved health. The role of pharmacists is beginning to play an important role in the patient-centered medical home, which needs to be incorporated into social networks. The patient-centered medical home can serve as an adaptive source for social network evolvement.

  17. Spectral measures of bipartivity in complex networks.

    PubMed

    Estrada, Ernesto; Rodríguez-Velázquez, Juan A

    2005-10-01

    We introduce a quantitative measure of network bipartivity as a proportion of even to total number of closed walks in the network. Spectral graph theory is used to quantify how close to bipartite a network is and the extent to which individual nodes and edges contribute to the global network bipartivity. It is shown that the bipartivity characterizes the network structure and can be related to the efficiency of semantic or communication networks, trophic interactions in food webs, construction principles in metabolic networks, or communities in social networks.

  18. The Strategic Paradox of Social Networks

    DTIC Science & Technology

    2011-03-18

    cause long-lasting effects. For example, a recent Career Builder survey found the number of civilian employers who used social networking sites as...Service members discussing political subjects on social networking sites can quickly attract negative attention. In March 2010, Marine Corps Sergeant...on social networking sites . In March 2010, stories surfaced reporting the Israeli army canceled a mission after a soldier “disclosed the name of the

  19. Discovering Mobile Social Networks by Semantic Technologies

    NASA Astrophysics Data System (ADS)

    Jung, Jason J.; Choi, Kwang Sun; Park, Sung Hyuk

    It has been important for telecommunication companies to discover social networks from mobile subscribers. They have attempted to provide a number of recommendation services, but they realized that the services were not successful. In this chapter, we present semantic technologies for discovering social networks. The process is mainly composed of two steps; (1) profile identification and (2) context understanding. Through developing a Next generation Contents dElivery (NICE) platform, we were able to generate various services based on the discovered social networks.

  20. Introduction to Focus Issue: synchronization in complex networks.

    PubMed

    Suykens, Johan A K; Osipov, Grigory V

    2008-09-01

    Synchronization in large ensembles of coupled interacting units is a fundamental phenomenon relevant for the understanding of working mechanisms in neuronal networks, genetic networks, coupled electrical and laser networks, coupled mechanical systems, networks in social sciences, and others. It relates to mathematical and computational analysis of the existence of different states and its stability, clustering, bifurcations and chaos, robustness and sensitivity analysis, etc., at the intersection between synchronization and pattern formation in complex networks. This interdisciplinary oriented Focus Issue presents recent progress in this area with contributions on generic methods, specific model studies, and applications.

  1. Robustness and structure of complex networks

    NASA Astrophysics Data System (ADS)

    Shao, Shuai

    This dissertation covers the two major parts of my PhD research on statistical physics and complex networks: i) modeling a new type of attack -- localized attack, and investigating robustness of complex networks under this type of attack; ii) discovering the clustering structure in complex networks and its influence on the robustness of coupled networks. Complex networks appear in every aspect of our daily life and are widely studied in Physics, Mathematics, Biology, and Computer Science. One important property of complex networks is their robustness under attacks, which depends crucially on the nature of attacks and the structure of the networks themselves. Previous studies have focused on two types of attack: random attack and targeted attack, which, however, are insufficient to describe many real-world damages. Here we propose a new type of attack -- localized attack, and study the robustness of complex networks under this type of attack, both analytically and via simulation. On the other hand, we also study the clustering structure in the network, and its influence on the robustness of a complex network system. In the first part, we propose a theoretical framework to study the robustness of complex networks under localized attack based on percolation theory and generating function method. We investigate the percolation properties, including the critical threshold of the phase transition pc and the size of the giant component Pinfinity. We compare localized attack with random attack and find that while random regular (RR) networks are more robust against localized attack, Erdoḧs-Renyi (ER) networks are equally robust under both types of attacks. As for scale-free (SF) networks, their robustness depends crucially on the degree exponent lambda. The simulation results show perfect agreement with theoretical predictions. We also test our model on two real-world networks: a peer-to-peer computer network and an airline network, and find that the real-world networks

  2. Opinion control in complex networks

    NASA Astrophysics Data System (ADS)

    Masuda, Naoki

    2015-03-01

    In many political elections, the electorate appears to be a composite of partisan and independent voters. Given that partisans are not likely to convert to a different party, an important goal for a political party could be to mobilize independent voters toward the party with the help of strong leadership, mass media, partisans, and the effects of peer-to-peer influence. Based on the exact solution of classical voter model dynamics in the presence of perfectly partisan voters (i.e., zealots), we propose a computational method that uses pinning control strategy to maximize the share of a party in a social network of independent voters. The party, corresponding to the controller or zealots, optimizes the nodes to be controlled given the information about the connectivity of independent voters and the set of nodes that the opposing party controls. We show that controlling hubs is generally a good strategy, but the optimized strategy is even better. The superiority of the optimized strategy is particularly eminent when the independent voters are connected as directed (rather than undirected) networks.

  3. How Many "Friends" Do You Need? Teaching Students How to Network Using Social Media

    ERIC Educational Resources Information Center

    Sacks, Michael Alan; Graves, Nikki

    2012-01-01

    Student reliance on social media is undeniable. However, while we largely regard social media as a new phenomena, the concepts underlying it come directly from social network theory in sociology and organizational behavior. In this article, the authors examine how the social network concepts of size, quality, complexity, diffusion, and distance…

  4. Autoscoring Essays Based on Complex Networks

    ERIC Educational Resources Information Center

    Ke, Xiaohua; Zeng, Yongqiang; Luo, Haijiao

    2016-01-01

    This article presents a novel method, the Complex Dynamics Essay Scorer (CDES), for automated essay scoring using complex network features. Texts produced by college students in China were represented as scale-free networks (e.g., a word adjacency model) from which typical network features, such as the in-/out-degrees, clustering coefficient (CC),…

  5. Networking for philanthropy: increasing volunteer behavior via social networking sites.

    PubMed

    Kim, Yoojung; Lee, Wei-Na

    2014-03-01

    Social networking sites (SNSs) provide a unique social venue to engage the young generation in philanthropy through their networking capabilities. An integrated model that incorporates social capital into the Theory of Reasoned Action is developed to explain volunteer behavior through social networks. As expected, volunteer behavior was predicted by volunteer intention, which was influenced by attitudes and subjective norms. In addition, social capital, an outcome of the extensive use of SNSs, was as an important driver of users' attitude and subjective norms toward volunteering via SNSs.

  6. Social Networks of Educated Nematodes

    PubMed Central

    Willett, Denis S.; Alborn, Hans T.; Duncan, Larry W.; Stelinski, Lukasz L.

    2015-01-01

    Entomopathogenic nematodes are obligate lethal parasitoids of insect larvae that navigate a chemically complex belowground environment while interacting with their insect hosts, plants, and each other. In this environment, prior exposure to volatile compounds appears to prime nematodes in a compound specific manner, increasing preference for volatiles they previously were exposed to and decreasing attraction to other volatiles. In addition, persistence of volatile exposure influences this response. Longer exposure not only increases preference, but also results in longer retention of that preference. These entomopathogenic nematodes display interspecific social behavioral plasticity; experienced nematodes influence the behavior of different species. This interspecific social behavioral plasticity suggests a mechanism for rapid adaptation of belowground communities to dynamic environments. PMID:26404058

  7. Competition between global and local online social networks.

    PubMed

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2016-04-27

    The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

  8. Competition between global and local online social networks

    NASA Astrophysics Data System (ADS)

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2016-04-01

    The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability.

  9. Competition between global and local online social networks

    PubMed Central

    Kleineberg, Kaj-Kolja; Boguñá, Marián

    2016-01-01

    The overwhelming success of online social networks, the key actors in the Web 2.0 cosmos, has reshaped human interactions globally. To help understand the fundamental mechanisms which determine the fate of online social networks at the system level, we describe the digital world as a complex ecosystem of interacting networks. In this paper, we study the impact of heterogeneity in network fitnesses on the competition between an international network, such as Facebook, and local services. The higher fitness of international networks is induced by their ability to attract users from all over the world, which can then establish social interactions without the limitations of local networks. In other words, inter-country social ties lead to increased fitness of the international network. To study the competition between an international network and local ones, we construct a 1:1000 scale model of the digital world, consisting of the 80 countries with the most Internet users. Under certain conditions, this leads to the extinction of local networks; whereas under different conditions, local networks can persist and even dominate completely. In particular, our model suggests that, with the parameters that best reproduce the empirical overtake of Facebook, this overtake could have not taken place with a significant probability. PMID:27117826

  10. A Complexity Theory of Neural Networks

    DTIC Science & Technology

    1990-04-14

    Significant results have been obtained on the computation complexity of analog neural networks , and distribute voting. The computing power and...learning algorithms for limited precision analog neural networks have been investigated. Lower bounds for constant depth, polynomial size analog neural ... networks , and a limited version of discrete neural networks have been obtained. The work on distributed voting has important applications for distributed

  11. Social Software: Participants' Experience Using Social Networking for Learning

    ERIC Educational Resources Information Center

    Batchelder, Cecil W.

    2010-01-01

    Social networking tools used in learning provides instructional design with tools for transformative change in education. This study focused on defining the meanings and essences of social networking through the lived common experiences of 7 college students. The problem of the study was a lack of learner voice in understanding the value of social…

  12. Going Social: The Impact of Social Networking in Promoting Education

    ERIC Educational Resources Information Center

    Jain, Neelesh Kumar; Verma, Ashish; Verma, Rama Shankar; Tiwari, Prashant

    2012-01-01

    The growth and the popularity of the Social networks has a high impact on the development of the students in the field of Personality, Attitudes, Knowledge and on its whole academic performance in classroom and society. This paper envisage on the impact of Social Network on Education and Training of the students.

  13. Link prediction in multiplex online social networks

    PubMed Central

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin

    2017-01-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%. PMID:28386441

  14. Link prediction in multiplex online social networks

    NASA Astrophysics Data System (ADS)

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  15. Link prediction in multiplex online social networks.

    PubMed

    Jalili, Mahdi; Orouskhani, Yasin; Asgari, Milad; Alipourfard, Nazanin; Perc, Matjaž

    2017-02-01

    Online social networks play a major role in modern societies, and they have shaped the way social relationships evolve. Link prediction in social networks has many potential applications such as recommending new items to users, friendship suggestion and discovering spurious connections. Many real social networks evolve the connections in multiple layers (e.g. multiple social networking platforms). In this article, we study the link prediction problem in multiplex networks. As an example, we consider a multiplex network of Twitter (as a microblogging service) and Foursquare (as a location-based social network). We consider social networks of the same users in these two platforms and develop a meta-path-based algorithm for predicting the links. The connectivity information of the two layers is used to predict the links in Foursquare network. Three classical classifiers (naive Bayes, support vector machines (SVM) and K-nearest neighbour) are used for the classification task. Although the networks are not highly correlated in the layers, our experiments show that including the cross-layer information significantly improves the prediction performance. The SVM classifier results in the best performance with an average accuracy of 89%.

  16. Complexity Characteristics of Currency Networks

    NASA Astrophysics Data System (ADS)

    Gorski, A. Z.; Drozdz, S.; Kwapien, J.; Oswiecimka, P.

    2006-11-01

    A large set of daily FOREX time series is analyzed. The corresponding correlation matrices (CM) are constructed for USD, EUR and PLN used as the base currencies. The triangle rule is interpreted as constraints reducing the number of independent returns. The CM spectrum is computed and compared with the cases of shuffled currencies and a fictitious random currency taken as a base currency. The Minimal Spanning Tree (MST) graphs are calculated and the clustering effects for strong currencies are found. It is shown that for MSTs the node rank has power like, scale free behavior. Finally, the scaling exponents are evaluated and found in the range analogous to those identified recently for various complex networks.

  17. Mining of the social network extraction

    NASA Astrophysics Data System (ADS)

    Nasution, M. K. M.; Hardi, M.; Syah, R.

    2017-01-01

    The use of Web as social media is steadily gaining ground in the study of social actor behaviour. However, information in Web can be interpreted in accordance with the ability of the method such as superficial methods for extracting social networks. Each method however has features and drawbacks: it cannot reveal the behaviour of social actors, but it has the hidden information about them. Therefore, this paper aims to reveal such information in the social networks mining. Social behaviour could be expressed through a set of words extracted from the list of snippets.

  18. Badger social networks correlate with tuberculosis infection.

    PubMed

    Weber, Nicola; Carter, Stephen P; Dall, Sasha R X; Delahay, Richard J; McDonald, Jennifer L; Bearhop, Stuart; McDonald, Robbie A

    2013-10-21

    Although disease hosts are classically assumed to interact randomly [1], infection is likely to spread across structured and dynamic contact networks [2]. We used social network analyses to investigate contact patterns of group-living European badgers, Meles meles, which are an important wildlife reservoir of bovine tuberculosis (TB). We found that TB test-positive badgers were socially isolated from their own groups but were more important for flow, potentially of infection, between social groups. The distinctive social position of infected badgers may help explain how social stability mitigates, and social perturbation increases, the spread of infection in badgers.

  19. Hypergraph topological quantities for tagged social networks

    NASA Astrophysics Data System (ADS)

    Zlatić, Vinko; Ghoshal, Gourab; Caldarelli, Guido

    2009-09-01

    Recent years have witnessed the emergence of a new class of social networks, which require us to move beyond previously employed representations of complex graph structures. A notable example is that of the folksonomy, an online process where users collaboratively employ tags to resources to impart structure to an otherwise undifferentiated database. In a recent paper, we proposed a mathematical model that represents these structures as tripartite hypergraphs and defined basic topological quantities of interest. In this paper, we extend our model by defining additional quantities such as edge distributions, vertex similarity and correlations as well as clustering. We then empirically measure these quantities on two real life folksonomies, the popular online photo sharing site Flickr and the bookmarking site CiteULike. We find that these systems share similar qualitative features with the majority of complex networks that have been previously studied. We propose that the quantities and methodology described here can be used as a standard tool in measuring the structure of tagged networks.

  20. Science, Society, and Social Networking

    NASA Astrophysics Data System (ADS)

    White, K. S.; Lohwater, T.

    2009-12-01

    The increased use of social networking is changing the way that scientific societies interact with their members and others. The American Association for the Advancement of Science (AAAS) uses a variety of online networks to engage its members and the broader scientific community. AAAS members and non-members can interact with AAAS staff and each other on AAAS sites on Facebook, YouTube, and Twitter, as well as blogs and forums on the AAAS website (www.aaas.org). These tools allow scientists to more readily become engaged in policy by providing information on current science policy topics as well as methods of involvement. For example, members and the public can comment on policy-relevant stories from Science magazine’s ScienceInsider blog, download a weekly policy podcast, receive a weekly email update of policy issues affecting the scientific community, or watch a congressional hearing from their computer. AAAS resource websites and outreach programs, including Communicating Science (www.aaas.org/communicatingscience), Working with Congress (www.aaas.org/spp/cstc/) and Science Careers (http://sciencecareers.sciencemag.org) also provide tools for scientists to become more personally engaged in communicating their findings and involved in the policy process.

  1. Trust Transitivity in Social Networks

    PubMed Central

    Richters, Oliver; Peixoto, Tiago P.

    2011-01-01

    Non-centralized recommendation-based decision making is a central feature of several social and technological processes, such as market dynamics, peer-to-peer file-sharing and the web of trust of digital certification. We investigate the properties of trust propagation on networks, based on a simple metric of trust transitivity. We investigate analytically the percolation properties of trust transitivity in random networks with arbitrary in/out-degree distributions, and compare with numerical realizations. We find that the existence of a non-zero fraction of absolute trust (i.e. entirely confident trust) is a requirement for the viability of global trust propagation in large systems: The average pair-wise trust is marked by a discontinuous transition at a specific fraction of absolute trust, below which it vanishes. Furthermore, we perform an extensive analysis of the Pretty Good Privacy (PGP) web of trust, in view of the concepts introduced. We compare different scenarios of trust distribution: community- and authority-centered. We find that these scenarios lead to sharply different patterns of trust propagation, due to the segregation of authority hubs and densely-connected communities. While the authority-centered scenario is more efficient, and leads to higher average trust values, it favours weakly-connected “fringe” nodes, which are directly trusted by authorities. The community-centered scheme, on the other hand, favours nodes with intermediate in/out-degrees, in detriment of the authorities and its “fringe” peers. PMID:21483683

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

    PubMed Central

    BurnSilver, Shauna B.; Arenas, Alex; Magdanz, James S.; Kofinas, Gary P.

    2016-01-01

    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. PMID:27856752

  3. Mining Heterogeneous Social Networks for Egocentric Information Abstraction

    NASA Astrophysics Data System (ADS)

    Li, Cheng-Te; Lin, Shou-De

    Social network is a powerful data structure that allows the depiction of relationship information between entities. However, real-world social networks are sometimes too complex for human to pursue further analysis. In this work, an unsupervised mechanism is proposed for egocentric information abstraction in heterogeneous social networks. To achieve this goal, we propose a vector space representation for heterogeneous social networks to identify combination of relations as features and compute statistical dependencies as feature values. These features, either linear or eyelie, intend to capture the semantic information in the surrounding environment of the ego. Then we design three abstraction measures to distill representative and important information to construct the abstracted graphs for visual presentation. The evaluations conducted on a real world movie datasct and an artificial crime dataset demonstrate that the abstractions can indeed retain significant information and facilitate more accurate and efficient human analysis.

  4. Social complexity as a proximate and ultimate factor in communicative complexity

    PubMed Central

    Freeberg, Todd M.; Dunbar, Robin I. M.; Ord, Terry J.

    2012-01-01

    The ‘social complexity hypothesis’ for communication posits that groups with complex social systems require more complex communicative systems to regulate interactions and relations among group members. Complex social systems, compared with simple social systems, are those in which individuals frequently interact in many different contexts with many different individuals, and often repeatedly interact with many of the same individuals in networks over time. Complex communicative systems, compared with simple communicative systems, are those that contain a large number of structurally and functionally distinct elements or possess a high amount of bits of information. Here, we describe some of the historical arguments that led to the social complexity hypothesis, and review evidence in support of the hypothesis. We discuss social complexity as a driver of communication and possible causal factor in human language origins. Finally, we discuss some of the key current limitations to the social complexity hypothesis—the lack of tests against alternative hypotheses for communicative complexity and evidence corroborating the hypothesis from modalities other than the vocal signalling channel. PMID:22641818

  5. Social networking sites and adolescent health.

    PubMed

    Moreno, Megan A; Kolb, Jennifer

    2012-06-01

    Social networking sites are popular among and consistently used by adolescents. These sites present benefits as well as risks to adolescent health. Recently, pediatric providers have also considered the benefits and risks of using social networking sites in their own practices.

  6. Entrepreneurial Idea Identification through Online Social Networks

    ERIC Educational Resources Information Center

    Lang, Matthew C.

    2010-01-01

    The increasing use of social network websites may signal a change in the way the next generation of entrepreneurs identify entrepreneurial ideas. An important part of the entrepreneurship literature emphasizes how vital the use of social networks is to entrepreneurial idea identification, opportunity recognition, and ultimately new venture…

  7. Enhancing Classroom Effectiveness through Social Networking Tools

    ERIC Educational Resources Information Center

    Kurthakoti, Raghu; Boostrom, Robert E., Jr.; Summey, John H.; Campbell, David A.

    2013-01-01

    To determine the usefulness of social networking Web sites such as Ning.com as a communication tool in marketing courses, a study was designed with special concern for social network use in comparison to Blackboard. Students from multiple marketing courses were surveyed. Assessments of Ning.com and Blackboard were performed both to understand how…

  8. Social Networking on the Semantic Web

    ERIC Educational Resources Information Center

    Finin, Tim; Ding, Li; Zhou, Lina; Joshi, Anupam

    2005-01-01

    Purpose: Aims to investigate the way that the semantic web is being used to represent and process social network information. Design/methodology/approach: The Swoogle semantic web search engine was used to construct several large data sets of Resource Description Framework (RDF) documents with social network information that were encoded using the…

  9. One Health in social networks and social media.

    PubMed

    Mekaru, S R; Brownstein, J S

    2014-08-01

    In the rapidly evolving world of social media, social networks, mobile applications and citizen science, online communities can develop organically and separately from larger or more established organisations. The One Health online community is experiencing expansion from both the bottom up and the top down. In this paper, the authors review social media's strengths and weaknesses, earlier work examining Internet resources for One Health, the current state of One Health in social media (e.g. Facebook, Twitter, YouTube) and online social networking sites (e.g. LinkedIn and ResearchGate), as well as social media in One Health-related citizen science projects. While One Health has a fairly strong presence on websites, its social media presence is more limited and has an uneven geographic distribution. In work following the Stone Mountain Meeting,the One Health Global Network Task Force Report recommended the creation of an online community of practice. Professional social networks as well as the strategic use of social media should be employed in this effort. Finally, One Health-related research projects using volunteers (citizen science) often use social media to enhance their recruitment. Including these researchers in a community of practitioners would take full advantage of their existing social media presence. In conclusion, the interactive nature of social media, combined with increasing global Internet access, provides the One Health community with opportunities to meaningfully expand their community and promote their message.

  10. One Health in social networks and social media

    PubMed Central

    Mekaru, S.R.; Brownstein, J.S.

    2015-01-01

    Summary In the rapidly evolving world of social media, social networks, mobile applications and citizen science, online communities can develop organically and separately from larger or more established organisations. The One Health online community is experiencing expansion from both the bottom up and the top down. In this paper, the authors review social media’s strengths and weaknesses, earlier work examining Internet resources for One Health, the current state of One Health in social media (e.g. Facebook, Twitter, YouTube) and online social networking sites (e.g. LinkedIn and ResearchGate), as well as social media in One Health-related citizen science projects. While One Health has a fairly strong presence on websites, its social media presence is more limited and has an uneven geographic distribution. In work following the Stone Mountain Meeting, the One Health Global Network Task Force Report recommended the creation of an online community of practice. Professional social networks as well as the strategic use of social media should be employed in this effort. Finally, One Health-related research projects using volunteers (citizen science) often use social media to enhance their recruitment. Including these researchers in a community of practitioners would take full advantage of their existing social media presence. In conclusion, the interactive nature of social media, combined with increasing global Internet access, provides the One Health community with opportunities to meaningfully expand their community and promote their message. PMID:25707189

  11. Dynamical complexity in the perception-based network formation model

    NASA Astrophysics Data System (ADS)

    Jo, Hang-Hyun; Moon, Eunyoung

    2016-12-01

    Many link formation mechanisms for the evolution of social networks have been successful to reproduce various empirical findings in social networks. However, they have largely ignored the fact that individuals make decisions on whether to create links to other individuals based on cost and benefit of linking, and the fact that individuals may use perception of the network in their decision making. In this paper, we study the evolution of social networks in terms of perception-based strategic link formation. Here each individual has her own perception of the actual network, and uses it to decide whether to create a link to another individual. An individual with the least perception accuracy can benefit from updating her perception using that of the most accurate individual via a new link. This benefit is compared to the cost of linking in decision making. Once a new link is created, it affects the accuracies of other individuals' perceptions, leading to a further evolution of the actual network. As for initial actual networks, we consider both homogeneous and heterogeneous cases. The homogeneous initial actual network is modeled by Erdős-Rényi (ER) random networks, while we take a star network for the heterogeneous case. In any cases, individual perceptions of the actual network are modeled by ER random networks with controllable linking probability. Then the stable link density of the actual network is found to show discontinuous transitions or jumps according to the cost of linking. As the number of jumps is the consequence of the dynamical complexity, we discuss the effect of initial conditions on the number of jumps to find that the dynamical complexity strongly depends on how much individuals initially overestimate or underestimate the link density of the actual network. For the heterogeneous case, the role of the highly connected individual as an information spreader is also discussed.

  12. Digital Social Network Mining for Topic Discovery

    NASA Astrophysics Data System (ADS)

    Moradianzadeh, Pooya; Mohi, Maryam; Sadighi Moshkenani, Mohsen

    Networked computers are expanding more and more around the world, and digital social networks becoming of great importance for many people's work and leisure. This paper mainly focused on discovering the topic of exchanging information in digital social network. In brief, our method is to use a hierarchical dictionary of related topics and words that mapped to a graph. Then, with comparing the extracted keywords from the context of social network with graph nodes, probability of relation between context and desired topics will be computed. This model can be used in many applications such as advertising, viral marketing and high-risk group detection.

  13. Online Community Detection for Large Complex Networks

    PubMed Central

    Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

    2014-01-01

    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. PMID:25061683

  14. Narcissism and social networking Web sites.

    PubMed

    Buffardi, Laura E; Campbell, W Keith

    2008-10-01

    The present research examined how narcissism is manifested on a social networking Web site (i.e., Facebook.com). Narcissistic personality self-reports were collected from social networking Web page owners. Then their Web pages were coded for both objective and subjective content features. Finally, strangers viewed the Web pages and rated their impression of the owner on agentic traits, communal traits, and narcissism. Narcissism predicted (a) higher levels of social activity in the online community and (b) more self-promoting content in several aspects of the social networking Web pages. Strangers who viewed the Web pages judged more narcissistic Web page owners to be more narcissistic. Finally, mediational analyses revealed several Web page content features that were influential in raters' narcissistic impressions of the owners, including quantity of social interaction, main photo self-promotion, and main photo attractiveness. Implications of the expression of narcissism in social networking communities are discussed.

  15. Information filtering on coupled social networks.

    PubMed

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks.

  16. Information Filtering on Coupled Social Networks

    PubMed Central

    Nie, Da-Cheng; Zhang, Zi-Ke; Zhou, Jun-Lin; Fu, Yan; Zhang, Kui

    2014-01-01

    In this paper, based on the coupled social networks (CSN), we propose a hybrid algorithm to nonlinearly integrate both social and behavior information of online users. Filtering algorithm, based on the coupled social networks, considers the effects of both social similarity and personalized preference. Experimental results based on two real datasets, Epinions and Friendfeed, show that the hybrid pattern can not only provide more accurate recommendations, but also enlarge the recommendation coverage while adopting global metric. Further empirical analyses demonstrate that the mutual reinforcement and rich-club phenomenon can also be found in coupled social networks where the identical individuals occupy the core position of the online system. This work may shed some light on the in-depth understanding of the structure and function of coupled social networks. PMID:25003525

  17. Spectral Analysis of Rich Network Topology in Social Networks

    ERIC Educational Resources Information Center

    Wu, Leting

    2013-01-01

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

  18. Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach.

    PubMed

    Senior, Alistair M; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J

    2016-01-01

    Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments.

  19. Social Network Analysis and Nutritional Behavior: An Integrated Modeling Approach

    PubMed Central

    Senior, Alistair M.; Lihoreau, Mathieu; Buhl, Jerome; Raubenheimer, David; Simpson, Stephen J.

    2016-01-01

    Animals have evolved complex foraging strategies to obtain a nutritionally balanced diet and associated fitness benefits. Recent research combining state-space models of nutritional geometry with agent-based models (ABMs), show how nutrient targeted foraging behavior can also influence animal social interactions, ultimately affecting collective dynamics and group structures. Here we demonstrate how social network analyses can be integrated into such a modeling framework and provide a practical analytical tool to compare experimental results with theory. We illustrate our approach by examining the case of nutritionally mediated dominance hierarchies. First we show how nutritionally explicit ABMs that simulate the emergence of dominance hierarchies can be used to generate social networks. Importantly the structural properties of our simulated networks bear similarities to dominance networks of real animals (where conflicts are not always directly related to nutrition). Finally, we demonstrate how metrics from social network analyses can be used to predict the fitness of agents in these simulated competitive environments. Our results highlight the potential importance of nutritional mechanisms in shaping dominance interactions in a wide range of social and ecological contexts. Nutrition likely influences social interactions in many species, and yet a theoretical framework for exploring these effects is currently lacking. Combining social network analyses with computational models from nutritional ecology may bridge this divide, representing a pragmatic approach for generating theoretical predictions for nutritional experiments. PMID:26858671

  20. Dynamics of and on complex networks

    NASA Astrophysics Data System (ADS)

    Halu, Arda

    Complex networks are dynamic, evolving structures that can host a great number of dynamical processes. In this thesis, we address current challenges regarding the dynamics of and dynamical processes on complex networks. First, we study complex network dynamics from the standpoint of network growth. As a quantitative measure of the complexity and information content of networks generated by growing network models, we define and evaluate their entropy rate. We propose stochastic growth models inspired by the duplication-divergence mechanism to generate epistatic interaction networks and find that they exhibit the property of monochromaticity as a result of their dynamical evolution. Second, we explore the dynamics of quantum mechanical processes on complex networks. We investigate the Bose-Hubbard model on annealed and quenched scale-free networks as well as Apollonian networks and show that their phase diagram changes significantly in the presence of complex topologies, depending on the second degree of the degree distribution and the maximal eigenvalue of the adjacency matrix. We then study the Jaynes-Cummings-Hubbard model on various complex topologies and demonstrate the importance of the maximal eigenvalue of the hopping matrix in determining the phase diagram of the model. Third, we investigate dynamical processes on interacting and multiplex networks. We study opinion dynamics in a simulated setting of two antagonistically interacting networks and recover the importance of connectivity and committed agents. We propose a multiplex centrality measure that takes into account the connectivity patterns within and across different layers and find that the dynamics of biased random walks on multiplex networks gives rise to a centrality ranking that is different from univariate centrality measures. Finally, we study the statistical mechanics of multilayered spatial networks and demonstrate the emergence of significant link overlap and improved navigability in

  1. Social Network Resources and Management of Hypertension*

    PubMed Central

    Cornwell, Erin York; Waite, Linda J.

    2013-01-01

    Hypertension is one of the most prevalent chronic diseases among older adults, but rates of blood pressure control are low. In this paper, we explore the role of social network ties and network-based resources (e.g., information and support) in hypertension diagnosis and management. We use data from the National Social Life, Health, and Aging Project (NSHAP) to identify older adults with undiagnosed or uncontrolled hypertension. We find that network characteristics and emotional support are associated with hypertension diagnosis and control. Importantly, the risks of undiagnosed and uncontrolled hypertension are lower among those with larger social networks -- if they discuss health issues with their network members. When these lines of communication are closed, network size is associated with greater risk of undiagnosed and uncontrolled hypertension. Health care utilization partially mediates associations with diagnosis, but the benefits of network resources for hypertension control do not seem to stem from health-related behaviors. PMID:22660826

  2. Loneliness, Social Networks, and Social Functioning in Borderline Personality Disorder.

    PubMed

    Liebke, Lisa; Bungert, Melanie; Thome, Janine; Hauschild, Sophie; Gescher, Dorothee Maria; Schmahl, Christian; Bohus, Martin; Lis, Stefanie

    2016-08-08

    Persistent loneliness is often reported by patients with borderline personality disorder (BPD). However, empirical studies investigating this aspect of BPD psychopathology are sparse. Studies from social psychology revealed that social isolation and low social functioning contribute to loneliness, that is, the subjective feeling of being alone. The aim of the present study was to contribute to the understanding of loneliness in BPD by investigating its relation to social isolation and functioning in different domains of life. Subjective experience of loneliness was measured in 80 women (40 BPD patients, 40 healthy controls) with the UCLA Loneliness Scale. Social isolation and social functioning were assessed with the Social Network Inventory and the Social Functioning Scale. In addition, we assessed global functioning with the Global Assessment of Functioning. BPD patients reported stronger feelings of loneliness compared to healthy participants. In general, the level of loneliness was linked to network size, social engagement, and prosocial behavior. Diversity of social networks and functioning in the domain of interpersonal communication were associated with the level of loneliness only in BPD. A reduced variety of roles in social life together with impairments in interpersonal communication were particularly relevant for the experience of loneliness in BPD, suggesting an indirect path to target this psychopathological feature in therapeutic interventions. However, both social isolation and social functioning were not sufficient to explain the severely increased loneliness experienced by these patients, stressing the need for further investigation of determinants of loneliness in this clinical population. (PsycINFO Database Record

  3. Internet-Based Community Networks: Finding the Social in Social Networks

    NASA Astrophysics Data System (ADS)

    Lawrence, K. Faith

    In this chapter we explore the concept of community within social networks and the effect that this primarily social construct can have on the way in which we understand trust within an online network. To do this we analyse and compare a number of the definitions that are both traditionally used to identify online communities and which have developed with the advent of semantically described social networks. Taking these definitions we apply them to a number of groups within a visualisation of a social network and, using this case study, consider the differences that are apparent between the types of groups. Finally, we discuss how the social implications inherent within the definition of community interact with the trust and reputation systems that exist in such networks. In doing so, we focus on the social aspect of the social network and the ways in which the social and technical worlds entwine.

  4. An Introduction to Social Network Data Analytics

    NASA Astrophysics Data System (ADS)

    Aggarwal, Charu C.

    The advent of online social networks has been one of the most exciting events in this decade. Many popular online social networks such as Twitter, LinkedIn, and Facebook have become increasingly popular. In addition, a number of multimedia networks such as Flickr have also seen an increasing level of popularity in recent years. Many such social networks are extremely rich in content, and they typically contain a tremendous amount of content and linkage data which can be leveraged for analysis. The linkage data is essentially the graph structure of the social network and the communications between entities; whereas the content data contains the text, images and other multimedia data in the network. The richness of this network provides unprecedented opportunities for data analytics in the context of social networks. This book provides a data-centric view of online social networks; a topic which has been missing from much of the literature. This chapter provides an overview of the key topics in this field, and their coverage in this book.

  5. Network Motifs: Simple Building Blocks of Complex Networks

    NASA Astrophysics Data System (ADS)

    Milo, R.; Shen-Orr, S.; Itzkovitz, S.; Kashtan, N.; Chklovskii, D.; Alon, U.

    2002-10-01

    Complex networks are studied across many fields of science. To uncover their structural design principles, we defined ``network motifs,'' patterns of interconnections occurring in complex networks at numbers that are significantly higher than those in randomized networks. We found such motifs in networks from biochemistry, neurobiology, ecology, and engineering. The motifs shared by ecological food webs were distinct from the motifs shared by the genetic networks of Escherichia coli and Saccharomyces cerevisiae or from those found in the World Wide Web. Similar motifs were found in networks that perform information processing, even though they describe elements as different as biomolecules within a cell and synaptic connections between neurons in Caenorhabditis elegans. Motifs may thus define universal classes of networks. This approach may uncover the basic building blocks of most networks.

  6. Parental Social Network and Child's Friendship Network.

    ERIC Educational Resources Information Center

    Uhlendorff, Harald; Oswald, Hans

    This study analyzed the relation between the friendship networks of parents and the peer networks of their children. Subjects were 255 second- through fifth-grade children of an inner-city primary school in the western part of Berlin, Germany, who were interviewed about friends. In the interview, children were asked to name other children with…

  7. Evolution of individual versus social learning on social networks

    PubMed Central

    Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

    2015-01-01

    A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of ‘cultural models’ exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. PMID:25631568

  8. The Social Origins of Networks and Diffusion.

    PubMed

    Centola, Damon

    2015-03-01

    Recent research on social contagion has demonstrated significant effects of network topology on the dynamics of diffusion. However, network topologies are not given a priori. Rather, they are patterns of relations that emerge from individual and structural features of society, such as population composition, group heterogeneity, homophily, and social consolidation. Following Blau and Schwartz, the author develops a model of social network formation that explores how social and structural constraints on tie formation generate emergent social topologies and then explores the effectiveness of these social networks for the dynamics of social diffusion. Results show that, at one extreme, high levels of consolidation can create highly balkanized communities with poor integration of shared norms and practices. As suggested by Blau and Schwartz, reducing consolidation creates more crosscutting circles and significantly improves the dynamics of social diffusion across the population. However, the author finds that further reducing consolidation creates highly intersecting social networks that fail to support the widespread diffusion of norms and practices, indicating that successful social diffusion can depend on moderate to high levels of structural consolidation.

  9. Approaching human language with complex networks

    NASA Astrophysics Data System (ADS)

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

  10. Approaching human language with complex networks.

    PubMed

    Cong, Jin; Liu, Haitao

    2014-12-01

    The interest in modeling and analyzing human language with complex networks is on the rise in recent years and a considerable body of research in this area has already been accumulated. We survey three major lines of linguistic research from the complex network approach: 1) characterization of human language as a multi-level system with complex network analysis; 2) linguistic typological research with the application of linguistic networks and their quantitative measures; and 3) relationships between the system-level complexity of human language (determined by the topology of linguistic networks) and microscopic linguistic (e.g., syntactic) features (as the traditional concern of linguistics). We show that the models and quantitative tools of complex networks, when exploited properly, can constitute an operational methodology for linguistic inquiry, which contributes to the understanding of human language and the development of linguistics. We conclude our review with suggestions for future linguistic research from the complex network approach: 1) relationships between the system-level complexity of human language and microscopic linguistic features; 2) expansion of research scope from the global properties to other levels of granularity of linguistic networks; and 3) combination of linguistic network analysis with other quantitative studies of language (such as quantitative linguistics).

  11. Securing Information with Complex Optical Encryption Networks

    DTIC Science & Technology

    2015-08-11

    encryption networks, and to provide effective and reliable solutions for information security. 15. SUBJECT TERMS Optical Encryption...popularization of networking and internet , much research effort is made in the field of information security. Military communication system makes an...objective is to propose the architectures for a number of complex optical encryption networks so as to provide effective and reliable solutions for

  12. Realizing actual feedback control of complex network

    NASA Astrophysics Data System (ADS)

    Tu, Chengyi; Cheng, Yuhua

    2014-06-01

    In this paper, we present the concept of feedbackability and how to identify the Minimum Feedbackability Set of an arbitrary complex directed network. Furthermore, we design an estimator and a feedback controller accessing one MFS to realize actual feedback control, i.e. control the system to our desired state according to the estimated system internal state from the output of estimator. Last but not least, we perform numerical simulations of a small linear time-invariant dynamics network and a real simple food network to verify the theoretical results. The framework presented here could make an arbitrary complex directed network realize actual feedback control and deepen our understanding of complex systems.

  13. Chaos synchronization of general complex dynamical networks

    NASA Astrophysics Data System (ADS)

    Lü, Jinhu; Yu, Xinghuo; Chen, Guanrong

    2004-03-01

    Recently, it has been demonstrated that many large-scale complex dynamical networks display a collective synchronization motion. Here, we introduce a time-varying complex dynamical network model and further investigate its synchronization phenomenon. Based on this new complex network model, two network chaos synchronization theorems are proved. We show that the chaos synchronization of a time-varying complex network is determined by means of the inner coupled link matrix, the eigenvalues and the corresponding eigenvectors of the coupled configuration matrix, rather than the conventional eigenvalues of the coupled configuration matrix for a uniform network. Especially, we do not assume that the coupled configuration matrix is symmetric and its off-diagonal elements are nonnegative, which in a way generalizes the related results existing in the literature.

  14. Pinning impulsive control algorithms for complex network

    SciTech Connect

    Sun, Wen; Lü, Jinhu; Chen, Shihua; Yu, Xinghuo

    2014-03-15

    In this paper, we further investigate the synchronization of complex dynamical network via pinning control in which a selection of nodes are controlled at discrete times. Different from most existing work, the pinning control algorithms utilize only the impulsive signals at discrete time instants, which may greatly improve the communication channel efficiency and reduce control cost. Two classes of algorithms are designed, one for strongly connected complex network and another for non-strongly connected complex network. It is suggested that in the strongly connected network with suitable coupling strength, a single controller at any one of the network's nodes can always pin the network to its homogeneous solution. In the non-strongly connected case, the location and minimum number of nodes needed to pin the network are determined by the Frobenius normal form of the coupling matrix. In addition, the coupling matrix is not necessarily symmetric or irreducible. Illustrative examples are then given to validate the proposed pinning impulsive control algorithms.

  15. Social Network Analysis for Program Implementation

    PubMed Central

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

    2015-01-01

    This paper introduces the use of social network analysis theory and tools for implementation research. The social network perspective is useful for understanding, monitoring, influencing, or evaluating the implementation process when programs, policies, practices, or principles are designed and scaled up or adapted to different settings. We briefly describe common barriers to implementation success and relate them to the social networks of implementation stakeholders. We introduce a few simple measures commonly used in social network analysis and discuss how these measures can be used in program implementation. Using the four stage model of program implementation (exploration, adoption, implementation, and sustainment) proposed by Aarons and colleagues [1] and our experience in developing multi-sector partnerships involving community leaders, organizations, practitioners, and researchers, we show how network measures can be used at each stage to monitor, intervene, and improve the implementation process. Examples are provided to illustrate these concepts. We conclude with expected benefits and challenges associated with this approach. PMID:26110842

  16. Structural permeability of complex networks to control signals

    PubMed Central

    Lo Iudice, Francesco; Garofalo, Franco; Sorrentino, Francesco

    2015-01-01

    Many biological, social and technological systems can be described as complex networks. The goal of affecting their behaviour has motivated recent work focusing on the relationship between the network structure and its propensity to be controlled. While this work has provided insight into several relevant problems, a comprehensive approach to address partial and complete controllability of networks is still lacking. Here, we bridge this gap by developing a framework to maximize the diffusion of the control signals through a network, while taking into account physical and economic constraints that inevitably arise in applications. This approach allows us to introduce the network permeability, a unified metric of the propensity of a network to be controllable. The analysis of the permeability of several synthetic and real networks enables us to extract some structural features that deepen our quantitative understanding of the ease with which specific controllability requirements can be met. PMID:26391186

  17. Organ trade using social networks.

    PubMed

    Alrogy, Waleed; Jawdat, Dunia; Alsemari, Muhannad; Alharbi, Abdulrahman; Alasaad, Abdullah; Hajeer, Ali H

    2016-01-01

    Organ transplantation is recognized worldwide as an effective treatment for organ failure. However, due to the increase in the number of patients requiring a transplant, a shortage of suitable organs for transplantation has become a global problem. Human organ trade is an illegal practice of buying or selling organs and is universally sentenced. The aim of this study was to search social network for organ trade and offerings in Saudi Arabia. The study was conducted from June 22, 2015 to February 19, 2016. The search was conducted on Twitter, Google answers, and Facebook using the following terms: kidney for sale, kidneys for sale, liver for sale, kidney wanted, liver wanted, kidney donor, and liver donor. We found a total of 557 adverts on organ trade, 165 (30%) from donors or sellers, and 392 (70%) from recipients or buyers. On Twitter, we found 472 (85%) adverts, on Google answers 61 (11%), and on Facebook 24 (4%). Organ trade is a global problem, and yet it is increasingly seen in many countries. Although the Saudi Center for Organ Transplantation by-laws specifically prohibits and monitors any form of commercial transplantation, it is still essential to enforce guidelines for medical professionals to detect and prevent such criminal acts.

  18. Social network structures and bank runs

    NASA Astrophysics Data System (ADS)

    Li, Shouwei; Li, Jiaheng

    2016-05-01

    This paper investigates the impact of social network structures of depositors on bank runs. The analyzed network structures include random networks, small-world networks and scale-free networks. Simulation results show that the probability of bank run occurrence in random networks is larger than that in small-world networks, but the probability of bank run occurrence in scale-free networks drops from the highest to the lowest among the three types of network structures with the increase of the proportion of impatient depositors. The average degree of depositor networks has a significant impact on bank runs, but this impact is related to the proportion of impatient depositors and the confidence levels of depositors in banks.

  19. A Complexity Theory of Neural Networks

    DTIC Science & Technology

    1991-08-09

    Significant progress has been made in laying the foundations of a complexity theory of neural networks . The fundamental complexity classes have been...identified and studied. The class of problems solvable by small, shallow neural networks has been found to be the same class even if (1) probabilistic...behaviour (2)Multi-valued logic, and (3)analog behaviour, are allowed (subject to certain resonable technical assumptions). Neural networks can be

  20. Social network sampling using spanning trees

    NASA Astrophysics Data System (ADS)

    Jalali, Zeinab S.; Rezvanian, Alireza; Meybodi, Mohammad Reza

    2016-12-01

    Due to the large scales and limitations in accessing most online social networks, it is hard or infeasible to directly access them in a reasonable amount of time for studying and analysis. Hence, network sampling has emerged as a suitable technique to study and analyze real networks. The main goal of sampling online social networks is constructing a small scale sampled network which preserves the most important properties of the original network. In this paper, we propose two sampling algorithms for sampling online social networks using spanning trees. The first proposed sampling algorithm finds several spanning trees from randomly chosen starting nodes; then the edges in these spanning trees are ranked according to the number of times that each edge has appeared in the set of found spanning trees in the given network. The sampled network is then constructed as a sub-graph of the original network which contains a fraction of nodes that are incident on highly ranked edges. In order to avoid traversing the entire network, the second sampling algorithm is proposed using partial spanning trees. The second sampling algorithm is similar to the first algorithm except that it uses partial spanning trees. Several experiments are conducted to examine the performance of the proposed sampling algorithms on well-known real networks. The obtained results in comparison with other popular sampling methods demonstrate the efficiency of the proposed sampling algorithms in terms of Kolmogorov-Smirnov distance (KSD), skew divergence distance (SDD) and normalized distance (ND).

  1. Regional Use of Social Networking Tools

    DTIC Science & Technology

    2014-12-01

    Tools 2 2.1 Social Networking Global Leaders 2 2.1.1 Facebook 2 2.1.2 Google+ 3 2.1.3 Twitter 3 2.1.4 LinkedIn 4 2.1.5 YouTube 4 2.1.6 Pinterest...for Top SNSs 2 Table 2: Facebook Penetration Among Internet Users by Region 3 Table 3: SNS Adoption in African Countries 7 Table 4: Rankings of the...social network. Table 1: Monthly Number of Active Users for Top SNSs Social Network Monthly Active Users Facebook 1.28 billion Google+ 540 million

  2. Topology analysis of social networks extracted from literature.

    PubMed

    Waumans, Michaël C; Nicodème, Thibaut; Bersini, Hugues

    2015-01-01

    In a world where complex networks are an increasingly important part of science, it is interesting to question how the new reading of social realities they provide applies to our cultural background and in particular, popular culture. Are authors of successful novels able to reproduce social networks faithful to the ones found in reality? Is there any common trend connecting an author's oeuvre, or a genre of fiction? Such an analysis could provide new insight on how we, as a culture, perceive human interactions and consume media. The purpose of the work presented in this paper is to define the signature of a novel's story based on the topological analysis of its social network of characters. For this purpose, an automated tool was built that analyses the dialogs in novels, identifies characters and computes their relationships in a time-dependent manner in order to assess the network's evolution over the course of the story.

  3. Social Support and Social Networks in COPD: A Scoping Review.

    PubMed

    Barton, Christopher; Effing, Tanya W; Cafarella, Paul

    2015-01-01

    A scoping review was conducted to determine the size and nature of the evidence describing associations between social support and networks on health, management and clinical outcomes amongst patients with COPD. Searches of PubMed, PsychInfo and CINAHL were undertaken for the period 1966-December 2013. A descriptive synthesis of the main findings was undertaken to demonstrate where there is current evidence for associations between social support, networks and health outcomes, and where further research is needed. The search yielded 318 papers of which 287 were excluded after applying selection criteria. Two areas emerged in which there was consistent evidence of benefit of social support; namely mental health and self-efficacy. There was inconsistent evidence for a relationship between perceived social support and quality of life, physical functioning and self-rated health. Hospital readmission was not associated with level of perceived social support. Only a small number of studies (3 articles) have reported on the social network of individuals with COPD. There remains a need to identify the factors that promote and enable social support. In particular, there is a need to further understand the characteristics of social networks within the broader social structural conditions in which COPD patients live and manage their illness.

  4. Locating privileged spreaders on an online social network

    NASA Astrophysics Data System (ADS)

    Borge-Holthoefer, Javier; Rivero, Alejandro; Moreno, Yamir

    2012-06-01

    Social media have provided plentiful evidence of their capacity for information diffusion. Fads and rumors but also social unrest and riots travel fast and affect large fractions of the population participating in online social networks (OSNs). This has spurred much research regarding the mechanisms that underlie social contagion, and also who (if any) can unleash system-wide information dissemination. Access to real data, both regarding topology—the network of friendships—and dynamics—the actual way in which OSNs users interact, is crucial to decipher how the former facilitates the latter's success, understood as efficiency in information spreading. With the quantitative analysis that stems from complex network theory, we discuss who (and why) has privileged spreading capabilities when it comes to information diffusion. This is done considering the evolution of an episode of political protest which took place in Spain, spanning one month in 2011.

  5. Social network approaches to leadership: an integrative conceptual review.

    PubMed

    Carter, Dorothy R; DeChurch, Leslie A; Braun, Michael T; Contractor, Noshir S

    2015-05-01

    Contemporary definitions of leadership advance a view of the phenomenon as relational, situated in specific social contexts, involving patterned emergent processes, and encompassing both formal and informal influence. Paralleling these views is a growing interest in leveraging social network approaches to study leadership. Social network approaches provide a set of theories and methods with which to articulate and investigate, with greater precision and rigor, the wide variety of relational perspectives implied by contemporary leadership theories. Our goal is to advance this domain through an integrative conceptual review. We begin by answering the question of why-Why adopt a network approach to study leadership? Then, we offer a framework for organizing prior research. Our review reveals 3 areas of research, which we term: (a) leadership in networks, (b) leadership as networks, and (c) leadership in and as networks. By clarifying the conceptual underpinnings, key findings, and themes within each area, this review serves as a foundation for future inquiry that capitalizes on, and programmatically builds upon, the insights of prior work. Our final contribution is to advance an agenda for future research that harnesses the confluent ideas at the intersection of leadership in and as networks. Leadership in and as networks represents a paradigm shift in leadership research-from an emphasis on the static traits and behaviors of formal leaders whose actions are contingent upon situational constraints, toward an emphasis on the complex and patterned relational processes that interact with the embedding social context to jointly constitute leadership emergence and effectiveness.

  6. Maintenance of cultural diversity: social roles, social networks, and cognitive networks.

    PubMed

    Abrams, Marshall

    2014-06-01

    Smaldino suggests that patterns that give rise to group-level cultural traits can also increase individual-level cultural diversity. I distinguish social roles and related social network structures and discuss ways in which each might maintain diversity. I suggest that cognitive analogs of "cohesion," a property of networks that helps maintenance of diversity, might mediate the effects of social roles on diversity.

  7. HIV/AIDS, social capital, and online social networks.

    PubMed

    Drushel, Bruce E

    2013-01-01

    The prospects for online social networks as sites of information-gathering and affiliation for persons with AIDS and others concerned about HIV/AIDS not only represent the latest development in a trend toward circumventing traditional media and official information sources, but also may offer hope for a revitalization of HIV/AIDS discourse in the public sphere. This article provides an overview of three decades of information-seeking on the pandemic and its social and personal implications, as well as case studies of three examples of social networking surrounding HIV/AIDS. It finds preliminary evidence of the formation of strong and weak ties as described in Social Network Theory and suggests that the online accumulation of social capital by opinion leaders could facilitate dissemination of messages on HIV/AIDS awareness and testing.

  8. Disease dynamics in a dynamic social network

    NASA Astrophysics Data System (ADS)

    Christensen, Claire; Albert, István; Grenfell, Bryan; Albert, Réka

    2010-07-01

    We develop a framework for simulating a realistic, evolving social network (a city) into which a disease is introduced. We compare our results to prevaccine era measles data for England and Wales, and find that they capture the quantitative and qualitative features of epidemics in populations spanning two orders of magnitude. Our results provide unique insight into how and why the social topology of the contact network influences the propagation of the disease through the population. We argue that network simulation is suitable for concurrently probing contact network dynamics and disease dynamics in ways that prior modeling approaches cannot and it can be extended to the study of less well-documented diseases.

  9. Searching social networks for subgraph patterns

    NASA Astrophysics Data System (ADS)

    Ogaard, Kirk; Kase, Sue; Roy, Heather; Nagi, Rakesh; Sambhoos, Kedar; Sudit, Moises

    2013-06-01

    Software tools for Social Network Analysis (SNA) are being developed which support various types of analysis of social networks extracted from social media websites (e.g., Twitter). Once extracted and stored in a database such social networks are amenable to analysis by SNA software. This data analysis often involves searching for occurrences of various subgraph patterns (i.e., graphical representations of entities and relationships). The authors have developed the Graph Matching Toolkit (GMT) which provides an intuitive Graphical User Interface (GUI) for a heuristic graph matching algorithm called the Truncated Search Tree (TruST) algorithm. GMT is a visual interface for graph matching algorithms processing large social networks. GMT enables an analyst to draw a subgraph pattern by using a mouse to select categories and labels for nodes and links from drop-down menus. GMT then executes the TruST algorithm to find the top five occurrences of the subgraph pattern within the social network stored in the database. GMT was tested using a simulated counter-insurgency dataset consisting of cellular phone communications within a populated area of operations in Iraq. The results indicated GMT (when executing the TruST graph matching algorithm) is a time-efficient approach to searching large social networks. GMT's visual interface to a graph matching algorithm enables intelligence analysts to quickly analyze and summarize the large amounts of data necessary to produce actionable intelligence.

  10. Social network analysis and dual rover communications

    NASA Astrophysics Data System (ADS)

    Litaker, Harry L.; Howard, Robert L.

    2013-10-01

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

  11. CORRELATION PROFILES AND MOTIFS IN COMPLEX NETWORKS.

    SciTech Connect

    MASLOV,S.SNEPPEN,K.ALON,U.

    2004-01-16

    Networks have recently emerged as a unifying theme in complex systems research [1]. It is in fact no coincidence that networks and complexity are so heavily intertwined. Any future definition of a complex system should reflect the fact that such systems consist of many mutually interacting components. These components are far from being identical as say electrons in systems studied by condensed matter physics. In a truly complex system each of them has a unique identity allowing one to separate it from the others. The very first question one may ask about such a system is which other components a given component interacts with? This information system wide can be visualized as a graph, whose nodes correspond to individual components of the complex system in question and edges to their mutual interactions. Such a network can be thought of as a backbone of the complex system. Of course, system's dynamics depends not only on the topology of an underlying network but also on the exact form of interaction of components with each other, which can be very different in various complex systems. However, the underlying network may contain clues about the basic design principles and/or evolutionary history of the complex system in question. The goal of this article is to provide readers with a set of useful tools that would help to decide which features of a complex network are there by pure chance alone, and which of them were possibly designed or evolved to their present state.

  12. Jamming in complex networks with degree correlation

    NASA Astrophysics Data System (ADS)

    Pastore Y Piontti, Ana; Braunstein, Lidia; Macri, Pablo

    2012-02-01

    We study the effects of the degree-degree correlations on the pressure congestion J for a diffusive transport process on scale free complex networks. Using the gradient network approach we find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally, we apply our transportation process to real world networks, and the results agree with our findings for model networks.

  13. Revealing the Hidden Language of Complex Networks

    NASA Astrophysics Data System (ADS)

    Yaveroğlu, Ömer Nebil; Malod-Dognin, Noël; Davis, Darren; Levnajic, Zoran; Janjic, Vuk; Karapandza, Rasa; Stojmirovic, Aleksandar; Pržulj, Nataša

    2014-04-01

    Sophisticated methods for analysing complex networks promise to be of great benefit to almost all scientific disciplines, yet they elude us. In this work, we make fundamental methodological advances to rectify this. We discover that the interaction between a small number of roles, played by nodes in a network, can characterize a network's structure and also provide a clear real-world interpretation. Given this insight, we develop a framework for analysing and comparing networks, which outperforms all existing ones. We demonstrate its strength by uncovering novel relationships between seemingly unrelated networks, such as Facebook, metabolic, and protein structure networks. We also use it to track the dynamics of the world trade network, showing that a country's role of a broker between non-trading countries indicates economic prosperity, whereas peripheral roles are associated with poverty. This result, though intuitive, has escaped all existing frameworks. Finally, our approach translates network topology into everyday language, bringing network analysis closer to domain scientists.

  14. Social networking policies in nursing education.

    PubMed

    Frazier, Blake; Culley, Joan M; Hein, Laura C; Williams, Amber; Tavakoli, Abbas S

    2014-03-01

    Social networking use has increased exponentially in the past few years. A literature review related to social networking and nursing revealed a research gap between nursing practice and education. Although there was information available on the appropriate use of social networking sites, there was limited research on the use of social networking policies within nursing education. The purpose of this study was to identify current use of social media by faculty and students and a need for policies within nursing education at one institution. A survey was developed and administered to nursing students (n = 273) and nursing faculty (n = 33). Inferential statistics included χ², Fisher exact test, t test, and General Linear Model. Cronbach's α was used to assess internal consistency of social media scales. The χ² result indicates that there were associations with the group and several social media items. t Test results indicate significant differences between student and faculty for average of policies are good (P = .0127), policies and discipline (P = .0315), and policy at the study school (P = .0013). General Linear Model analyses revealed significant differences for "friend" a patient with a bond, unprofessional posts, policy, and nursing with class level. Results showed that students and faculty supported the development of a social networking policy.

  15. Social networks in improvement of health care.

    PubMed

    Masic, Izet; Sivic, Suad; Toromanovic, Selim; Borojevic, Tea; Pandza, Haris

    2012-01-01

    Social network is a social structure made of individuals or organizations associated with one or more types of interdependence (friendship, common interests, work, knowledge, prestige, etc.) which are the "nodes" of the network. Networks can be organized to exchange information, knowledge or financial assistance under the various interest groups in universities, workplaces and associations of citizens. Today the most popular and widely used networks are based on application of the Internet as the main ICT. Depending on the method of connection, their field of activity and expertise of those who participate in certain networks, the network can be classified into the following groups: a) Social Networks with personal physical connectivity (the citizens' associations, transplant networks, etc.), b) Global social internet network (Facebook, Twitter, Skype), c) specific health internet social network (forums, Health Care Forums, Healthcare Industry Forum), d) The health community internet network of non professionals (DailyStrength, CaringBridge, CarePages, MyFamilyHealth), e) Scientific social internet network (BiomedExperts, ResearchGate, iMedExchange), f) Social internet network which supported professionals (HealthBoards, Spas and Hope Association of Disabled and diabetic Enurgi), g) Scientific medical internet network databases in the system of scientific and technical information (CC, Pubmed/Medline, Excerpta Medica/EMBASE, ISI Web Knowledge, EBSCO, Index Copernicus, Social Science Index, etc.). The information in the network are exchanged in real time and in a way that has until recently been impossible in real life of people in the community. Networks allow tens of thousands of specific groups of people performing a series of social, professional and educational activities in the place of living and housing, place of work or other locations where individuals are. Network provides access to information related to education, health, nutrition, drugs, procedures

  16. Center of mass in complex networks

    NASA Astrophysics Data System (ADS)

    Fu, Chuanji; Gao, Yachun; Cai, Shimin; Yang, Hongchun; Yang, Chun

    2017-01-01

    Network dynamics is always a big challenge in nonlinear dynamics. Although great advancements have been made in various types of complex systems, an universal theoretical framework is required. In this paper, we introduce the concept of center of ‘mass’ of complex networks, where ‘mass’ stands for node importance or centrality in contrast to that of particle systems, and further prove that the phase transition and evolutionary state of the system can be characterized by the activity of center of ‘mass’. The steady states of several complex networks (gene regulatory networks and epidemic spreading systems) are then studied by analytically calculating the decoupled equation of the dynamic activity of center of ‘mass’, which is derived from the dynamic equation of the complex networks. The limitations of this method are also pointed out, such as the dynamical problems that related with the relative activities among components, and those systems that consist of oscillatory or chaotic motions.

  17. Social ecological complexity and resilience processes.

    PubMed

    Ungar, Michael

    2015-01-01

    A social ecological model of resilience avoids the reductionism of simple explanations of the complex and multisystemic processes associated with well-being in contexts of adversity. There is evidence that when stressors are abnormally high, environmental factors account for more of an individual's resilience than do individual traits or cognitions. In this commentary, a social ecological model of resilience is discussed.

  18. Model of community emergence in weighted social networks

    NASA Astrophysics Data System (ADS)

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

    2009-04-01

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

  19. Competitive Dynamics on Complex Networks

    NASA Astrophysics Data System (ADS)

    Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan

    2014-07-01

    We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition.

  20. Competitive Dynamics on Complex Networks

    PubMed Central

    Zhao, Jiuhua; Liu, Qipeng; Wang, Xiaofan

    2014-01-01

    We consider a dynamical network model in which two competitors have fixed and different states, and each normal agent adjusts its state according to a distributed consensus protocol. The state of each normal agent converges to a steady value which is a convex combination of the competitors' states, and is independent of the initial states of agents. This implies that the competition result is fully determined by the network structure and positions of competitors in the network. We compute an Influence Matrix (IM) in which each element characterizing the influence of an agent on another agent in the network. We use the IM to predict the bias of each normal agent and thus predict which competitor will win. Furthermore, we compare the IM criterion with seven node centrality measures to predict the winner. We find that the competitor with higher Katz Centrality in an undirected network or higher PageRank in a directed network is most likely to be the winner. These findings may shed new light on the role of network structure in competition and to what extent could competitors adjust network structure so as to win the competition. PMID:25068622

  1. A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression

    PubMed Central

    So, Nina; Franks, Becca; Lim, Sean; Curley, James P.

    2015-01-01

    Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David’s Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg’s Hub Centrality and Bonacich’s Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive

  2. A Social Network Approach Reveals Associations between Mouse Social Dominance and Brain Gene Expression.

    PubMed

    So, Nina; Franks, Becca; Lim, Sean; Curley, James P

    2015-01-01

    Modelling complex social behavior in the laboratory is challenging and requires analyses of dyadic interactions occurring over time in a physically and socially complex environment. In the current study, we approached the analyses of complex social interactions in group-housed male CD1 mice living in a large vivarium. Intensive observations of social interactions during a 3-week period indicated that male mice form a highly linear and steep dominance hierarchy that is maintained by fighting and chasing behaviors. Individual animals were classified as dominant, sub-dominant or subordinate according to their David's Scores and I& SI ranking. Using a novel dynamic temporal Glicko rating method, we ascertained that the dominance hierarchy was stable across time. Using social network analyses, we characterized the behavior of individuals within 66 unique relationships in the social group. We identified two individual network metrics, Kleinberg's Hub Centrality and Bonacich's Power Centrality, as accurate predictors of individual dominance and power. Comparing across behaviors, we establish that agonistic, grooming and sniffing social networks possess their own distinctive characteristics in terms of density, average path length, reciprocity out-degree centralization and out-closeness centralization. Though grooming ties between individuals were largely independent of other social networks, sniffing relationships were highly predictive of the directionality of agonistic relationships. Individual variation in dominance status was associated with brain gene expression, with more dominant individuals having higher levels of corticotropin releasing factor mRNA in the medial and central nuclei of the amygdala and the medial preoptic area of the hypothalamus, as well as higher levels of hippocampal glucocorticoid receptor and brain-derived neurotrophic factor mRNA. This study demonstrates the potential and significance of combining complex social housing and intensive

  3. Hypothesis testing in animal social networks.

    PubMed

    Croft, Darren P; Madden, Joah R; Franks, Daniel W; James, Richard

    2011-10-01

    Behavioural ecologists are increasingly using social network analysis to describe the social organisation of animal populations and to test hypotheses. However, the statistical analysis of network data presents a number of challenges. In particular the non-independent nature of the data violates the assumptions of many common statistical approaches. In our opinion there is currently confusion and uncertainty amongst behavioural ecologists concerning the potential pitfalls when hypotheses testing using social network data. Here we review what we consider to be key considerations associated with the analysis of animal social networks and provide a practical guide to the use of null models based on randomisation to control for structure and non-independence in the data.

  4. Percolation of localized attack on complex networks

    NASA Astrophysics Data System (ADS)

    Shao, Shuai; Huang, Xuqing; Stanley, H. Eugene; Havlin, Shlomo

    2015-02-01

    The robustness of complex networks against node failure and malicious attack has been of interest for decades, while most of the research has focused on random attack or hub-targeted attack. In many real-world scenarios, however, attacks are neither random nor hub-targeted, but localized, where a group of neighboring nodes in a network are attacked and fail. In this paper we develop a percolation framework to analytically and numerically study the robustness of complex networks against such localized attack. In particular, we investigate this robustness in Erdős-Rényi networks, random-regular networks, and scale-free networks. Our results provide insight into how to better protect networks, enhance cybersecurity, and facilitate the design of more robust infrastructures.

  5. A random interacting network model for complex networks

    PubMed Central

    Goswami, Bedartha; Shekatkar, Snehal M.; Rheinwalt, Aljoscha; Ambika, G.; Kurths, Jürgen

    2015-01-01

    We propose a RAndom Interacting Network (RAIN) model to study the interactions between a pair of complex networks. The model involves two major steps: (i) the selection of a pair of nodes, one from each network, based on intra-network node-based characteristics, and (ii) the placement of a link between selected nodes based on the similarity of their relative importance in their respective networks. Node selection is based on a selection fitness function and node linkage is based on a linkage probability defined on the linkage scores of nodes. The model allows us to relate within-network characteristics to between-network structure. We apply the model to the interaction between the USA and Schengen airline transportation networks (ATNs). Our results indicate that two mechanisms: degree-based preferential node selection and degree-assortative link placement are necessary to replicate the observed inter-network degree distributions as well as the observed inter-network assortativity. The RAIN model offers the possibility to test multiple hypotheses regarding the mechanisms underlying network interactions. It can also incorporate complex interaction topologies. Furthermore, the framework of the RAIN model is general and can be potentially adapted to various real-world complex systems. PMID:26657032

  6. COMPLEX NETWORKS IN CLIMATE SCIENCE: PROGRESS, OPPORTUNITIES AND CHALLENGES

    SciTech Connect

    Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

    2010-01-01

    Networks have been used to describe and model a wide range of complex systems, both natural as well as man-made. One particularly interesting application in the earth sciences is the use of complex networks to represent and study the global climate system. In this paper, we motivate this general approach, explain the basic methodology, report on the state of the art (including our contributions), and outline open questions and opportunities for future research. Datasets and systems that can be represented as interaction networks (or graphs), broadly defined as any collection of interrelated objects or entities, have received considerable attention both from a theoretical viewpoint as well as various application domains; examples include the analysis of social networks, chemical interactions between proteins, the behavior of financial markets, and many others. Recently, the study of complex networks - that is, networks which exhibit non-trivial topological properties - has permeated numerous fields and disciplines spanning the physical, social, and computational sciences. So why do networks enjoy such broad appeal? Briefly, it is their ability to serve at once as a data representation, as an analysis framework, and as a visualization tool. The analytic capabilities in particular are quite powerful, as networks can uncover structure and patterns at multiple scales, ranging from local properties to global phenomena, and thus help better understand the characteristics of complex systems. We focus on one particular application of networks in the earth sciences, namely, the construction and analysis of climate networks. Identifying and analyzing patterns in global climate is an important task of growing scientific, social, and political interest, with the goal of deepening our understanding of the complex processes underlying observed phenomena. To this end, we make the case that complex networks offer a compelling perspective for capturing the dynamics of the climate

  7. Managing Complex Network Operation with Predictive Analytics

    SciTech Connect

    Huang, Zhenyu; Wong, Pak C.; Mackey, Patrick S.; Chen, Yousu; Ma, Jian; Schneider, Kevin P.; Greitzer, Frank L.

    2008-03-26

    Complex networks play an important role in modern societies. Their failures, such as power grid blackouts, would lead to significant disruption of people’s life, industry and commercial activities, and result in massive economic losses. Operation of these complex networks is an extremely challenging task due to their complex structures, wide geographical coverage, complex data/information technology systems, and highly dynamic and nonlinear behaviors. None of the complex network operation is fully automated; human-in-the-loop operation is critical. Given the complexity involved, there may be thousands of possible topological configurations at any given time. During an emergency, it is not uncommon for human operators to examine thousands of possible configurations in near real-time to choose the best option and operate the network effectively. In today’s practice, network operation is largely based on experience with very limited real-time decision support, resulting in inadequate management of complex predictions and inability to anticipate, recognize, and respond to situations caused by human errors, natural disasters, and cyber attacks. A systematic approach is needed to manage the complex operation paradigms and choose the best option in a near-real-time manner. This paper applies predictive analytics techniques to establish a decision support system for complex network operation management and help operators to predict potential network failures and adapt the network to adverse situations. The resultant decision support system enables continuous monitoring of network performance and turns large amounts of data into actionable information. Examples with actual power grid data are presented to demonstrate the capability of this proposed decision support system.

  8. The Dynamics of Coalition Formation on Complex Networks

    NASA Astrophysics Data System (ADS)

    Auer, S.; Heitzig, J.; Kornek, U.; Schöll, E.; Kurths, J.

    2015-08-01

    Complex networks describe the structure of many socio-economic systems. However, in studies of decision-making processes the evolution of the underlying social relations are disregarded. In this report, we aim to understand the formation of self-organizing domains of cooperation (“coalitions”) on an acquaintance network. We include both the network’s influence on the formation of coalitions and vice versa how the network adapts to the current coalition structure, thus forming a social feedback loop. We increase complexity from simple opinion adaptation processes studied in earlier research to more complex decision-making determined by costs and benefits, and from bilateral to multilateral cooperation. We show how phase transitions emerge from such coevolutionary dynamics, which can be interpreted as processes of great transformations. If the network adaptation rate is high, the social dynamics prevent the formation of a grand coalition and therefore full cooperation. We find some empirical support for our main results: Our model develops a bimodal coalition size distribution over time similar to those found in social structures. Our detection and distinguishing of phase transitions may be exemplary for other models of socio-economic systems with low agent numbers and therefore strong finite-size effects.

  9. Social Network Privacy via Evolving Access Control

    NASA Astrophysics Data System (ADS)

    di Crescenzo, Giovanni; Lipton, Richard J.

    We study the problem of limiting privacy loss due to data shared in a social network, where the basic underlying assumptions are that users are interested in sharing data and cannot be assumed to constantly follow appropriate privacy policies. Note that if these two assumptions do not hold, social network privacy is theoretically very easy to achieve; for instance, via some form of access control and confidentiality transformation on the data.

  10. Brand communities embedded in social networks.

    PubMed

    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.

  11. Power grid vulnerability: a complex network approach.

    PubMed

    Arianos, S; Bompard, E; Carbone, A; Xue, F

    2009-03-01

    Power grids exhibit patterns of reaction to outages similar to complex networks. Blackout sequences follow power laws, as complex systems operating near a critical point. Here, the tolerance of electric power grids to both accidental and malicious outages is analyzed in the framework of complex network theory. In particular, the quantity known as efficiency is modified by introducing a new concept of distance between nodes. As a result, a new parameter called net-ability is proposed to evaluate the performance of power grids. A comparison between efficiency and net-ability is provided by estimating the vulnerability of sample networks, in terms of both the metrics.

  12. Power grid vulnerability: A complex network approach

    NASA Astrophysics Data System (ADS)

    Arianos, S.; Bompard, E.; Carbone, A.; Xue, F.

    2009-03-01

    Power grids exhibit patterns of reaction to outages similar to complex networks. Blackout sequences follow power laws, as complex systems operating near a critical point. Here, the tolerance of electric power grids to both accidental and malicious outages is analyzed in the framework of complex network theory. In particular, the quantity known as efficiency is modified by introducing a new concept of distance between nodes. As a result, a new parameter called net-ability is proposed to evaluate the performance of power grids. A comparison between efficiency and net-ability is provided by estimating the vulnerability of sample networks, in terms of both the metrics.

  13. Information diffusion in structured online social networks

    NASA Astrophysics Data System (ADS)

    Li, Pei; Zhang, Yini; Qiao, Fengcai; Wang, Hui

    2015-05-01

    Nowadays, due to the word-of-mouth effect, online social networks have been considered to be efficient approaches to conduct viral marketing, which makes it of great importance to understand the diffusion dynamics in online social networks. However, most research on diffusion dynamics in epidemiology and existing social networks cannot be applied directly to characterize online social networks. In this paper, we propose models to characterize the information diffusion in structured online social networks with push-based forwarding mechanism. We introduce the term user influence to characterize the average number of times that messages are browsed which is incurred by a given type user generating a message, and study the diffusion threshold, above which the user influence of generating a message will approach infinity. We conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of use in understanding the diffusion dynamics in online social networks and also critical for advertisers in viral marketing who want to estimate the user influence before posting an advertisement.

  14. Optimization of spatial complex networks

    NASA Astrophysics Data System (ADS)

    Guillier, S.; Muñoz, V.; Rogan, J.; Zarama, R.; Valdivia, J. A.

    2017-02-01

    First, we estimate the connectivity properties of a predefined (fixed node locations) spatial network which optimizes a connectivity functional that balances construction and transportation costs. In this case we obtain a Gaussian distribution for the connectivity. However, when we consider these spatial networks in a growing process, we obtain a power law distribution for the connectivity. If the transportation costs in the functional involve the shortest geometrical path, we obtain a scaling exponent γ = 2.5. However, if the transportation costs in the functional involve just the shortest path, we obtain γ = 2.2. Both cases may be useful to analyze in some real networks.

  15. Coupled disease-behavior dynamics on complex networks: A review.

    PubMed

    Wang, Zhen; Andrews, Michael A; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T

    2015-12-01

    It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

  16. Coupled disease-behavior dynamics on complex networks: A review

    NASA Astrophysics Data System (ADS)

    Wang, Zhen; Andrews, Michael A.; Wu, Zhi-Xi; Wang, Lin; Bauch, Chris T.

    2015-12-01

    It is increasingly recognized that a key component of successful infection control efforts is understanding the complex, two-way interaction between disease dynamics and human behavioral and social dynamics. Human behavior such as contact precautions and social distancing clearly influence disease prevalence, but disease prevalence can in turn alter human behavior, forming a coupled, nonlinear system. Moreover, in many cases, the spatial structure of the population cannot be ignored, such that social and behavioral processes and/or transmission of infection must be represented with complex networks. Research on studying coupled disease-behavior dynamics in complex networks in particular is growing rapidly, and frequently makes use of analysis methods and concepts from statistical physics. Here, we review some of the growing literature in this area. We contrast network-based approaches to homogeneous-mixing approaches, point out how their predictions differ, and describe the rich and often surprising behavior of disease-behavior dynamics on complex networks, and compare them to processes in statistical physics. We discuss how these models can capture the dynamics that characterize many real-world scenarios, thereby suggesting ways that policy makers can better design effective prevention strategies. We also describe the growing sources of digital data that are facilitating research in this area. Finally, we suggest pitfalls which might be faced by researchers in the field, and we suggest several ways in which the field could move forward in the coming years.

  17. Multimedia Information Networks in Social Media

    NASA Astrophysics Data System (ADS)

    Cao, Liangliang; Qi, Guojun; Tsai, Shen-Fu; Tsai, Min-Hsuan; Pozo, Andrey Del; Huang, Thomas S.; Zhang, Xuemei; Lim, Suk Hwan

    The popularity of personal digital cameras and online photo/video sharing community has lead to an explosion of multimedia information. Unlike traditional multimedia data, many new multimedia datasets are organized in a structural way, incorporating rich information such as semantic ontology, social interaction, community media, geographical maps, in addition to the multimedia contents by themselves. Studies of such structured multimedia data have resulted in a new research area, which is referred to as Multimedia Information Networks. Multimedia information networks are closely related to social networks, but especially focus on understanding the topics and semantics of the multimedia files in the context of network structure. This chapter reviews different categories of recent systems related to multimedia information networks, summarizes the popular inference methods used in recent works, and discusses the applications related to multimedia information networks. We also discuss a wide range of topics including public datasets, related industrial systems, and potential future research directions in this field.

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

    NASA Astrophysics Data System (ADS)

    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.

  19. Entropic origin of disassortativity in complex networks.

    PubMed

    Johnson, Samuel; Torres, Joaquín J; Marro, J; Muñoz, Miguel A

    2010-03-12

    Why are most empirical networks, with the prominent exception of social ones, generically degree-degree anticorrelated? To answer this long-standing question, we define the ensemble of correlated networks and obtain the associated Shannon entropy. Maximum entropy can correspond to either assortative (correlated) or disassortative (anticorrelated) configurations, but in the case of highly heterogeneous, scale-free networks a certain disassortativity is predicted--offering a parsimonious explanation for the question above. Our approach provides a neutral model from which, in the absence of further knowledge regarding network evolution, one can obtain the expected value of correlations. When empirical observations deviate from the neutral predictions--as happens for social networks--one can then infer that there are specific correlating mechanisms at work.

  20. Mother's Social Network and Family Language Maintenance

    ERIC Educational Resources Information Center

    Velazquez, Isabel

    2013-01-01

    This article reports the results of a social network analysis (SNA) performed on the mother's primary network of interaction in 15 Mexican American families in the city of El Paso, Texas, the neighbourhood of La Villita, in Chicago, and the city of Lincoln, Nebraska. The goal of this study was to examine potential opportunities for Spanish use by…

  1. The Elderly and Their Informal Social Networks.

    ERIC Educational Resources Information Center

    Thompson, J. Victor

    1989-01-01

    A sample of 334 people aged 56 and older living in British Columbia were interviewed about their supportive social network. Four supportive roles were investigated: caretaker, helper, confident, and advisor. The research supports earlier findings about the vulnerability of widows over 74 years old. They are most in need of networks. (Author/JOW)

  2. A Layered Social and Operational Network Analysis

    DTIC Science & Technology

    2007-03-01

    Library and Information Science Research, 18: 323 – 342 (1996). Herbranson, Travis. Isolating Key Players in Clandestine Networks. MS thesis...07 A LAYERED SOCIAL AND OPERATIONAL NETWORK ANALYSIS THESIS Presented to the Faculty Department of Operational Sciences ...Command In Partial Fulfillment of the Requirements for the Degree of Master of Science in Operations Research Jennifer L. Geffre, BS

  3. Universality in complex networks: random matrix analysis.

    PubMed

    Bandyopadhyay, Jayendra N; Jalan, Sarika

    2007-08-01

    We apply random matrix theory to complex networks. We show that nearest neighbor spacing distribution of the eigenvalues of the adjacency matrices of various model networks, namely scale-free, small-world, and random networks follow universal Gaussian orthogonal ensemble statistics of random matrix theory. Second, we show an analogy between the onset of small-world behavior, quantified by the structural properties of networks, and the transition from Poisson to Gaussian orthogonal ensemble statistics, quantified by Brody parameter characterizing a spectral property. We also present our analysis for a protein-protein interaction network in budding yeast.

  4. Jamming in complex networks with degree correlation

    NASA Astrophysics Data System (ADS)

    Pastore y Piontti, Ana L.; Braunstein, Lidia A.; Macri, Pablo A.

    2010-10-01

    We study the effects of the degree-degree correlations on the pressure congestion J when we apply a dynamical process on scale free complex networks using the gradient network approach. We find that the pressure congestion for disassortative (assortative) networks is lower (bigger) than the one for uncorrelated networks which allow us to affirm that disassortative networks enhance transport through them. This result agree with the fact that many real world transportation networks naturally evolve to this kind of correlation. We explain our results showing that for the disassortative case the clusters in the gradient network turn out to be as much elongated as possible, reducing the pressure congestion J and observing the opposite behavior for the assortative case. Finally we apply our model to real world networks, and the results agree with our theoretical model.

  5. Optimal dynamic bandwidth allocation for complex networks

    NASA Astrophysics Data System (ADS)

    Jiang, Zhong-Yuan; Liang, Man-Gui; Li, Qian; Guo, Dong-Chao

    2013-03-01

    Traffic capacity of one network strongly depends on the link’s bandwidth allocation strategy. In previous bandwidth allocation mechanisms, once one link’s bandwidth is allocated, it will be fixed throughout the overall traffic transmission process. However, the traffic load of every link changes from time to time. In this paper, with finite total bandwidth resource of the network, we propose to dynamically allocate the total bandwidth resource in which each link’s bandwidth is proportional to the queue length of the output buffer of the link per time step. With plenty of data packets in the network, the traffic handling ability of all links of the network achieves full utilization. The theoretical analysis and the extensive simulation results on complex networks are consistent. This work is valuable for network service providers to improve network performance or to do reasonable network design efficiently.

  6. Aversive Peer Experiences on Social Networking Sites: Development of the Social Networking-Peer Experiences Questionnaire (SN-PEQ).

    PubMed

    Landoll, Ryan R; La Greca, Annette M; Lai, Betty S

    2013-12-01

    Cyber victimization is an important research area; yet, little is known about aversive peer experiences on social networking sites (SNSs), which are used extensively by youth and host complex social exchanges. Across samples of adolescents (n=216) and young adults (n=214), we developed the Social Networking-Peer Experiences Questionnaire (SN-PEQ), and examined its psychometric properties, distinctiveness from traditional peer victimization, and associations with internalized distress. The SN-PEQ demonstrated strong factorial invariance and a single factor structure that was distinct from other forms of peer victimization. Negative SNS experiences were associated with youths' symptoms of social anxiety and depression, even when controlling for traditional peer victimization. Findings highlight the importance of examining the effects of aversive peer experiences that occur via social media.

  7. Aversive Peer Experiences on Social Networking Sites: Development of the Social Networking-Peer Experiences Questionnaire (SN-PEQ)

    PubMed Central

    Landoll, Ryan R.; La Greca, Annette M.; Lai, Betty S.

    2012-01-01

    Cyber victimization is an important research area; yet, little is known about aversive peer experiences on social networking sites (SNSs), which are used extensively by youth and host complex social exchanges. Across samples of adolescents (n=216) and young adults (n=214), we developed the Social Networking-Peer Experiences Questionnaire (SN-PEQ), and examined its psychometric properties, distinctiveness from traditional peer victimization, and associations with internalized distress. The SN-PEQ demonstrated strong factorial invariance and a single factor structure that was distinct from other forms of peer victimization. Negative SNS experiences were associated with youths’ symptoms of social anxiety and depression, even when controlling for traditional peer victimization. Findings highlight the importance of examining the effects of aversive peer experiences that occur via social media. PMID:24288449

  8. Network Analysis in Comparative Social Sciences

    ERIC Educational Resources Information Center

    Vera, Eugenia Roldan; Schupp, Thomas

    2006-01-01

    This essay describes the pertinence of Social Network Analysis (SNA) for the social sciences in general, and discusses its methodological and conceptual implications for comparative research in particular. The authors first present a basic summary of the theoretical and methodological assumptions of SNA, followed by a succinct overview of its…

  9. College Students' Social Networking Experiences on Facebook

    ERIC Educational Resources Information Center

    Pempek, Tiffany A.; Yermolayeva, Yevdokiya A.; Calvert, Sandra L.

    2009-01-01

    Millions of contemporary young adults use social networking sites. However, little is known about how much, why, and how they use these sites. In this study, 92 undergraduates completed a diary-like measure each day for a week, reporting daily time use and responding to an activities checklist to assess their use of the popular social networking…

  10. Online Formative Assessments with Social Network Awareness

    ERIC Educational Resources Information Center

    Lin, Jian-Wei; Lai, Yuan-Cheng

    2013-01-01

    Social network awareness (SNA) has been used extensively as one of the strategies to increase knowledge sharing and collaboration opportunities. However, most SNA studies either focus on being aware of peer's knowledge context or on social context. This work proposes online formative assessments with SNA, trying to address the problems of online…

  11. Spatial and Social Networks in Organizational Innovation

    ERIC Educational Resources Information Center

    Wineman, Jean D.; Kabo, Felichism W.; Davis, Gerald F.

    2009-01-01

    Research on the enabling factors of innovation has focused on either the social component of organizations or on the spatial dimensions involved in the innovation process. But no one has examined the aggregate consequences of the link from spatial layout, to social networks, to innovation. This project enriches our understanding of how innovation…

  12. Facebook, Social Networking, and Business Education

    ERIC Educational Resources Information Center

    Taylor, Steven A.; Mulligan, Jamie R.; Ishida, Chiharu

    2012-01-01

    Brown (2012) asserts that faculty perceptions of Web 2.0 for teaching will influence its adoption. For example, social media's influence on educational delivery is growing (Hrastinski and Dennon 2012). Zulu et al. (2011) note that business educators are only beginning to understand social networking related to education. We report an exploratory…

  13. Creating Socially Networked Knowledge through Interdisciplinary Collaboration

    ERIC Educational Resources Information Center

    Chuk, Eric; Hoetzlein, Rama; Kim, David; Panko, Julia

    2012-01-01

    We report on the experience of creating a socially networked system, the Research-oriented Social Environment (RoSE), for representing knowledge in the form of relationships between people, documents, and groups. Developed as an intercampus, interdisciplinary project of the University of California, this work reflects on a collaboration between…

  14. Social media networking: Facebook and Twitter.

    PubMed

    Schneider, Andrew; Jackson, Rem; Baum, Neil

    2010-01-01

    The new wave of marketing and practice promotion will include social media networking. This article will discuss Facebook and Twitter. After reading this article you, will have an understanding of these two important aspects of social media and how you might use Facebook and Twitter in your practice to enhance your communication with your existing patients and attract new patients.

  15. Social Networks and Home-Schooling Relations.

    ERIC Educational Resources Information Center

    Graue, M. Elizabeth

    1993-01-01

    Describes how parents in two communities constructed their roles in a social context shaped by economic resources, local notions of home-school relations, and information networks. Examines parenting as a social activity internalized by individuals through their daily interactions. Shows how family-role perceptions and school practices are…

  16. District Policy and Teachers' Social Networks

    ERIC Educational Resources Information Center

    Coburn, Cynthia E.; Russell, Jennifer Lin

    2008-01-01

    Policy makers increasingly include provisions aimed at fostering professional community as part of reform initiatives. Yet little is known about the impact of policy on teachers' professional relations in schools. Drawing theoretically from social capital theory and methodologically from qualitative social network analysis, this article explores…

  17. Traffic congestion in interconnected complex networks

    NASA Astrophysics Data System (ADS)

    Tan, Fei; Wu, Jiajing; Xia, Yongxiang; Tse, Chi K.

    2014-06-01

    Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.

  18. Traffic congestion in interconnected complex networks.

    PubMed

    Tan, Fei; Wu, Jiajing; Xia, Yongxiang; Tse, Chi K

    2014-06-01

    Traffic congestion in isolated complex networks has been investigated extensively over the last decade. Coupled network models have recently been developed to facilitate further understanding of real complex systems. Analysis of traffic congestion in coupled complex networks, however, is still relatively unexplored. In this paper, we try to explore the effect of interconnections on traffic congestion in interconnected Barabási-Albert scale-free networks. We find that assortative coupling can alleviate traffic congestion more readily than disassortative and random coupling when the node processing capacity is allocated based on node usage probability. Furthermore, the optimal coupling probability can be found for assortative coupling. However, three types of coupling preferences achieve similar traffic performance if all nodes share the same processing capacity. We analyze interconnected Internet autonomous-system-level graphs of South Korea and Japan and obtain similar results. Some practical suggestions are presented to optimize such real-world interconnected networks accordingly.

  19. Visual Matrix Clustering of Social Networks

    SciTech Connect

    Wong, Pak C.; Mackey, Patrick S.; Foote, Harlan P.; May, Richard A.

    2013-07-01

    The prevailing choices to graphically represent a social network in today’s literature are a node-link graph layout and an adjacency matrix. Both visualization techniques have unique strengths and weaknesses when applied to different domain applications. In this article, we focus our discussion on adjacency matrix and how to turn the matrix-based visualization technique from merely showing pairwise associations among network actors (or graph nodes) to depicting clusters of a social network. We also use node-link layouts to supplement the discussion.

  20. Predicting fate from early connectivity in a social network.

    PubMed

    McDonald, David B

    2007-06-26

    In the long-tailed manakin (Chiroxiphia linearis), a long-lived tropical bird, early connectivity within a social network predicts male success an average of 4.8 years later. Long-tailed manakins have an unusual lek mating system in which pairs of unrelated males, at the top of complex overlapping teams of as many as 15 males, cooperate for obligate dual-male song and dance courtship displays. For as long as 8 years before forming stable "alpha-beta" partnerships, males interact with many other males in complex, temporally dynamic social networks. "Information centrality" is a network connectivity metric that accounts for indirect as well as shortest (geodesic) paths among interactors. The odds that males would rise socially rose by a factor of five for each one-unit increase in their early information centrality. Connectivity of males destined to rise did not change over time but increased in males that failed to rise socially. The results suggest that network connectivity is important for young males (ages 1-6) but less so for older males of high status (ages 10-15) and that it is difficult to explain present success without reference to social history.

  1. Prisoner's dilemma on real social networks: revisited.

    PubMed

    Cameron, Sharon M; Cintron-Arias, Ariel

    2013-01-01

    Prisoner's Dilemma is a game theory model used to describe altruistic behavior seen in various populations. This theoretical game is important in understanding why a seemingly selfish strategy does persist and spread throughout a population that is mixing homogeneously at random. For a population with structure determined by social interactions, Prisoner's Dilemma brings to light certain requirements for the altruistic strategy to become established. Monte Carlo simulations of Prisoner's Dilemma are carried out using both simulated social networks and a dataset of a real social network. In both scenarios we confirm the requirements for the persistence of altruism in a population.

  2. Social Networks and the Maintenance of Conformity: Japanese sojourner women

    PubMed Central

    Saint Arnault, Denise; Roles, Deborah J.

    2011-01-01

    Asian immigrant women have the lowest utilization of mental health services of any ethnic minority (Garland, Lau, Yeh & McCabe 2005). Because help seeking for distress occurs within social networks, we examined how social networks supported or disabled help seeking for Japanese sojourners living in the US. Unfortunately, most of the literature about Japanese social relationships focuses on men in organizational settings. This study used intensive ethnographic interviewing with 49 Japanese expatriate women to examine how social relationships influenced psychosocial distress and help seeking. We found that the women in these samples engaged in complex, highly regulated, complicated and obligatory relationships through their primary affiliation with other “company wives.” Like many immigrant women, increased traditional cultural norms (referred to in Japanese as ryoosai kenbo, or good wives and wise mothers), were expected from these modern women, and the enactment of these roles was enforced through scrutiny, gossip and the possibility of ostracism. Fears of scrutiny was described by the women as a primary barrier to their self-disclosure and ultimate help seeking. Understanding the social organization and support within the Japanese women's community is central to understanding how culturally specific social networks can both give support, as well as create social constraints to help seeking. Health oriented prevention programs must consider these social factors when evaluating the immigration stressors faced by these families. PMID:23162609

  3. Synchronization reveals topological scales in complex networks.

    PubMed

    Arenas, Alex; Díaz-Guilera, Albert; Pérez-Vicente, Conrad J

    2006-03-24

    We study the relationship between topological scales and dynamic time scales in complex networks. The analysis is based on the full dynamics towards synchronization of a system of coupled oscillators. In the synchronization process, modular structures corresponding to well-defined communities of nodes emerge in different time scales, ordered in a hierarchical way. The analysis also provides a useful connection between synchronization dynamics, complex networks topology, and spectral graph analysis.

  4. How Do Online Social Networks Grow?

    PubMed Central

    Zhu, Konglin; Li, Wenzhong; Fu, Xiaoming; Nagler, Jan

    2014-01-01

    Online social networks such as Facebook, Twitter and Gowalla allow people to communicate and interact across borders. In past years online social networks have become increasingly important for studying the behavior of individuals, group formation, and the emergence of online societies. Here we focus on the characterization of the average growth of online social networks and try to understand which are possible processes behind seemingly long-range temporal correlated collective behavior. In agreement with recent findings, but in contrast to Gibrat's law of proportionate growth, we find scaling in the average growth rate and its standard deviation. In contrast, Renren and Twitter deviate, however, in certain important aspects significantly from those found in many social and economic systems. Whereas independent methods suggest no significance for temporally long-range correlated behavior for Renren and Twitter, a scaling analysis of the standard deviation does suggest long-range temporal correlated growth in Gowalla. However, we demonstrate that seemingly long-range temporal correlations in the growth of online social networks, such as in Gowalla, can be explained by a decomposition into temporally and spatially independent growth processes with a large variety of entry rates. Our analysis thus suggests that temporally or spatially correlated behavior does not play a major role in the growth of online social networks. PMID:24940744

  5. Topology Analysis of Social Networks Extracted from Literature

    PubMed Central

    2015-01-01

    In a world where complex networks are an increasingly important part of science, it is interesting to question how the new reading of social realities they provide applies to our cultural background and in particular, popular culture. Are authors of successful novels able to reproduce social networks faithful to the ones found in reality? Is there any common trend connecting an author’s oeuvre, or a genre of fiction? Such an analysis could provide new insight on how we, as a culture, perceive human interactions and consume media. The purpose of the work presented in this paper is to define the signature of a novel’s story based on the topological analysis of its social network of characters. For this purpose, an automated tool was built that analyses the dialogs in novels, identifies characters and computes their relationships in a time-dependent manner in order to assess the network’s evolution over the course of the story. PMID:26039072

  6. Discovering Influential Nodes for SIS Models in Social Networks

    NASA Astrophysics Data System (ADS)

    Saito, Kazumi; Kimura, Masahiro; Motoda, Hiroshi

    We address the problem of efficiently discovering the influential nodes in a social network under the susceptible/infected/susceptible (SIS) model, a diffusion model where nodes are allowed to be activated multiple times. The computational complexity drastically increases because of this multiple activation property. We solve this problem by constructing a layered graph from the original social network with each layer added on top as the time proceeds, and applying the bond percolation with pruning and burnout strategies. We experimentally demonstrate that the proposed method gives much better solutions than the conventional methods that are solely based on the notion of centrality for social network analysis using two large-scale real-world networks (a blog network and a wikipedia network). We further show that the computational complexity of the proposed method is much smaller than the conventional naive probabilistic simulation method by a theoretical analysis and confirm this by experimentation. The properties of the influential nodes discovered are substantially different from those identified by the centrality-based heuristic methods.

  7. Study of complex networks using statistical physics methods

    NASA Astrophysics Data System (ADS)

    Chen, Yiping

    The goal of this thesis is to study the behaviors of complex networks in several aspects using methods from statistical physics. Networks are structures that consist of nodes and links. By changing the way links connect to nodes, different complex network structures can be constructed such as Erdḧs-Renyi (ER) networks and scale-free (SF) networks. Complex networks have wide relevance to many real world problems, including the spread of disease in human society, message routing in the Internet, etc. In this thesis analytical and simulation results are obtained regarding optimal paths in disordered networks, fragmentation of social networks, and improved strategies for immunization against diseases. In the study of disordered systems, of particular current interest is the scaling behavior of the optimal path length ℓopt from strong disorder to weak disorder state for different weight distributions P(w). Here we derive analytically a new criterion. Using this criterion we find that for all P(w) that possess a strong-weak disorder crossover, the distributions p(ℓ) of the optimal path lengths display the same universal behavior. Fragmentation in social networks is also studied using methods from percolation theory. Recently, a new measure of fragmentation F has been developed in social network studies. For each removal of a subset of links or nodes, F is defined as the ratio between the number of pairs of nodes that are not connected in the fragmented network after removal, and the total number of pairs in the original fully connected network. We study the statistical behavior of F using both analytical and numerical methods and relate it to the traditional measure of fragmentation, the relative size of the largest cluster, Pinfinity, used in percolation theory. Finally, we tried to find a better immunization strategy. It is widely accepted that the most efficient immunization strategies are based on "targeted" strategies. Here we propose a novel "equal graph

  8. Renormalization Group for Critical Phenomena in Complex Networks

    PubMed Central

    Boettcher, S.; Brunson, C. T.

    2011-01-01

    We discuss the behavior of statistical models on a novel class of complex “Hanoi” networks. Such modeling is often the cornerstone for the understanding of many dynamical processes in complex networks. Hanoi networks are special because they integrate small-world hierarchies common to many social and economical structures with the inevitable geometry of the real world these structures exist in. In addition, their design allows exact results to be obtained with the venerable renormalization group (RG). Our treatment will provide a detailed, pedagogical introduction to RG. In particular, we will study the Ising model with RG, for which the fixed points are determined and the RG flow is analyzed. We show that the small-world bonds result in non-universal behavior. It is shown that a diversity of different behaviors can be observed with seemingly small changes in the structure of hierarchical networks generally, and we provide a general theory to describe our findings. PMID:22194725

  9. Localized recovery of complex networks against failure

    NASA Astrophysics Data System (ADS)

    Shang, Yilun

    2016-07-01

    Resilience of complex networks to failure has been an important issue in network research for decades, and recent studies have begun to focus on the inverse recovery of network functionality through strategically healing missing nodes or edges. However, the effect of network recovery is far from fully understood, and a general theory is still missing. Here we propose and study a general model of localized recovery, where a group of neighboring nodes are restored in an invasive way from a seed node. We develop a theoretical framework to compare the effect of random recovery (RR) and localized recovery (LR) in complex networks including Erdős-Rényi networks, random regular networks, and scale-free networks. We find detailed phase diagrams for the subnetwork of occupied nodes and the “complement network” of failed nodes under RR and LR. By identifying the two competitive forces behind LR, we present an analytical and numerical approach to guide us in choosing the appropriate recovery strategy and provide estimation on its effect by using the degree distribution of the original network as the only input. Our work therefore provides insight for quantitatively understanding recovery process and its implications in infrastructure protection in various complex systems.

  10. Localized recovery of complex networks against failure

    PubMed Central

    Shang, Yilun

    2016-01-01

    Resilience of complex networks to failure has been an important issue in network research for decades, and recent studies have begun to focus on the inverse recovery of network functionality through strategically healing missing nodes or edges. However, the effect of network recovery is far from fully understood, and a general theory is still missing. Here we propose and study a general model of localized recovery, where a group of neighboring nodes are restored in an invasive way from a seed node. We develop a theoretical framework to compare the effect of random recovery (RR) and localized recovery (LR) in complex networks including Erdős-Rényi networks, random regular networks, and scale-free networks. We find detailed phase diagrams for the subnetwork of occupied nodes and the “complement network” of failed nodes under RR and LR. By identifying the two competitive forces behind LR, we present an analytical and numerical approach to guide us in choosing the appropriate recovery strategy and provide estimation on its effect by using the degree distribution of the original network as the only input. Our work therefore provides insight for quantitatively understanding recovery process and its implications in infrastructure protection in various complex systems. PMID:27456202

  11. Attack Robustness and Centrality of Complex Networks

    PubMed Central

    Iyer, Swami; Killingback, Timothy; Sundaram, Bala; Wang, Zhen

    2013-01-01

    Many complex systems can be described by networks, in which the constituent components are represented by vertices and the connections between the components are represented by edges between the corresponding vertices. A fundamental issue concerning complex networked systems is the robustness of the overall system to the failure of its constituent parts. Since the degree to which a networked system continues to function, as its component parts are degraded, typically depends on the integrity of the underlying network, the question of system robustness can be addressed by analyzing how the network structure changes as vertices are removed. Previous work has considered how the structure of complex networks change as vertices are removed uniformly at random, in decreasing order of their degree, or in decreasing order of their betweenness centrality. Here we extend these studies by investigating the effect on network structure of targeting vertices for removal based on a wider range of non-local measures of potential importance than simply degree or betweenness. We consider the effect of such targeted vertex removal on model networks with different degree distributions, clustering coefficients and assortativity coefficients, and for a variety of empirical networks. PMID:23565156

  12. An Adaptive Complex Network Model for Brain Functional Networks

    PubMed Central

    Gomez Portillo, Ignacio J.; Gleiser, Pablo M.

    2009-01-01

    Brain functional networks are graph representations of activity in the brain, where the vertices represent anatomical regions and the edges their functional connectivity. These networks present a robust small world topological structure, characterized by highly integrated modules connected sparsely by long range links. Recent studies showed that other topological properties such as the degree distribution and the presence (or absence) of a hierarchical structure are not robust, and show different intriguing behaviors. In order to understand the basic ingredients necessary for the emergence of these complex network structures we present an adaptive complex network model for human brain functional networks. The microscopic units of the model are dynamical nodes that represent active regions of the brain, whose interaction gives rise to complex network structures. The links between the nodes are chosen following an adaptive algorithm that establishes connections between dynamical elements with similar internal states. We show that the model is able to describe topological characteristics of human brain networks obtained from functional magnetic resonance imaging studies. In particular, when the dynamical rules of the model allow for integrated processing over the entire network scale-free non-hierarchical networks with well defined communities emerge. On the other hand, when the dynamical rules restrict the information to a local neighborhood, communities cluster together into larger ones, giving rise to a hierarchical structure, with a truncated power law degree distribution. PMID:19738902

  13. Teachers, Networks and Social Capital

    ERIC Educational Resources Information Center

    Healey, Kaleen

    2013-01-01

    A growing body of research suggests that school leaders and policymakers should attend to the social conditions within schools that promote instructional improvement and student achievement gains. This dissertation uses theoretical and empirical work on social capital to frame three aspects of the relationships among teachers. The three studies…

  14. Community Structure in Online Collegiate Social Networks

    NASA Astrophysics Data System (ADS)

    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.

  15. Onset of traffic congestion in complex networks.

    PubMed

    Zhao, Liang; Lai, Ying-Cheng; Park, Kwangho; Ye, Nong

    2005-02-01

    Free traffic flow on a complex network is key to its normal and efficient functioning. Recent works indicate that many realistic networks possess connecting topologies with a scale-free feature: the probability distribution of the number of links at nodes, or the degree distribution, contains a power-law component. A natural question is then how the topology influences the dynamics of traffic flow on a complex network. Here we present two models to address this question, taking into account the network topology, the information-generating rate, and the information-processing capacity of individual nodes. For each model, we study four kinds of networks: scale-free, random, and regular networks and Cayley trees. In the first model, the capacity of packet delivery of each node is proportional to its number of links, while in the second model, it is proportional to the number of shortest paths passing through the node. We find, in both models, that there is a critical rate of information generation, below which the network traffic is free but above which traffic congestion occurs. Theoretical estimates are given for the critical point. For the first model, scale-free networks and random networks are found to be more tolerant to congestion. For the second model, the congestion condition is independent of network size and topology, suggesting that this model may be practically useful for designing communication protocols.

  16. Empirical Models of Social Learning in a Large, Evolving Network

    PubMed Central

    Bener, Ayşe Başar; Çağlayan, Bora; Henry, Adam Douglas; Prałat, Paweł

    2016-01-01

    This paper advances theories of social learning through an empirical examination of how social networks change over time. Social networks are important for learning because they constrain individuals’ access to information about the behaviors and cognitions of other people. Using data on a large social network of mobile device users over a one-month time period, we test three hypotheses: 1) attraction homophily causes individuals to form ties on the basis of attribute similarity, 2) aversion homophily causes individuals to delete existing ties on the basis of attribute dissimilarity, and 3) social influence causes individuals to adopt the attributes of others they share direct ties with. Statistical models offer varied degrees of support for all three hypotheses and show that these mechanisms are more complex than assumed in prior work. Although homophily is normally thought of as a process of attraction, people also avoid relationships with others who are different. These mechanisms have distinct effects on network structure. While social influence does help explain behavior, people tend to follow global trends more than they follow their friends. PMID:27701430

  17. Controlling extreme events on complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-08-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network ``mobile'' can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed.

  18. Symmetries, Cluster Synchronization, and Isolated Desynchronization in Complex Networks

    NASA Astrophysics Data System (ADS)

    Pecora, Louis

    2015-03-01

    Many networks are observed to produce patterns of synchronized clusters, but it has been difficult to predict these clusters in general or understand the conditions for their formation. We show the intimate connection between network symmetry and cluster synchronization. We apply computational group theory to reveal the clusters and determine their stability. In complex networks the symmetries can number in the millions, billions, and more. The connection between symmetry and cluster synchronization is experimentally explored using an electro-optic network. We observe and explain a surprising and common phenomenon (isolated desynchronization) in which some clusters lose synchrony while leaving others connected to them synchronized. We show the isolated desynchronization is intimately related to the decomposition of the group of symmetries into subgroups. The results could guide the design of new power grid systems or lead to new understanding of the dynamical behavior of networks ranging from neural to social.

  19. Input graph: the hidden geometry in controlling complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Xizhe; Lv, Tianyang; Pu, Yuanyuan

    2016-11-01

    The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes.

  20. Input graph: the hidden geometry in controlling complex networks

    PubMed Central

    Zhang, Xizhe; Lv, Tianyang; Pu, Yuanyuan

    2016-01-01

    The ability to control a complex network towards a desired behavior relies on our understanding of the complex nature of these social and technological networks. The existence of numerous control schemes in a network promotes us to wonder: what is the underlying relationship of all possible input nodes? Here we introduce input graph, a simple geometry that reveals the complex relationship between all control schemes and input nodes. We prove that the node adjacent to an input node in the input graph will appear in another control scheme, and the connected nodes in input graph have the same type in control, which they are either all possible input nodes or not. Furthermore, we find that the giant components emerge in the input graphs of many real networks, which provides a clear topological explanation of bifurcation phenomenon emerging in dense networks and promotes us to design an efficient method to alter the node type in control. The findings provide an insight into control principles of complex networks and offer a general mechanism to design a suitable control scheme for different purposes. PMID:27901102

  1. Travel and tourism: Into a complex network

    NASA Astrophysics Data System (ADS)

    Miguéns, J. I. L.; Mendes, J. F. F.

    2008-05-01

    It is discussed how the worldwide tourist arrivals, about 10% of the world’s domestic product, form a largely heterogeneous and directed complex network. Remarkably the random network of connectivity is converted into a scale-free network of intensities. The importance of weights on network connections is brought into discussion. It is also shown how strategic positioning particularly benefits from market diversity and that interactions among countries prevail on a technological and economic pattern, questioning the backbone of driving forces in traveling.

  2. Quantum Navigation and Ranking in Complex Networks

    PubMed Central

    Sánchez-Burillo, Eduardo; Duch, Jordi; Gómez-Gardeñes, Jesús; Zueco, David

    2012-01-01

    Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems. PMID:22930671

  3. Quantum Navigation and Ranking in Complex Networks

    NASA Astrophysics Data System (ADS)

    Sánchez-Burillo, Eduardo; Duch, Jordi; Gómez-Gardeñes, Jesús; Zueco, David

    2012-08-01

    Complex networks are formal frameworks capturing the interdependencies between the elements of large systems and databases. This formalism allows to use network navigation methods to rank the importance that each constituent has on the global organization of the system. A key example is Pagerank navigation which is at the core of the most used search engine of the World Wide Web. Inspired in this classical algorithm, we define a quantum navigation method providing a unique ranking of the elements of a network. We analyze the convergence of quantum navigation to the stationary rank of networks and show that quantumness decreases the number of navigation steps before convergence. In addition, we show that quantum navigation allows to solve degeneracies found in classical ranks. By implementing the quantum algorithm in real networks, we confirm these improvements and show that quantum coherence unveils new hierarchical features about the global organization of complex systems.

  4. Community core evolution in mobile social networks.

    PubMed

    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.

  5. Dynamics of deceptive interactions in social networks.

    PubMed

    Barrio, Rafael A; Govezensky, Tzipe; Dunbar, Robin; Iñiguez, Gerardo; Kaski, Kimmo

    2015-11-06

    In this paper, we examine the role of lies in human social relations by implementing some salient characteristics of deceptive interactions into an opinion formation model, so as to describe the dynamical behaviour of a social network more realistically. In this model, we take into account such basic properties of social networks as the dynamics of the intensity of interactions, the influence of public opinion and the fact that in every human interaction it might be convenient to deceive or withhold information depending on the instantaneous situation of each individual in the network. We find that lies shape the topology of social networks, especially the formation of tightly linked, small communities with loose connections between them. We also find that agents with a larger proportion of deceptive interactions are the ones that connect communities of different opinion, and, in this sense, they have substantial centrality in the network. We then discuss the consequences of these results for the social behaviour of humans and predict the changes that could arise due to a varying tolerance for lies in society.

  6. Understanding Social Contagion in Adoption Processes Using Dynamic Social Networks.

    PubMed

    Herrera, Mauricio; Armelini, Guillermo; Salvaj, Erica

    2015-01-01

    There are many studies in the marketing and diffusion literature of the conditions in which social contagion affects adoption processes. Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion period. Rooted in physics and epidemiology theories, this study proposes a Susceptible Infectious Susceptible (SIS) model to assess the role of social contagion in adoption processes, which takes changes in social dynamics over time into account. To study the adoption over a span of ten years, the authors used detailed data sets from a community of consumers and determined the importance of social contagion, as well as how the interplay of social and non-social influences from outside the community drives adoption processes. Although social contagion matters for diffusion, it is less relevant in shaping adoption when the study also includes social dynamics among members of the community. This finding is relevant for managers and entrepreneurs who trust in word-of-mouth marketing campaigns whose effect may be overestimated if marketers fail to acknowledge variations in social interactions.

  7. Understanding Social Contagion in Adoption Processes Using Dynamic Social Networks

    PubMed Central

    2015-01-01

    There are many studies in the marketing and diffusion literature of the conditions in which social contagion affects adoption processes. Yet most of these studies assume that social interactions do not change over time, even though actors in social networks exhibit different likelihoods of being influenced across the diffusion period. Rooted in physics and epidemiology theories, this study proposes a Susceptible Infectious Susceptible (SIS) model to assess the role of social contagion in adoption processes, which takes changes in social dynamics over time into account. To study the adoption over a span of ten years, the authors used detailed data sets from a community of consumers and determined the importance of social contagion, as well as how the interplay of social and non-social influences from outside the community drives adoption processes. Although social contagion matters for diffusion, it is less relevant in shaping adoption when the study also includes social dynamics among members of the community. This finding is relevant for managers and entrepreneurs who trust in word-of-mouth marketing campaigns whose effect may be overestimated if marketers fail to acknowledge variations in social interactions. PMID:26505473

  8. Predicting and Controlling Complex Networks

    DTIC Science & Technology

    2015-06-22

    networks and control . . . . . . . . . . . . . . . . . . . 7 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically...Ni, Y.-C. Lai, and C. Grebogi, “Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games,” Physical Review E 83...of Physics B 76, 179-183 (2010). 3.4 Pattern formation, synchronization and outbreak of biodiversity in cyclically competing games Biodiversity is

  9. Conformity biased transmission in social networks.

    PubMed

    Whalen, Andrew; Laland, Kevin

    2015-09-07

    In this paper we explore how the structure of a population can differentially influence the spread of novel behaviors, depending on the learning strategy of each individual. We use a series of simulations to analyze how frequency dependent learning rules might affect how easily novel behaviors can spread through a population on four artificial social networks, and three real social networks. We measured the likelihood that a novel behavior could spread through the population, and the likelihood that there were multiple behavioral variants in the population, a measure of cultural diversity. Surprisingly, we find few differences between networks on either measure. However, we do find that where a behavior originated on a network can have a substantial impact on the likelihood that it spreads, and that this location effect depends on the learning strategy of an individual. These results suggest that for first-order analysis of how behaviors spread through a population, social network structure can be ignored, but that the social network structure may be useful for more fine-tuned analyses and predictions.

  10. Social contagions on interdependent lattice networks

    NASA Astrophysics Data System (ADS)

    Shu, Panpan; Gao, Lei; Zhao, Pengcheng; Wang, Wei; Stanley, H. Eugene

    2017-03-01

    Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes.

  11. Social contagions on interdependent lattice networks.

    PubMed

    Shu, Panpan; Gao, Lei; Zhao, Pengcheng; Wang, Wei; Stanley, H Eugene

    2017-03-16

    Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes.

  12. Persistent ISR: the social network analysis connection

    NASA Astrophysics Data System (ADS)

    Bowman, Elizabeth K.

    2012-06-01

    Persistent surveillance provides decision makers with unprecedented access to multisource data collected from humans and sensor assets around the globe, yet these data exist in the physical world and provide few overt clues to meaning behind actions. In this paper we explore the recent growth in online social networking and ask the questions: 1) can these sites provide value-added information to compliment physical sensing and 2) what are the mechanisms by which these data could inform situational awareness and decision making? In seeking these answers we consider the range of options provided by Social Network Analysis (SNA), and focus especially on the dynamic nature of these networks. In our discussion we focus on the wave of reform experienced by the North African nations in early 2011 known as the Arab Spring. Demonstrators made widespread use of social networking applications to coordinate, document, and publish material to aid their cause. Unlike members of covert social networks who hide their activity and associations, these demonstrators openly posted multimedia information to coordinate activity and stimulate global support. In this paper we provide a review of SNA approaches and consider how one might track network adaptations by capturing temporal and conceptual trends. We identify opportunities and challenges for merging SNA with physical sensor output, and conclude by addressing future challenges in the persistent ISR domain with respect to SNA.

  13. Social contagions on interdependent lattice networks

    PubMed Central

    Shu, Panpan; Gao, Lei; Zhao, Pengcheng; Wang, Wei; Stanley, H. Eugene

    2017-01-01

    Although an increasing amount of research is being done on the dynamical processes on interdependent spatial networks, knowledge of how interdependent spatial networks influence the dynamics of social contagion in them is sparse. Here we present a novel non-Markovian social contagion model on interdependent spatial networks composed of two identical two-dimensional lattices. We compare the dynamics of social contagion on networks with different fractions of dependency links and find that the density of final recovered nodes increases as the number of dependency links is increased. We use a finite-size analysis method to identify the type of phase transition in the giant connected components (GCC) of the final adopted nodes and find that as we increase the fraction of dependency links, the phase transition switches from second-order to first-order. In strong interdependent spatial networks with abundant dependency links, increasing the fraction of initial adopted nodes can induce the switch from a first-order to second-order phase transition associated with social contagion dynamics. In networks with a small number of dependency links, the phase transition remains second-order. In addition, both the second-order and first-order phase transition points can be decreased by increasing the fraction of dependency links or the number of initially-adopted nodes. PMID:28300198

  14. Social networking for adolescents with severe haemophilia.

    PubMed

    Khair, K; Holland, M; Carrington, S

    2012-05-01

    Access to modern treatments allows adolescents with haemophilia to manage their haemophilia at home, with improved treatment outcomes and quality of life, but has reduced peer support and the potential for experiential learning from older peers. Social networking, aided by modern communication technologies, may offer health benefits through peer support. We sought to assess whether or not disease-specific social networking could benefit adolescents with severe haemophilia. A total of 150 adolescents (aged 10-18) with severe haemophilia A or B from 11 UK treatment centres or those who had attended focus groups to explore the potential for a social network designed specifically for their use were surveyed. Teenage boys with severe haemophilia in the UK who responded to an online and paper questionnaire (n = 47; 31% response rate) rarely knew of or socialized with others with haemophilia outside their families. Two-thirds of respondents said they would like to meet others. For 70% of boys, parents were the major source of information about haemophilia, yet more than half said they often had trouble finding answers to their questions. These boys frequently used online social networks to chat with friends. Adolescents with severe haemophilia frequently have limited contact with others and many wish to have greater contact. They may benefit from peer support and experiential learning gained through online social networking. The SixVibe restricted access social network is to be launched in 2011. It includes features designed to promote and facilitate the development of peer-to peer disease management skills for adolescents with severe haemophilia.

  15. Shock waves on complex networks

    PubMed Central

    Mones, Enys; Araújo, Nuno A. M.; Vicsek, Tamás; Herrmann, Hans J.

    2014-01-01

    Power grids, road maps, and river streams are examples of infrastructural networks which are highly vulnerable to external perturbations. An abrupt local change of load (voltage, traffic density, or water level) might propagate in a cascading way and affect a significant fraction of the network. Almost discontinuous perturbations can be modeled by shock waves which can eventually interfere constructively and endanger the normal functionality of the infrastructure. We study their dynamics by solving the Burgers equation under random perturbations on several real and artificial directed graphs. Even for graphs with a narrow distribution of node properties (e.g., degree or betweenness), a steady state is reached exhibiting a heterogeneous load distribution, having a difference of one order of magnitude between the highest and average loads. Unexpectedly we find for the European power grid and for finite Watts-Strogatz networks a broad pronounced bimodal distribution for the loads. To identify the most vulnerable nodes, we introduce the concept of node-basin size, a purely topological property which we show to be strongly correlated to the average load of a node. PMID:24821422

  16. Social capital, friendship networks, and youth unemployment.

    PubMed

    Hällsten, Martin; Edling, Christofer; Rydgren, Jens

    2017-01-01

    Youth unemployment is a contemporary social problem in many societies. Youths often have limited access to information about jobs and limited social influence, yet little is known about the relationship between social capital and unemployment risk among youth. We study the effect of social capital on unemployment risk in a sample of 19 year olds of Swedish, Iranian, and Yugoslavian origin living in Sweden (N = 1590). We distinguish between two dimensions of social capital: occupational contact networks and friendship networks. First, ego's unemployment is found to be strongly associated with friends' unemployment among individuals of Yugoslavian origins and individuals of Swedish origin, but not Iranian origin. Second, occupational contact networks reduce unemployment risks for all groups, but especially so for Iranians. The effect sizes of the two dimensions are similar and substantial: going from low to high values on these measures is associated with a difference of some 60-70 percent relative difference in unemployment risk. The findings are robust to a number of different model specifications, including a rich set of social origin controls, personality traits, educational performance, friends' characteristics, and friendship network characteristics, as well as controls for geographical employment patterns. A sensitivity simulation shows that homogeneity bias need to be very strong to explain away the effect.

  17. Phase transitions in complex network dynamics

    NASA Astrophysics Data System (ADS)

    Squires, Shane

    Two phase transitions in complex networks are analyzed. The first of these is a percolation transition, in which the network develops a macroscopic connected component as edges are added to it. Recent work has shown that if edges are added "competitively" to an undirected network, the onset of percolation is abrupt or "explosive." A new variant of explosive percolation is introduced here for directed networks, whose critical behavior is explored using numerical simulations and finite-size scaling theory. This process is also characterized by a very rapid percolation transition, but it is not as sudden as in undirected networks. The second phase transition considered here is the emergence of instability in Boolean networks, a class of dynamical systems that are widely used to model gene regulation. The dynamics, which are determined by the network topology and a set of update rules, may be either stable or unstable, meaning that small perturbations to the state of the network either die out or grow to become macroscopic. Here, this transition is analytically mapped onto a well-studied percolation problem, which can be used to predict the average steady-state distance between perturbed and unperturbed trajectories. This map applies to specific Boolean networks with few restrictions on network topology, but can only be applied to two commonly used types of update rules. Finally, a method is introduced for predicting the stability of Boolean networks with a much broader range of update rules. The network is assumed to have a given complex topology, subject only to a locally tree-like condition, and the update rules may be correlated with topological features of the network. While past work has addressed the separate effects of topology and update rules on stability, the present results are the first widely applicable approach to studying how these effects interact. Numerical simulations agree with the theory and show that such correlations between topology and update

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

    PubMed Central

    Belza, Basia; Leith, Katherine; Allen, Peg; Coe, Norma B.; Anderson, Lynda A.

    2015-01-01

    Introduction Addressing chronic disease burden requires the creation of collaborative networks to promote systemic changes and engage stakeholders. Although many such networks exist, they are rarely assessed with tools that account for their complexity. This study examined the structure of mentorship and collaboration relationships among members of the Healthy Aging Research Network (HAN) using social network analysis (SNA). Methods We invited 97 HAN members and partners to complete an online social network survey that included closed-ended questions about HAN-specific mentorship and collaboration during the previous 12 months. Collaboration was measured by examining the activity of the network on 6 types of products: published articles, in-progress manuscripts, grant applications, tools, research projects, and presentations. We computed network-level measures such as density, number of components, and centralization to assess the cohesiveness of the network. Results Sixty-three respondents completed the survey (response rate, 65%). Responses, which included information about collaboration with nonrespondents, suggested that 74% of HAN members were connected through mentorship ties and that all 97 members were connected through at least one form of collaboration. Mentorship and collaboration ties were present both within and across boundaries of HAN member organizations. Conclusion SNA of public health collaborative networks provides understanding about the structure of relationships that are formed as a result of participation in network activities. This approach may offer members and funders a way to assess the impact of such networks that goes beyond simply measuring products and participation at the individual level. PMID:26292061

  19. Measuring the significance of community structure in complex networks

    NASA Astrophysics Data System (ADS)

    Hu, Yanqing; Nie, Yuchao; Yang, Hua; Cheng, Jie; Fan, Ying; di, Zengru

    2010-12-01

    Many complex systems can be represented as networks, and separating a network into communities could simplify functional analysis considerably. Many approaches have recently been proposed to detect communities, but a method to determine whether the detected communities are significant is still lacking. In this paper, an index to evaluate the significance of communities in networks is proposed based on perturbation of the network. In contrast to previous approaches, the network is disturbed gradually, and the index is defined by integrating all of the similarities between the community structures before and after perturbation. Moreover, by taking the null model into account, the index eliminates scale effects. Thus, it can evaluate and compare the significance of communities in different networks. The method has been tested in many artificial and real-world networks. The results show that the index is in fact independent of the size of the network and the number of communities. With this approach, clear communities are found to always exist in social networks, but significant communities cannot be found in protein interactions and metabolic networks.

  20. Self-similarity and scaling theory of complex networks

    NASA Astrophysics Data System (ADS)

    Song, Chaoming

    Scale-free networks have been studied extensively due to their relevance to many real systems as diverse as the World Wide Web (WWW), the Internet, biological and social networks. We present a novel approach to the analysis of scale-free networks, revealing that their structure is self-similar. This result is achieved by the application of a renormalization procedure which coarse-grains the system into boxes containing nodes within a given "size". Concurrently, we identify a power-law relation between the number of boxes needed to cover the network and the size of the box defining a self-similar exponent, which classifies fractal and non-fractal networks. By using the concept of renormalization as a mechanism for the growth of fractal and non-fractal modular networks, we show that the key principle that gives rise to the fractal architecture of networks is a strong effective "repulsion" between the most connected nodes (hubs) on all length scales, rendering them very dispersed. We show that a robust network comprised of functional modules, such as a cellular network, necessitates a fractal topology, suggestive of a evolutionary drive for their existence. These fundamental properties help to understand the emergence of the scale-free property in complex networks.

  1. Undermining and Strengthening Social Networks through Network Modification

    NASA Astrophysics Data System (ADS)

    Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

    2016-10-01

    Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention.

  2. Undermining and Strengthening Social Networks through Network Modification

    PubMed Central

    Mellon, Jonathan; Yoder, Jordan; Evans, Daniel

    2016-01-01

    Social networks have well documented effects at the individual and aggregate level. Consequently it is often useful to understand how an attempt to influence a network will change its structure and consequently achieve other goals. We develop a framework for network modification that allows for arbitrary objective functions, types of modification (e.g. edge weight addition, edge weight removal, node removal, and covariate value change), and recovery mechanisms (i.e. how a network responds to interventions). The framework outlined in this paper helps both to situate the existing work on network interventions but also opens up many new possibilities for intervening in networks. In particular use two case studies to highlight the potential impact of empirically calibrating the objective function and network recovery mechanisms as well as showing how interventions beyond node removal can be optimised. First, we simulate an optimal removal of nodes from the Noordin terrorist network in order to reduce the expected number of attacks (based on empirically predicting the terrorist collaboration network from multiple types of network ties). Second, we simulate optimally strengthening ties within entrepreneurial ecosystems in six developing countries. In both cases we estimate ERGM models to simulate how a network will endogenously evolve after intervention. PMID:27703198

  3. Origin of Peer Influence in Social Networks

    NASA Astrophysics Data System (ADS)

    Pinheiro, Flávio L.; Santos, Marta D.; Santos, Francisco C.; Pacheco, Jorge M.

    2014-03-01

    Social networks pervade our everyday lives: we interact, influence, and are influenced by our friends and acquaintances. With the advent of the World Wide Web, large amounts of data on social networks have become available, allowing the quantitative analysis of the distribution of information on them, including behavioral traits and fads. Recent studies of correlations among members of a social network, who exhibit the same trait, have shown that individuals influence not only their direct contacts but also friends' friends, up to a network distance extending beyond their closest peers. Here, we show how such patterns of correlations between peers emerge in networked populations. We use standard models (yet reflecting intrinsically different mechanisms) of information spreading to argue that empirically observed patterns of correlation among peers emerge naturally from a wide range of dynamics, being essentially independent of the type of information, on how it spreads, and even on the class of underlying network that interconnects individuals. Finally, we show that the sparser and clustered the network, the more far reaching the influence of each individual will be.

  4. Online and Offline Social Networks: Use of Social Networking Sites by Emerging Adults

    ERIC Educational Resources Information Center

    Subrahmanyam, Kaveri; Reich, Stephanie M.; Waechter, Natalia; Espinoza, Guadalupe

    2008-01-01

    Social networking sites (e.g., MySpace and Facebook) are popular online communication forms among adolescents and emerging adults. Yet little is known about young people's activities on these sites and how their networks of "friends" relate to their other online (e.g., instant messaging) and offline networks. In this study, college students…

  5. Distributed multiple path routing in complex networks

    NASA Astrophysics Data System (ADS)

    Chen, Guang; Wang, San-Xiu; Wu, Ling-Wei; Mei, Pan; Yang, Xu-Hua; Wen, Guang-Hui

    2016-12-01

    Routing in complex transmission networks is an important problem that has garnered extensive research interest in the recent years. In this paper, we propose a novel routing strategy called the distributed multiple path (DMP) routing strategy. For each of the O-D node pairs in a given network, the DMP routing strategy computes and stores multiple short-length paths that overlap less with each other in advance. And during the transmission stage, it rapidly selects an actual routing path which provides low transmission cost from the pre-computed paths for each transmission task, according to the real-time network transmission status information. Computer simulation results obtained for the lattice, ER random, and scale-free networks indicate that the strategy can significantly improve the anti-congestion ability of transmission networks, as well as provide favorable routing robustness against partial network failures.

  6. Random matrix analysis of complex networks.

    PubMed

    Jalan, Sarika; Bandyopadhyay, Jayendra N

    2007-10-01

    We study complex networks under random matrix theory (RMT) framework. Using nearest-neighbor and next-nearest-neighbor spacing distributions we analyze the eigenvalues of the adjacency matrix of various model networks, namely, random, scale-free, and small-world networks. These distributions follow the Gaussian orthogonal ensemble statistic of RMT. To probe long-range correlations in the eigenvalues we study spectral rigidity via the Delta_{3} statistic of RMT as well. It follows RMT prediction of linear behavior in semilogarithmic scale with the slope being approximately 1pi;{2} . Random and scale-free networks follow RMT prediction for very large scale. A small-world network follows it for sufficiently large scale, but much less than the random and scale-free networks.

  7. Dynamic information routing in complex networks

    PubMed Central

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-01-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function. PMID:27067257

  8. Dynamic information routing in complex networks

    NASA Astrophysics Data System (ADS)

    Kirst, Christoph; Timme, Marc; Battaglia, Demian

    2016-04-01

    Flexible information routing fundamentally underlies the function of many biological and artificial networks. Yet, how such systems may specifically communicate and dynamically route information is not well understood. Here we identify a generic mechanism to route information on top of collective dynamical reference states in complex networks. Switching between collective dynamics induces flexible reorganization of information sharing and routing patterns, as quantified by delayed mutual information and transfer entropy measures between activities of a network's units. We demonstrate the power of this mechanism specifically for oscillatory dynamics and analyse how individual unit properties, the network topology and external inputs co-act to systematically organize information routing. For multi-scale, modular architectures, we resolve routing patterns at all levels. Interestingly, local interventions within one sub-network may remotely determine nonlocal network-wide communication. These results help understanding and designing information routing patterns across systems where collective dynamics co-occurs with a communication function.

  9. The Analysis of Duocentric Social Networks: A Primer.

    PubMed

    Kennedy, David P; Jackson, Grace L; Green, Harold D; Bradbury, Thomas N; Karney, Benjamin R

    2015-02-01

    Marriages and other intimate partnerships are facilitated or constrained by the social networks within which they are embedded. To date, methods used to assess the social networks of couples have been limited to global ratings of social network characteristics or network data collected from each partner separately. In the current article, the authors offer new tools for expanding on the existing literature by describing methods of collecting and analyzing duocentric social networks, that is, the combined social networks of couples. They provide an overview of the key considerations for measuring duocentric networks, such as how and why to combine separate network interviews with partners into one shared duocentric network, the number of network members to assess, and the implications of different network operationalizations. They illustrate these considerations with analyses of social network data collected from 57 low-income married couples, presenting visualizations and quantitative measures of network composition and structure.

  10. Online Social Networking and Mental Health

    PubMed Central

    2014-01-01

    Abstract During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction. PMID:25192305

  11. Online social networking and mental health.

    PubMed

    Pantic, Igor

    2014-10-01

    During the past decade, online social networking has caused profound changes in the way people communicate and interact. It is unclear, however, whether some of these changes may affect certain normal aspects of human behavior and cause psychiatric disorders. Several studies have indicated that the prolonged use of social networking sites (SNS), such as Facebook, may be related to signs and symptoms of depression. In addition, some authors have indicated that certain SNS activities might be associated with low self-esteem, especially in children and adolescents. Other studies have presented opposite results in terms of positive impact of social networking on self-esteem. The relationship between SNS use and mental problems to this day remains controversial, and research on this issue is faced with numerous challenges. This concise review focuses on the recent findings regarding the suggested connection between SNS and mental health issues such as depressive symptoms, changes in self-esteem, and Internet addiction.

  12. Virality Prediction and Community Structure in Social Networks

    NASA Astrophysics Data System (ADS)

    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.

  13. Virality prediction and community structure in social networks.

    PubMed

    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.

  14. Build your own social network laboratory with Social Lab: a tool for research in social media.

    PubMed

    Garaizar, Pablo; Reips, Ulf-Dietrich

    2014-06-01

    Social networking has surpassed e-mail and instant messaging as the dominant form of online communication (Meeker, Devitt, & Wu, 2010). Currently, all large social networks are proprietary, making it difficult to impossible for researchers to make changes to such networks for the purpose of study design and access to user-generated data from the networks. To address this issue, the authors have developed and present Social Lab, an Internet-based free and open-source social network software system available from http://www.sociallab.es . Having full availability of navigation and communication data in Social Lab allows researchers to investigate behavior in social media on an individual and group level. Automated artificial users ("bots") are available to the researcher to simulate and stimulate social networking situations. These bots respond dynamically to situations as they unfold. The bots can easily be configured with scripts and can be used to experimentally manipulate social networking situations in Social Lab. Examples for setting up, configuring, and using Social Lab as a tool for research in social media are provided.

  15. Network model of bilateral power markets based on complex networks

    NASA Astrophysics Data System (ADS)

    Wu, Yang; Liu, Junyong; Li, Furong; Yan, Zhanxin; Zhang, Li

    2014-06-01

    The bilateral power transaction (BPT) mode becomes a typical market organization with the restructuring of electric power industry, the proper model which could capture its characteristics is in urgent need. However, the model is lacking because of this market organization's complexity. As a promising approach to modeling complex systems, complex networks could provide a sound theoretical framework for developing proper simulation model. In this paper, a complex network model of the BPT market is proposed. In this model, price advantage mechanism is a precondition. Unlike other general commodity transactions, both of the financial layer and the physical layer are considered in the model. Through simulation analysis, the feasibility and validity of the model are verified. At same time, some typical statistical features of BPT network are identified. Namely, the degree distribution follows the power law, the clustering coefficient is low and the average path length is a bit long. Moreover, the topological stability of the BPT network is tested. The results show that the network displays a topological robustness to random market member's failures while it is fragile against deliberate attacks, and the network could resist cascading failure to some extent. These features are helpful for making decisions and risk management in BPT markets.

  16. Multiobjective blockmodeling for social network analysis.

    PubMed

    Brusco, Michael; Doreian, Patrick; Steinley, Douglas; Satornino, Cinthia B

    2013-07-01

    To date, most methods for direct blockmodeling of social network data have focused on the optimization of a single objective function. However, there are a variety of social network applications where it is advantageous to consider two or more objectives simultaneously. These applications can broadly be placed into two categories: (1) simultaneous optimization of multiple criteria for fitting a blockmodel based on a single network matrix and (2) simultaneous optimization of multiple criteria for fitting a blockmodel based on two or more network matrices, where the matrices being fit can take the form of multiple indicators for an underlying relationship, or multiple matrices for a set of objects measured at two or more different points in time. A multiobjective tabu search procedure is proposed for estimating the set of Pareto efficient blockmodels. This procedure is used in three examples that demonstrate possible applications of the multiobjective blockmodeling paradigm.

  17. The "Majority Illusion" in Social Networks

    PubMed Central

    Lerman, Kristina; Yan, Xiaoran; Wu, Xin-Zeng

    2016-01-01

    Individual’s decisions, from what product to buy to whether to engage in risky behavior, often depend on the choices, behaviors, or states of other people. People, however, rarely have global knowledge of the states of others, but must estimate them from the local observations of their social contacts. Network structure can significantly distort individual’s local observations. Under some conditions, a state that is globally rare in a network may be dramatically over-represented in the local neighborhoods of many individuals. This effect, which we call the “majority illusion,” leads individuals to systematically overestimate the prevalence of that state, which may accelerate the spread of social contagions. We develop a statistical model that quantifies this effect and validate it with measurements in synthetic and real-world networks. We show that the illusion is exacerbated in networks with a heterogeneous degree distribution and disassortative structure. PMID:26886112

  18. Gossip spread in social network Models

    NASA Astrophysics Data System (ADS)

    Johansson, Tobias

    2017-04-01

    Gossip almost inevitably arises in real social networks. In this article we investigate the relationship between the number of friends of a person and limits on how far gossip about that person can spread in the network. How far gossip travels in a network depends on two sets of factors: (a) factors determining gossip transmission from one person to the next and (b) factors determining network topology. For a simple model where gossip is spread among people who know the victim it is known that a standard scale-free network model produces a non-monotonic relationship between number of friends and expected relative spread of gossip, a pattern that is also observed in real networks (Lind et al., 2007). Here, we study gossip spread in two social network models (Toivonen et al., 2006; Vázquez, 2003) by exploring the parameter space of both models and fitting them to a real Facebook data set. Both models can produce the non-monotonic relationship of real networks more accurately than a standard scale-free model while also exhibiting more realistic variability in gossip spread. Of the two models, the one given in Vázquez (2003) best captures both the expected values and variability of gossip spread.

  19. Spreading paths in partially observed social networks

    PubMed Central

    Onnela, Jukka-Pekka; Christakis, Nicholas A.

    2012-01-01

    Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using a static, structurally realistic social network as a platform for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is. PMID:22587148

  20. Social networks and health service utilization.

    PubMed

    Deri, Catherine

    2005-11-01

    While social networks have been examined in the context of many economic choices and outcomes, this study is the first to investigate the effects of social networks on health service utilization decisions. Networks can affect utilization decisions in many ways. They can provide information on institutional details of the health care system, and can reduce the search costs of locating an appropriate health care provider. Networks can even alter the demand for services by affecting the perceived efficacy or desirability of the available services. Using health service utilization decisions to study networks has two main advantages over work that studies other public programs. First, because health care in Canada is universal, there are no questions of eligibility. Second, by studying the different measures of utilization, it is possible to observe how the network effects vary across measures that reflect visits primarily instigated by the patient, to measures that reflect visits instigated by both patients and their physician. Using data from three cycles of the Canadian National Population Health Survey, this work exploits regional and language group variation to identify network effects. Strong and robust evidence of networks effects is found on the decision to utilize services reflecting initial contact with the health care system. As well, this work presents novel evidence that utilization of health services by immigrants increases with the number of doctors that speak their language in their neighborhood.

  1. Social Trust Prediction Using Heterogeneous Networks.

    PubMed

    Huang, Jin; Nie, Feiping; Huang, Heng; Tu, Yi-Cheng; Lei, Yu

    2013-11-01

    Along with increasing popularity of social websites, online users rely more on the trustworthiness information to make decisions, extract and filter information, and tag and build connections with other users. However, such social network data often suffer from severe data sparsity and are not able to provide users with enough information. Therefore, trust prediction has emerged as an important topic in social network research. Traditional approaches are primarily based on exploring trust graph topology itself. However, research in sociology and our life experience suggest that people who are in the same social circle often exhibit similar behaviors and tastes. To take advantage of the ancillary information for trust prediction, the challenge then becomes what to transfer and how to transfer. In this article, we address this problem by aggregating heterogeneous social networks and propose a novel joint social networks mining (JSNM) method. Our new joint learning model explores the user-group-level similarity between correlated graphs and simultaneously learns the individual graph structure; therefore, the shared structures and patterns from multiple social networks can be utilized to enhance the prediction tasks. As a result, we not only improve the trust prediction in the target graph but also facilitate other information retrieval tasks in the auxiliary graphs. To optimize the proposed objective function, we use the alternative technique to break down the objective function into several manageable subproblems. We further introduce the auxiliary function to solve the optimization problems with rigorously proved convergence. The extensive experiments have been conducted on both synthetic and real- world data. All empirical results demonstrate the effectiveness of our method.

  2. Sentiment analysis on smoking in social networks.

    PubMed

    Sofean, Mustafa; Smith, Matthew

    2013-01-01

    Online social networks play a vital role in daily life to share the opinions or behaviors on different topics. The data of social networks can be used to understand health-related behaviors. In this work, we used Twitter status updates to survey of smoking behaviors among the users. We introduce approach to classify the sentiment of smoke-related tweets into positive and negative tweets. The classifier is based on the Support Vector Machines (SVMs) and can achieve high accuracy up to 86%.

  3. Social Network Sites as Educational Factors

    PubMed Central

    Ebrahimpour, Alireza; Rajabali, Farnaz; Yazdanfar, Fatemeh; Azarbad, Reza; Nodeh, Majid Rezaei; Siamian, Hasan; Vahedi, Mohammad

    2016-01-01

    Background: in this present era, the technology development has established certain type of communication. Nowadays education as the fundamental principle in transferring cognition to the learners has found various methods. Recently the concept that social networks could be effective tool in easing the achievement to the educational goals has been under attention. Therefore, this investigation is trying to find out whether, the social networks could play role on the process of education among students? Materials and Methods: This cross sectional descriptive study was performed on 1000 students from 7 medical universities in 2015. The data collection tool was questionnaire that was approved Cronbach’s alpha: was 0.85. Meanwhile its validity was confirmed too. The obtained data were analyzed by the descriptive statistic, ANOVA, Turkey and used X2 SPSS-19. Results: In this investigation, 940 subjects were under study. 85% used daily the social network. The highest usage was attributed to the Telegram. 52% preferred image suitable for transferring of information. Even though, 73% believed that these networks have significant effects on coordinating of students with in university charges. Conclusion: Considering the findings of the present study, it is proposed that the universities integrate the social networks in the education programs and recognize it as the awareness factor, therefore benefit it in the educational affairs. PMID:27147807

  4. Applying DNA computation to intractable problems in social network analysis.

    PubMed

    Chen, Rick C S; Yang, Stephen J H

    2010-09-01

    From ancient times to the present day, social networks have played an important role in the formation of various organizations for a range of social behaviors. As such, social networks inherently describe the complicated relationships between elements around the world. Based on mathematical graph theory, social network analysis (SNA) has been developed in and applied to various fields such as Web 2.0 for Web applications and product developments in industries, etc. However, some definitions of SNA, such as finding a clique, N-clique, N-clan, N-club and K-plex, are NP-complete problems, which are not easily solved via traditional computer architecture. These challenges have restricted the uses of SNA. This paper provides DNA-computing-based approaches with inherently high information density and massive parallelism. Using these approaches, we aim to solve the three primary problems of social networks: N-clique, N-clan, and N-club. Their accuracy and feasible time complexities discussed in the paper will demonstrate that DNA computing can be used to facilitate the development of SNA.

  5. A natural experiment of social network formation and dynamics.

    PubMed

    Phan, Tuan Q; Airoldi, Edoardo M

    2015-05-26

    Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities.

  6. A natural experiment of social network formation and dynamics

    PubMed Central

    Phan, Tuan Q.; Airoldi, Edoardo M.

    2015-01-01

    Social networks affect many aspects of life, including the spread of diseases, the diffusion of information, the workers' productivity, and consumers' behavior. Little is known, however, about how these networks form and change. Estimating causal effects and mechanisms that drive social network formation and dynamics is challenging because of the complexity of engineering social relations in a controlled environment, endogeneity between network structure and individual characteristics, and the lack of time-resolved data about individuals' behavior. We leverage data from a sample of 1.5 million college students on Facebook, who wrote more than 630 million messages and 590 million posts over 4 years, to design a long-term natural experiment of friendship formation and social dynamics in the aftermath of a natural disaster. The analysis shows that affected individuals are more likely to strengthen interactions, while maintaining the same number of friends as unaffected individuals. Our findings suggest that the formation of social relationships may serve as a coping mechanism to deal with high-stress situations and build resilience in communities. PMID:25964337

  7. Pain tolerance predicts human social network size

    PubMed Central

    Johnson, Katerina V.-A.; Dunbar, Robin I. M.

    2016-01-01

    Personal social network size exhibits considerable variation in the human population and is associated with both physical and mental health status. Much of this inter-individual variation in human sociality remains unexplained from a biological perspective. According to the brain opioid theory of social attachment, binding of the neuropeptide β-endorphin to μ-opioid receptors in the central nervous system (CNS) is a key neurochemical mechanism involved in social bonding, particularly amongst primates. We hypothesise that a positive association exists between activity of the μ-opioid system and the number of social relationships that an individual maintains. Given the powerful analgesic properties of β-endorphin, we tested this hypothesis using pain tolerance as an assay for activation of the endogenous μ-opioid system. We show that a simple measure of pain tolerance correlates with social network size in humans. Our results are in line with previous studies suggesting that μ-opioid receptor signalling has been elaborated beyond its basic function of pain modulation to play an important role in managing our social encounters. The neuroplasticity of the μ-opioid system is of future research interest, especially with respect to psychiatric disorders associated with symptoms of social withdrawal and anhedonia, both of which are strongly modulated by endogenous opioids. PMID:27121297

  8. The Application of Social Network Analysis to Team Sports

    ERIC Educational Resources Information Center

    Lusher, Dean; Robins, Garry; Kremer, Peter

    2010-01-01

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

  9. Social Network Methods for the Educational and Psychological Sciences

    ERIC Educational Resources Information Center

    Sweet, Tracy M.

    2016-01-01

    Social networks are especially applicable in educational and psychological studies involving social interactions. A social network is defined as a specific relationship among a group of individuals. Social networks arise in a variety of situations such as friendships among children, collaboration and advice seeking among teachers, and coauthorship…

  10. Health and the Structure of Adolescent Social Networks

    ERIC Educational Resources Information Center

    Haas, Steven A.; Schaefer, David R.; Kornienko, Olga

    2010-01-01

    Much research has explored the role of social networks in promoting health through the provision of social support. However, little work has examined how social networks themselves may be structured by health. This article investigates the link between individuals' health and the characteristics of their social network positions.We first develop…

  11. The Curriculum Prerequisite Network: Modeling the Curriculum as a Complex System

    ERIC Educational Resources Information Center

    Aldrich, Preston R.

    2015-01-01

    This article advances the prerequisite network as a means to visualize the hidden structure in an academic curriculum. Networks have been used to represent a variety of complex systems ranging from social systems to biochemical pathways and protein interactions. Here, I treat the academic curriculum as a complex system with nodes representing…

  12. Predictive Control of Large Complex Networks

    NASA Astrophysics Data System (ADS)

    Haber, Aleksandar; Motter, Adilson E.

    Networks of coupled dynamical subsystems are increasingly used to represent complex natural and engineered systems. While recent technological developments give us improved means to actively control the dynamics of individual subsystems in various domains, network control remains a challenging problem due to difficulties imposed by intrinsic nonlinearities, control constraints, and the large-scale nature of the systems. In this talk, we will present a model predictive control approach that is effective while accounting for these realistic properties of complex networks. Our method can systematically identify control interventions that steer the trajectory to a desired state, even in the presence of strong nonlinearities and constraints. Numerical tests show that the method is applicable to a variety of networks, ranging from power grids to chemical reaction systems.

  13. Discriminating complex networks through supervised NDR and Bayesian classifier

    NASA Astrophysics Data System (ADS)

    Yan, Ke-Sheng; Rong, Li-Li; Yu, Kai

    2016-12-01

    Discriminating complex networks is a particularly important task for the purpose of the systematic study of networks. In order to discriminate unknown networks exactly, a large set of network measurements are needed to be taken into account for comprehensively considering network properties. However, as we demonstrate in this paper, these measurements are nonlinear correlated with each other in general, resulting in a wide variety of redundant measurements which unintentionally explain the same aspects of network properties. To solve this problem, we adopt supervised nonlinear dimensionality reduction (NDR) to eliminate the nonlinear redundancy and visualize networks in a low-dimensional projection space. Though unsupervised NDR can achieve the same aim, we illustrate that supervised NDR is more appropriate than unsupervised NDR for discrimination task. After that, we perform Bayesian classifier (BC) in the projection space to discriminate the unknown network by considering the projection score vectors as the input of the classifier. We also demonstrate the feasibility and effectivity of this proposed method in six extensive research real networks, ranging from technological to social or biological. Moreover, the effectiveness and advantage of the proposed method is proved by the contrast experiments with the existing method.

  14. Power-Hop: A Pervasive Observation for Real Complex Networks

    PubMed Central

    Papalexakis, Evangelos; Hooi, Bryan; Pelechrinis, Konstantinos; Faloutsos, Christos

    2016-01-01

    Complex networks have been shown to exhibit universal properties, with one of the most consistent patterns being the scale-free degree distribution, but are there regularities obeyed by the r-hop neighborhood in real networks? We answer this question by identifying another power-law pattern that describes the relationship between the fractions of node pairs C(r) within r hops and the hop count r. This scale-free distribution is pervasive and describes a large variety of networks, ranging from social and urban to technological and biological networks. In particular, inspired by the definition of the fractal correlation dimension D2 on a point-set, we consider the hop-count r to be the underlying distance metric between two vertices of the network, and we examine the scaling of C(r) with r. We find that this relationship follows a power-law in real networks within the range 2 ≤ r ≤ d, where d is the effective diameter of the network, that is, the 90-th percentile distance. We term this relationship as power-hop and the corresponding power-law exponent as power-hop exponent h. We provide theoretical justification for this pattern under successful existing network models, while we analyze a large set of real and synthetic network datasets and we show the pervasiveness of the power-hop. PMID:26974560

  15. Efficient Access Control in Multimedia Social Networks

    NASA Astrophysics Data System (ADS)

    Sachan, Amit; Emmanuel, Sabu

    Multimedia social networks (MMSNs) have provided a convenient way to share multimedia contents such as images, videos, blogs, etc. Contents shared by a person can be easily accessed by anybody else over the Internet. However, due to various privacy, security, and legal concerns people often want to selectively share the contents only with their friends, family, colleagues, etc. Access control mechanisms play an important role in this situation. With access control mechanisms one can decide the persons who can access a shared content and who cannot. But continuously growing content uploads and accesses, fine grained access control requirements (e.g. different access control parameters for different parts in a picture), and specific access control requirements for multimedia contents can make the time complexity of access control to be very large. So, it is important to study an efficient access control mechanism suitable for MMSNs. In this chapter we present an efficient bit-vector transform based access control mechanism for MMSNs. The proposed approach is also compatible with other requirements of MMSNs, such as access rights modification, content deletion, etc. Mathematical analysis and experimental results show the effectiveness and efficiency of our proposed approach.

  16. The organization of strong links in complex networks

    NASA Astrophysics Data System (ADS)

    Pajevic, Sinisa; Plenz, Dietmar

    2012-05-01

    Many complex systems reveal a small-world topology, which allows simultaneously local and global efficiency in the interaction between system constituents. Here, we report the results of a comprehensive study that investigates the relation between the clustering properties in such small-world systems and the strength of interactions between its constituents, quantified by the link weight. For brain, gene, social and language networks, we find a local integrative weight organization in which strong links preferentially occur between nodes with overlapping neighbourhoods; we relate this to global robustness of the clustering to removal of the weakest links. Furthermore, we identify local learning rules that establish integrative networks and improve network traffic in response to past traffic failures. Our findings identify a general organization for complex systems that strikes a balance between efficient local and global communication in their strong interactions, while allowing for robust, exploratory development of weak interactions.

  17. Text documents as social networks

    NASA Astrophysics Data System (ADS)

    Balinsky, Helen; Balinsky, Alexander; Simske, Steven J.

    2012-03-01

    The extraction of keywords and features is a fundamental problem in text data mining. Document processing applications directly depend on the quality and speed of the identification of salient terms and phrases. Applications as disparate as automatic document classification, information visualization, filtering and security policy enforcement all rely on the quality of automatically extracted keywords. Recently, a novel approach to rapid change detection in data streams and documents has been developed. It is based on ideas from image processing and in particular on the Helmholtz Principle from the Gestalt Theory of human perception. By modeling a document as a one-parameter family of graphs with its sentences or paragraphs defining the vertex set and with edges defined by Helmholtz's principle, we demonstrated that for some range of the parameters, the resulting graph becomes a small-world network. In this article we investigate the natural orientation of edges in such small world networks. For two connected sentences, we can say which one is the first and which one is the second, according to their position in a document. This will make such a graph look like a small WWW-type network and PageRank type algorithms will produce interesting ranking of nodes in such a document.

  18. How multiple social networks affect user awareness: The information diffusion process in multiplex networks

    NASA Astrophysics Data System (ADS)

    Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

    2015-10-01

    The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ . Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

  19. How multiple social networks affect user awareness: The information diffusion process in multiplex networks.

    PubMed

    Li, Weihua; Tang, Shaoting; Fang, Wenyi; Guo, Quantong; Zhang, Xiao; Zheng, Zhiming

    2015-10-01

    The information diffusion process in single complex networks has been extensively studied, especially for modeling the spreading activities in online social networks. However, individuals usually use multiple social networks at the same time, and can share the information they have learned from one social network to another. This phenomenon gives rise to a new diffusion process on multiplex networks with more than one network layer. In this paper we account for this multiplex network spreading by proposing a model of information diffusion in two-layer multiplex networks. We develop a theoretical framework using bond percolation and cascading failure to describe the intralayer and interlayer diffusion. This allows us to obtain analytical solutions for the fraction of informed individuals as a function of transmissibility T and the interlayer transmission rate θ. Simulation results show that interaction between layers can greatly enhance the information diffusion process. And explosive diffusion can occur even if the transmissibility of the focal layer is under the critical threshold, due to interlayer transmission.

  20. Complex network analysis of time series

    NASA Astrophysics Data System (ADS)

    Gao, Zhong-Ke; Small, Michael; Kurths, Jürgen

    2016-12-01

    Revealing complicated behaviors from time series constitutes a fundamental problem of continuing interest and it has attracted a great deal of attention from a wide variety of fields on account of its significant importance. The past decade has witnessed a rapid development of complex network studies, which allow to characterize many types of systems in nature and technology that contain a large number of components interacting with each other in a complicated manner. Recently, the complex network theory has been incorporated into the analysis of time series and fruitful achievements have been obtained. Complex network analysis of time series opens up new venues to address interdisciplinary challenges in climate dynamics, multiphase flow, brain functions, ECG dynamics, economics and traffic systems.

  1. Massive Social Network Analysis: Mining Twitter for Social Good

    SciTech Connect

    Ediger, David; Jiang, Karl; Riedy, Edward J.; Bader, David A.; Corley, Courtney D.; Farber, Robert M.; Reynolds, William

    2010-10-11

    Social networks produce an enormous quantity of data. Facebook consists of over 400 million active users sharing over 5 billion pieces of information each month. Analyzing this vast quantity of unstructured data presents challenges for software and hardware. We present GraphCT, a Graph Characterization Tooklit for massive graphs representing social network data. On a 128-processor Cray XMT, GraphCT estimates the betweenness centrality of an artificially generated (R-MAT) 537 million vertex, 8.6 billion edge graph in 55 minutes. We use GraphCT to analyze public data from Twitter, a microblogging network. Twitter's message connections appear primarily tree-structured as a news dissemination system. Within the public data, however, are clusters of conversations. Using GraphCT, we can rank actors within these conversations and help analysts focus attention on a much smaller data subset.

  2. Hierarchical social networks and information flow

    NASA Astrophysics Data System (ADS)

    López, Luis; F. F. Mendes, Jose; Sanjuán, Miguel A. F.

    2002-12-01

    Using a simple model for the information flow on social networks, we show that the traditional hierarchical topologies frequently used by companies and organizations, are poorly designed in terms of efficiency. Moreover, we prove that this type of structures are the result of the individual aim of monopolizing as much information as possible within the network. As the information is an appropriate measurement of centrality, we conclude that this kind of topology is so attractive for leaders, because the global influence each actor has within the network is completely determined by the hierarchical level occupied.

  3. Network representations of immune system complexity

    PubMed Central

    Subramanian, Naeha; Torabi-Parizi, Parizad; Gottschalk, Rachel A.; Germain, Ronald N.; Dutta, Bhaskar

    2015-01-01

    The mammalian immune system is a dynamic multi-scale system composed of a hierarchically organized set of molecular, cellular and organismal networks that act in concert to promote effective host defense. These networks range from those involving gene regulatory and protein-protein interactions underlying intracellular signaling pathways and single cell responses to increasingly complex networks of in vivo cellular interaction, positioning and migration that determine the overall immune response of an organism. Immunity is thus not the product of simple signaling events but rather non-linear behaviors arising from dynamic, feedback-regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multi-scale spatial and temporal interactions, permitting development of computational models that can be used to predict responses to perturbation. Recent technological advances permit collection of comprehensive datasets at multiple molecular and cellular levels while advances in network biology support representation of the relationships of components at each level as physical or functional interaction networks. The latter facilitate effective visualization of patterns and recognition of emergent properties arising from the many interactions of genes, molecules, and cells of the immune system. We illustrate the power of integrating ‘omics’ and network modeling approaches for unbiased reconstruction of signaling and transcriptional networks with a focus on applications involving the innate immune system. We further discuss future possibilities for reconstruction of increasingly complex cellular and organism-level networks and development of sophisticated computational tools for prediction of emergent immune behavior arising from the concerted action of these networks. PMID:25625853

  4. Micro-Macro Analysis of Complex Networks

    PubMed Central

    Marchiori, Massimo; Possamai, Lino

    2015-01-01

    Complex systems have attracted considerable interest because of their wide range of applications, and are often studied via a “classic” approach: study a specific system, find a complex network behind it, and analyze the corresponding properties. This simple methodology has produced a great deal of interesting results, but relies on an often implicit underlying assumption: the level of detail on which the system is observed. However, in many situations, physical or abstract, the level of detail can be one out of many, and might also depend on intrinsic limitations in viewing the data with a different level of abstraction or precision. So, a fundamental question arises: do properties of a network depend on its level of observability, or are they invariant? If there is a dependence, then an apparently correct network modeling could in fact just be a bad approximation of the true behavior of a complex system. In order to answer this question, we propose a novel micro-macro analysis of complex systems that quantitatively describes how the structure of complex networks varies as a function of the detail level. To this extent, we have developed a new telescopic algorithm that abstracts from the local properties of a system and reconstructs the original structure according to a fuzziness level. This way we can study what happens when passing from a fine level of detail (“micro”) to a different scale level (“macro”), and analyze the corresponding behavior in this transition, obtaining a deeper spectrum analysis. The obtained results show that many important properties are not universally invariant with respect to the level of detail, but instead strongly depend on the specific level on which a network is observed. Therefore, caution should be taken in every situation where a complex network is considered, if its context allows for different levels of observability. PMID:25635812

  5. Structurally robust control of complex networks

    NASA Astrophysics Data System (ADS)

    Nacher, Jose C.; Akutsu, Tatsuya

    2015-01-01

    Robust control theory has been successfully applied to numerous real-world problems using a small set of devices called controllers. However, the real systems represented by networks contain unreliable components and modern robust control engineering has not addressed the problem of structural changes on complex networks including scale-free topologies. Here, we introduce the concept of structurally robust control of complex networks and provide a concrete example using an algorithmic framework that is widely applied in engineering. The developed analytical tools, computer simulations, and real network analyses lead herein to the discovery that robust control can be achieved in scale-free networks with exactly the same order of controllers required in a standard nonrobust configuration by adjusting only the minimum degree. The presented methodology also addresses the probabilistic failure of links in real systems, such as neural synaptic unreliability in Caenorhabditis elegans, and suggests a new direction to pursue in studies of complex networks in which control theory has a role.

  6. Privacy-Preserving Relationship Path Discovery in Social Networks

    NASA Astrophysics Data System (ADS)

    Mezzour, Ghita; Perrig, Adrian; Gligor, Virgil; Papadimitratos, Panos

    As social networks sites continue to proliferate and are being used for an increasing variety of purposes, the privacy risks raised by the full access of social networking sites over user data become uncomfortable. A decentralized social network would help alleviate this problem, but offering the functionalities of social networking sites is a distributed manner is a challenging problem. In this paper, we provide techniques to instantiate one of the core functionalities of social networks: discovery of paths between individuals. Our algorithm preserves the privacy of relationship information, and can operate offline during the path discovery phase. We simulate our algorithm on real social network topologies.

  7. Studies on a network of complex neurons

    NASA Astrophysics Data System (ADS)

    Chakravarthy, Srinivasa V.; Ghosh, Joydeep

    1993-08-01

    In the last decade, much effort has been directed towards understanding the role of chaos in the brain. Work with rabbits reveals that in the resting state the electrical activity on the surface of the olfactory bulb is chaotic. But, when the animal is involved in a recognition task, the activity shifts to a specific pattern corresponding to the odor that is being recognized. Unstable, quasiperiodic behavior can be found in a class of conservative, deterministic physical systems called the Hamiltonian systems. In this paper, we formulate a complex version of Hopfield's network os real parameters and show that a variation on this model is a conservative system. Conditions under which the complex network can be used as a Content Addressable memory are studied. We also examine the effect of singularities of the complex sigmoid function on the network dynamics. The network exhibits unpredictable behavior at the singularities due to the failure of a uniqueness condition for the solution of the dynamic equations. On incorporating a weight adaptation rule, the structure of the resulting complex network equations is shown to have an interesting similarity with Kosko's Adaptive Bidirectional Associative Memory.

  8. Studies on a network of complex neurons

    NASA Astrophysics Data System (ADS)

    Chakravarthy, Srinivasa V.; Ghosh, Joydeep

    1993-09-01

    In the last decade, much effort has been directed towards understanding the role of chaos in the brain. Work with rabbits reveals that in the resting state the electrical activity on the surface of the olfactory bulb is chaotic. But, when the animal is involved in a recognition task, the activity shifts to a specific pattern corresponding to the odor that is being recognized. Unstable, quasiperiodic behavior can be found in a class of conservative, deterministic physical systems called the Hamiltonian systems. In this paper, we formulate a complex version of Hopfield's network of real parameters and show that a variation on this model is a conservative system. Conditions under which the complex network can be used as a Content Addressable memory are studied. We also examine the effect of singularities of the complex sigmoid function on the network dynamics. The network exhibits unpredictable behavior at the singularities due to the failure of a uniqueness condition for the solution of the dynamic equations. On incorporating a weight adaptation rule, the structure of the resulting complex network equations is shown to have an interesting similarity with Kosko's Adaptive Bidirectional Associative Memory.

  9. Complexity, dynamic cellular network, and tumorigenesis.

    PubMed

    Waliszewski, P

    1997-01-01

    A holistic approach to tumorigenesis is proposed. The main element of the model is the existence of dynamic cellular network. This network comprises a molecular and an energetistic structure of a cell connected through the multidirectional flow of information. The interactions within dynamic cellular network are complex, stochastic, nonlinear, and also involve quantum effects. From this non-reductionist perspective, neither tumorigenesis can be limited to the genetic aspect, nor the initial event must be of molecular nature, nor mutations and epigenetic factors are mutually exclusive, nor a link between cause and effect can be established. Due to complexity, an unstable stationary state of dynamic cellular network rather than a group of unrelated genes determines the phenotype of normal and transformed cells. This implies relativity of tumor suppressor genes and oncogenes. A bifurcation point is defined as an unstable state of dynamic cellular network leading to the other phenotype-stationary state. In particular, the bifurcation point may be determined by a change of expression of a single gene. Then, the gene is called bifurcation point gene. The unstable stationary state facilitates the chaotic dynamics. This may result in a fractal dimension of both normal and tumor tissues. The co-existence of chaotic dynamics and complexity is the essence of cellular processes and shapes differentiation, morphogenesis, and tumorigenesis. In consequence, tumorigenesis is a complex, unpredictable process driven by the interplay between self-organisation and selection.

  10. Size reduction of complex networks preserving modularity

    SciTech Connect

    Arenas, A.; Duch, J.; Fernandez, A.; Gomez, S.

    2008-12-24

    The ubiquity of modular structure in real-world complex networks is being the focus of attention in many trials to understand the interplay between network topology and functionality. The best approaches to the identification of modular structure are based on the optimization of a quality function known as modularity. However this optimization is a hard task provided that the computational complexity of the problem is in the NP-hard class. Here we propose an exact method for reducing the size of weighted (directed and undirected) complex networks while maintaining invariant its modularity. This size reduction allows the heuristic algorithms that optimize modularity for a better exploration of the modularity landscape. We compare the modularity obtained in several real complex-networks by using the Extremal Optimization algorithm, before and after the size reduction, showing the improvement obtained. We speculate that the proposed analytical size reduction could be extended to an exact coarse graining of the network in the scope of real-space renormalization.

  11. Center of mass in complex networks

    PubMed Central

    Fu, Chuanji; Gao, Yachun; Cai, Shimin; Yang, Hongchun; Yang, Chun

    2017-01-01

    Network dynamics is always a big challenge in nonlinear dynamics. Although great advancements have been made in various types of complex systems, an universal theoretical framework is required. In this paper, we introduce the concept of center of ‘mass’ of complex networks, where ‘mass’ stands for node importance or centrality in contrast to that of particle systems, and further prove that the phase transition and evolutionary state of the system can be characterized by the activity of center of ‘mass’. The steady states of several complex networks (gene regulatory networks and epidemic spreading systems) are then studied by analytically calculating the decoupled equation of the dynamic activity of center of ‘mass’, which is derived from the dynamic equation of the complex networks. The limitations of this method are also pointed out, such as the dynamical problems that related with the relative activities among components, and those systems that consist of oscillatory or chaotic motions. PMID:28106109

  12. Characterizing English Poetic Style Using Complex Networks

    NASA Astrophysics Data System (ADS)

    Roxas-Villanueva, Ranzivelle Marianne; Nambatac, Maelori Krista; Tapang, Giovanni

    Complex networks have been proven useful in characterizing written texts. Here, we use networks to probe if there exist a similarity within, and difference across, era as reflected within the poem's structure. In literary history, boundary lines are set to distinguish the change in writing styles through time. We obtain the network parameters and motif frequencies of 845 poems published from 1522 to 1931 and relate this to the writing of the Elizabethan, 17th Century, Augustan, Romantic and Victorian eras. Analysis of the different network parameters shows a significant difference of the Augustan era (1667-1780) with the rest. The network parameters and the convex hull and centroids of the motif frequencies reflect the adjectival sequence pattern of the poems of the Augustan era.

  13. The complexity of Chinese syntactic dependency networks

    NASA Astrophysics Data System (ADS)

    Liu, Haitao

    2008-05-01

    This paper proposes how to build a syntactic network based on syntactic theory and presents some statistical properties of Chinese syntactic dependency networks based on two Chinese treebanks with different genres. The results show that the two syntactic networks are small-world networks, and their degree distributions obey a power law. The finding, that the two syntactic networks have the same diameter and different average degrees, path lengths, clustering coefficients and power exponents, can be seen as an indicator that complexity theory can work as a means of stylistic study. The paper links the degree of a vertex with a valency of a word, the small world with the minimized average distance of a language, that reinforces the explanations of the findings from linguistics.

  14. Betweenness centrality correlation in social networks

    NASA Astrophysics Data System (ADS)

    Goh, K.-I.; Oh, E.; Kahng, B.; Kim, D.

    2003-01-01

    Scale-free (SF) networks exhibiting a power-law degree distribution can be grouped into the assortative, dissortative, and neutral networks according to the behavior of the degree-degree correlation coefficient. Here we investigate the betweenness centrality (BC) correlation for each type of SF networks. While the BC-BC correlation coefficients behave similarly to the degree-degree correlation coefficients for the dissortative and neutral networks, the BC correlation is nontrivial for the assortative ones found mainly in social networks. The mean BC of neighbors of a vertex with BC gi is almost independent of gi, implying that each person is surrounded by almost the same influential environments of people no matter how influential the person may be.

  15. Computational Statistical Methods for Social Network Models

    PubMed Central

    Hunter, David R.; Krivitsky, Pavel N.; Schweinberger, Michael

    2013-01-01

    We review the broad range of recent statistical work in social network models, with emphasis on computational aspects of these methods. Particular focus is applied to exponential-family random graph models (ERGM) and latent variable models for data on complete networks observed at a single time point, though we also briefly review many methods for incompletely observed networks and networks observed at multiple time points. Although we mention far more modeling techniques than we can possibly cover in depth, we provide numerous citations to current literature. We illustrate several of the methods on a small, well-known network dataset, Sampson’s monks, providing code where possible so that these analyses may be duplicated. PMID:23828720

  16. Network Analysis of Social Interactions in Laboratories

    NASA Astrophysics Data System (ADS)

    Warren, Aaron R.

    2008-10-01

    An ongoing study of the structure, function, and evolution of individual activity within lab groups is introduced. This study makes extensive use of techniques from social network analysis. These techniques allow rigorous quantification and hypothesis-testing of the interactions inherent in social groups and the impact of intrinsic characteristics of individuals on their social interactions. As these techniques are novel within the physics education research community, an overview of their meaning and application is given. We then present preliminary results from videotaped laboratory groups involving mixed populations of traditional and non-traditional students in an introductory algebra-based physics course.

  17. Complex root networks of Chinese characters

    NASA Astrophysics Data System (ADS)

    Lee, Po-Han; Chen, Jia-Ling; Wang, Po-Cheng; Chi, Ting-Ting; Xiao, Zhi-Ren; Jhang, Zih-Jian; Yeh, Yeong-Nan; Chen, Yih-Yuh; Hu, Chin-Kun

    There are several sets of Chinese characters still available today, including Oracle Bone Inscriptions (OBI) in Shang Dynasty, Chu characters (CC) used in Chu of Warring State Period, Small Seal Script in dictionary Shuowen Jiezi (SJ) in Eastern Han Dynasty, and Kangxi Dictionary (KD) in Qing Dynasty. Such as Chinese characters were all constructed via combinations of meaningful patterns, called roots. Our studies for the complex networks of all roots indicate that the roots of the characters in OBI, CC, SJ and KD have characteristics of small world networks and scale-free networks.

  18. Role of Edges in Complex Network Epidemiology

    NASA Astrophysics Data System (ADS)

    Zhang, Hao; Jiang, Zhi-Hong; Wang, Hui; Xie, Fei; Chen, Chao

    2012-09-01

    In complex network epidemiology, diseases spread along contacting edges between individuals and different edges may play different roles in epidemic outbreaks. Quantifying the efficiency of edges is an important step towards arresting epidemics. In this paper, we study the efficiency of edges in general susceptible-infected-recovered models, and introduce the transmission capability to measure the efficiency of edges. Results show that deleting edges with the highest transmission capability will greatly decrease epidemics on scale-free networks. Basing on the message passing approach, we get exact mathematical solution on configuration model networks with edge deletion in the large size limit.

  19. Architectural approach for quality and safety aware healthcare social networks.

    PubMed

    López, Diego M; Blobel, Bernd; González, Carolina

    2012-01-01

    Quality of information and privacy and safety issues are frequently identified as main limitations to make most benefit from social media in healthcare. The objective of the paper is to contribute to the analysis of healthcare social networks (SN), and online healthcare social network services (SNS) by proposing a formal architectural analysis of healthcare SN and SNS, considering the complexity of both systems, but stressing on quality, safety and usability aspects. Quality policies are necessary to control the quality of content published by experts and consumers. Privacy and safety policies protect against inappropriate use of information and users responsibility for sharing information. After the policies are established and documented, a proof of concept online SNS supporting primary healthcare promotion is presented in the paper.

  20. Employment, Social Networks and Undocumented Migrants: The Employer Perspective

    PubMed Central

    Bloch, Alice; McKay, Sonia

    2015-01-01

    This article draws on data from qualitative interviews with ethnic enclave and ethnic economy business entrepreneurs from Chinese, Bangladeshi and Turkish-speaking communities in London. Routes into business and worker recruitment practices are explored, demonstrating the centrality of social capital in the form of family and other social networks within these processes. The article investigates what employers consider the desirable characteristics of workers: trust, kinship, gender, social networks, language compatibility and the needs of the business intersect with racialised notions of workers’ strengths and characteristics. Finally, we consider changing practices in relation to the employment of undocumented migrants, in the context of an increasingly punitive legislative regime. The complex and variable impact of policy alongside the ways in which other obligations and positions outweigh the fear and risks of sanctions associated with non-compliance is revealed. PMID:25866421

  1. Employment, Social Networks and Undocumented Migrants: The Employer Perspective.

    PubMed

    Bloch, Alice; McKay, Sonia

    2015-02-01

    This article draws on data from qualitative interviews with ethnic enclave and ethnic economy business entrepreneurs from Chinese, Bangladeshi and Turkish-speaking communities in London. Routes into business and worker recruitment practices are explored, demonstrating the centrality of social capital in the form of family and other social networks within these processes. The article investigates what employers consider the desirable characteristics of workers: trust, kinship, gender, social networks, language compatibility and the needs of the business intersect with racialised notions of workers' strengths and characteristics. Finally, we consider changing practices in relation to the employment of undocumented migrants, in the context of an increasingly punitive legislative regime. The complex and variable impact of policy alongside the ways in which other obligations and positions outweigh the fear and risks of sanctions associated with non-compliance is revealed.

  2. Prediction of missing links and reconstruction of complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Cheng-Jun; Zeng, An

    2016-04-01

    Predicting missing links in complex networks is of great significance from both theoretical and practical point of view, which not only helps us understand the evolution of real systems but also relates to many applications in social, biological and online systems. In this paper, we study the features of different simple link prediction methods, revealing that they may lead to the distortion of networks’ structural and dynamical properties. Moreover, we find that high prediction accuracy is not definitely corresponding to a high performance in preserving the network properties when using link prediction methods to reconstruct networks. Our work highlights the importance of considering the feedback effect of the link prediction methods on network properties when designing the algorithms.

  3. Identifying influential spreaders in complex networks based on gravity formula

    NASA Astrophysics Data System (ADS)

    Ma, Ling-ling; Ma, Chuang; Zhang, Hai-Feng; Wang, Bing-Hong

    2016-06-01

    How to identify the influential spreaders in social networks is crucial for accelerating/hindering information diffusion, increasing product exposure, controlling diseases and rumors, and so on. In this paper, by viewing the k-shell value of each node as its mass and the shortest path distance between two nodes as their distance, then inspired by the idea of the gravity formula, we propose a gravity centrality index to identify the influential spreaders in complex networks. The comparison between the gravity centrality index and some well-known centralities, such as degree centrality, betweenness centrality, closeness centrality, and k-shell centrality, and so forth, indicates that our method can effectively identify the influential spreaders in real networks as well as synthetic networks. We also use the classical Susceptible-Infected-Recovered (SIR) epidemic model to verify the good performance of our method.

  4. Measuring robustness of community structure in complex networks

    NASA Astrophysics Data System (ADS)

    Li, Hui-Jia; Wang, Hao; Chen, Luonan

    2014-12-01

    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 derived 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, γC , 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 γC . The research can be applied to broad clustering problems in network analysis and data mining due to its solid mathematical basis and experimental effects.

  5. Structural and dynamical properties of complex networks

    NASA Astrophysics Data System (ADS)

    Ghoshal, Gourab

    Recent years have witnessed a substantial amount of interest within the physics community in the properties of networks. Techniques from statistical physics coupled with the widespread availability of computing resources have facilitated studies ranging from large scale empirical analysis of the worldwide web, social networks, biological systems, to the development of theoretical models and tools to explore the various properties of these systems. Following these developments, in this dissertation, we present and solve for a diverse set of new problems, investigating the structural and dynamical properties of both model and real world networks. We start by defining a new metric to measure the stability of network structure to disruptions, and then using a combination of theory and simulation study its properties in detail on artificially generated networks; we then compare our results to a selection of networks from the real world and find good agreement in most cases. In the following chapter, we propose a mathematical model that mimics the structure of popular file-sharing websites such as Flickr and CiteULike and demonstrate that many of its properties can solved exactly in the limit of large network size. The remaining part of the dissertation primarily focuses on the dynamical properties of networks. We first formulate a model of a network that evolves under the addition and deletion of vertices and edges, and solve for the equilibrium degree distribution for a variety of cases of interest. We then consider networks whose structure can be manipulated by adjusting the rules by which vertices enter and leave the network. We focus in particular on degree distributions and show that, with some mild constraints, it is possible by a suitable choice of rules to arrange for the network to have any degree distribution we desire. In addition we define a simple local algorithm by which appropriate rules can be implemented in practice. Finally, we conclude our

  6. Proximity data-loggers increase the quantity and quality of social network data.

    PubMed

    Ryder, Thomas B; Horton, Brent M; van den Tillaart, Mike; Morales, Juan De Dios; Moore, Ignacio T

    2012-12-23

    Social network analysis is an ideal quantitative tool for advancing our understanding of complex social behaviour. However, this approach is often limited by the challenges of accurately characterizing social structure and measuring network heterogeneity. Technological advances have facilitated the study of social networks, but to date, all such work has focused on large vertebrates. Here, we provide proof of concept for using proximity data-logging to quantify the frequency of social interactions, construct weighted networks and characterize variation in the social behaviour of a lek-breeding bird, the wire-tailed manakin, Pipra filicauda. Our results highlight how this approach can ameliorate the challenges of social network data collection and analysis by concurrently improving data quality and quantity.

  7. Data reliability in complex directed networks

    NASA Astrophysics Data System (ADS)

    Sanz, Joaquín; Cozzo, Emanuele; Moreno, Yamir

    2013-12-01

    The availability of data from many different sources and fields of science has made it possible to map out an increasing number of networks of contacts and interactions. However, quantifying how reliable these data are remains an open problem. From Biology to Sociology and Economics, the identification of false and missing positives has become a problem that calls for a solution. In this work we extend one of the newest, best performing models—due to Guimerá and Sales-Pardo in 2009—to directed networks. The new methodology is able to identify missing and spurious directed interactions with more precision than previous approaches, which renders it particularly useful for analyzing data reliability in systems like trophic webs, gene regulatory networks, communication patterns and several social systems. We also show, using real-world networks, how the method can be employed to help search for new interactions in an efficient way.

  8. [Social representations of complex social environments through drawings and texts].

    PubMed

    de Souza Filho, Edson

    2009-12-01

    The objective of this work was to observe representations of complex social environments through drawings and texts. We adopted Moscovici's theory, which supposes that the social representations phenomenon is a modern and democratic societies' manifestation. To overcome existing constraints/unequalities, we adopted drawings as a means of expression. We asked secondary students, self-defined as African-Brazilians, Mixed people and Whites, to drawn the classroom. The material was analysed according to manifest themes. There was statistical diferentiation on objects, spatial perspectives, teacher colleagues and student. Among high academic performer African-Brazilians we noticed more references to colleagues and conflict/negotiation with teachers.

  9. Social networks of educated nematodes

    Technology Transfer Automated Retrieval System (TEKTRAN)

    Entomopathogenic nematodes are obligate lethal parasitoids of insect larvae that navigate a chemically complex belowground environment while interacting with their insect hosts, plants, and each other. In this environment, prior exposure to volatile compounds appears to prime nematodes in a compound...

  10. Adoption of Social Networking in Education: A Study of the Use of Social Networks by Higher Education Students in Oman

    ERIC Educational Resources Information Center

    Al-Mukhaini, Elham M.; Al-Qayoudhi, Wafa S.; Al-Badi, Ali H.

    2014-01-01

    The use of social networks is a growing phenomenon, being increasingly important in both private and academic life. Social networks are used as tools to enable users to have social interaction. The use of social networks (SNs) complements and enhances the teaching in traditional classrooms. For example, YouTube, Facebook, wikis, and blogs provide…

  11. Offdiagonal complexity: A computationally quick complexity measure for graphs and networks

    NASA Astrophysics Data System (ADS)

    Claussen, Jens Christian

    2007-02-01

    A vast variety of biological, social, and economical networks shows topologies drastically differing from random graphs; yet the quantitative characterization remains unsatisfactory from a conceptual point of view. Motivated from the discussion of small scale-free networks, a biased link distribution entropy is defined, which takes an extremum for a power-law distribution. This approach is extended to the node-node link cross-distribution, whose nondiagonal elements characterize the graph structure beyond link distribution, cluster coefficient and average path length. From here a simple (and computationally cheap) complexity measure can be defined. This offdiagonal complexity (OdC) is proposed as a novel measure to characterize the complexity of an undirected graph, or network. While both for regular lattices and fully connected networks OdC is zero, it takes a moderately low value for a random graph and shows high values for apparently complex structures as scale-free networks and hierarchical trees. The OdC approach is applied to the Helicobacter pylori protein interaction network and randomly rewired surrogates.

  12. Social Network Data and Practices: The Case of Friendfeed

    NASA Astrophysics Data System (ADS)

    Celli, Fabio; di Lascio, F. Marta L.; Magnani, Matteo; Pacelli, Barbara; Rossi, Luca

    Due to their large worldwide adoption, Social Network Sites (SNSs) have been widely used in many global events as an important source to spread news and information. While the searchability and persistence of this information make it ideal for sociological research, a quantitative approach is still challenging because of the size and complexity of the data. In this paper we provide a first analysis of Friendfeed, a well-known and feature-rich SNS.

  13. Social Dynamics within Electronic Networks of Practice

    ERIC Educational Resources Information Center

    Mattson, Thomas A., Jr.

    2013-01-01

    Electronic networks of practice (eNoP) are special types of electronic social structures focused on discussing domain-specific problems related to a skill-based craft or profession in question and answer style forums. eNoP have implemented peer-to-peer feedback systems in order to motivate future contributions and to distinguish contribution…

  14. Unravelling the Social Network: Theory and Research

    ERIC Educational Resources Information Center

    Merchant, Guy

    2012-01-01

    Despite the widespread popularity of social networking sites (SNSs) amongst children and young people in compulsory education, relatively little scholarly work has explored the fundamental issues at stake. This paper makes an original contribution to the field by locating the study of this online activity within the broader terrain of social…

  15. Ethical Considerations of Social Networking for Counsellors

    ERIC Educational Resources Information Center

    Bratt, William Edgar Vernon

    2010-01-01

    The use of online social networking websites has increased among Canadians in recent years. There are many professional and ethical implications for counsellors who use these sites (Boyd, 2007). Although they offer advantages to counsellors, their use can also raise issues around ethical conduct. Because the counselling literature has not yet…

  16. Libraries' Place in Virtual Social Networks

    ERIC Educational Resources Information Center

    Mathews, Brian S.

    2007-01-01

    Do libraries belong in the virtual world of social networking? With more than 100 million users, this environment is impossible to ignore. A rising philosophy for libraries, particularly in blog-land, involves the concept of being where the users are. Simply using new media to deliver an old message is not progress. Instead, librarians should…

  17. Social Networks, Teacher Beliefs, and Educational Change.

    ERIC Educational Resources Information Center

    Aston, Michelle; Hyle, Adrienne E.

    This study examined the social networks of teachers' strong and weak ties and the impact of those associations upon their beliefs about the realities of elementary education in terms of the knowledge and skills that influenced those realities: school context, general pedagogy, specific subject matter pedagogy, nature of the learner, and self as…

  18. Social Networking: A Collaborative Open Educational Resource

    ERIC Educational Resources Information Center

    Toetenel, Lisette

    2014-01-01

    Studies undertaken since the introduction of Web 2.0 have focussed mainly on open educational resources (OERs) such as email, blogging and virtual learning environments. No consistent efforts have been undertaken to study the use of social networking sites as a tool for learning in the second language classroom. This study examined the use of…

  19. Social Networking Tools for Academic Libraries

    ERIC Educational Resources Information Center

    Chu, Samuel Kai-Wah; Du, Helen S.

    2013-01-01

    This is an exploratory study investigating the use of social networking tools in academic libraries, examining the extent of their use, library staff's perceptions of their usefulness and challenges, and factors influencing decisions to use or not to use such tools. Invitations to participate in a web-based survey were sent to 140 university…

  20. Using Social Networking in the Library

    ERIC Educational Resources Information Center

    Lindsay, Elizabeth Blakesley

    2009-01-01

    With celebrities discussing Twitter on television talk shows, Facebook being used by people to share pictures of their grandchildren, and academic seminars being delivered in Second Life, it is hard to get through a day without being faced with some sort of social networking tool. Librarians often talk about the importance of outreach and of…

  1. The Benefits and Limitations of Social Networking

    ERIC Educational Resources Information Center

    Strom, Paris; Strom, Robert

    2012-01-01

    The Center for the Digital Future at the University of Southern California surveys 2,000 households each year to find out how online technology affects Internet users. Findings in the latest report show social networks are increasing and a majority of users report feeling as strongly about their communities online as their real-world communities.…

  2. Social Networking Postings: Views from School Principals

    ERIC Educational Resources Information Center

    Griffin, Marlynn M.; Lake, Robert L.

    2012-01-01

    Numerous recent media accounts indicate that teachers are being fired, put on probation, or otherwise censured because of information found on their social networking sites (SNS). While the literature in business, psychology, and pharmacy shows initial investigations of the impact of SNS information on hiring decisions, this area has not been…

  3. Social Networking Sites as a Learning Tool

    ERIC Educational Resources Information Center

    Sanchez-Casado, Noelia; Cegarra Navarro, Juan Gabriel; Wensley, Anthony; Tomaseti-Solano, Eva

    2016-01-01

    Purpose: Over the past few years, social networking sites (SNSs) have become very useful for firms, allowing companies to manage the customer-brand relationships. In this context, SNSs can be considered as a learning tool because of the brand knowledge that customers develop from these relationships. Because of the fact that knowledge in…

  4. Social Networking Services in E-Learning

    ERIC Educational Resources Information Center

    Weber, Peter; Rothe, Hannes

    2016-01-01

    This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we…

  5. Social Network Predictors of Bullying and Victimization

    ERIC Educational Resources Information Center

    Mouttapa, Michele; Valente, Tom; Gallaher, Peggy; Rohrbach, Louise Ann; Unger, Jennifer B.

    2004-01-01

    This study examined whether bullies, victims, and aggressive victims (those who are both bullies and victims) differed on classroom social network variables, gender, and ethnicity. Survey data were collected from a primarily Latino and Asian sample of 1,368 Southern California 6th graders (mean age = 11.3 years). Logistic regression analyses were…

  6. Parental Social Networks and Child Development.

    ERIC Educational Resources Information Center

    Homel, R.; Burns, A.

    This paper looks at the relationship between parents' social networks and aspects of child development. It has often been suggested that parents' links with kin, neighbors, friends, and local and non-local organizations are likely to have many effects on their children's development. These effects, however, have never been systematically…

  7. How to Analyze Company Using Social Network?

    NASA Astrophysics Data System (ADS)

    Palus, Sebastian; Bródka, Piotr; Kazienko, Przemysław

    Every single company or institution wants to utilize its resources in the most efficient way. In order to do so they have to be have good structure. The new way to analyze company structure by utilizing existing within company natural social network and example of its usage on Enron company are presented in this paper.

  8. Tractable Analysis for Large Social Networks

    ERIC Educational Resources Information Center

    Zhang, Bin

    2012-01-01

    Social scientists usually are more interested in consumers' dichotomous choice, such as purchase a product or not, adopt a technology or not, etc. However, up to date, there is nearly no model can help us solve the problem of multi-network effects comparison with a dichotomous dependent variable. Furthermore, the study of multi-network…

  9. Protecting Personal Information on Social Networking Sites

    ERIC Educational Resources Information Center

    Gallant, David T.

    2011-01-01

    Almost everyone uses social networking sites like Facebook, MySpace, and LinkedIn. Since Facebook is the most popular site in the history of the Internet, this article will focus on how one can protect his/her personal information and how that extends to protecting the private information of others.

  10. Social Network Structures among Groundnut Farmers

    ERIC Educational Resources Information Center

    Thuo, Mary; Bell, Alexandra A.; Bravo-Ureta, Boris E.; Okello, David K.; Okoko, Evelyn Nasambu; Kidula, Nelson L.; Deom, C. Michael; Puppala, Naveen

    2013-01-01

    Purpose: Groundnut farmers in East Africa have experienced declines in production despite research and extension efforts to increase productivity. This study examined how social network structures related to acquisition of information about new seed varieties and productivity among groundnut farmers in Uganda and Kenya.…

  11. Social Networking Services in E-Learning

    ERIC Educational Resources Information Center

    Weber, Peter; Rothe, Hannes

    2012-01-01

    This paper is a report on the findings of a study conducted on the use of the social networking service NING in a cross-location e-learning setting named "Net Economy." We describe how we implemented NING as a fundamental part of the setting through a special phase concept and team building approach. With the help of user statistics, we examine…

  12. Social Networks and Youngspeak in Study Abroad

    ERIC Educational Resources Information Center

    Fernandez, Julieta

    2013-01-01

    Interactions with experienced L2 speakers can have a positive effect on study abroad (SA) students' language acquisition (e.g., development in informal vocabulary use, Schauer, 2009). Many SA students, however, experience difficulties in establishing social networks in Latin America (e.g., Isabelli-Garcia, 2006). SA experience, therefore, cannot…

  13. Electronic Social Networks, Teaching, and Learning

    ERIC Educational Resources Information Center

    Pidduck, Anne Banks

    2010-01-01

    This paper explores the relationship between electronic social networks, teaching, and learning. Previous studies have shown a strong positive correlation between student engagement and learning. By extending this work to engage instructors and add an electronic component, our study shows possible teaching improvement as well. In particular,…

  14. Effects of deception in social networks.

    PubMed

    Iñiguez, Gerardo; Govezensky, Tzipe; Dunbar, Robin; Kaski, Kimmo; Barrio, Rafael A

    2014-09-07

    Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies ('white' lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agent-based model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these group-level effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole.

  15. Effects of deception in social networks

    PubMed Central

    Iñiguez, Gerardo; Govezensky, Tzipe; Dunbar, Robin; Kaski, Kimmo; Barrio, Rafael A.

    2014-01-01

    Honesty plays a crucial role in any situation where organisms exchange information or resources. Dishonesty can thus be expected to have damaging effects on social coherence if agents cannot trust the information or goods they receive. However, a distinction is often drawn between prosocial lies (‘white’ lies) and antisocial lying (i.e. deception for personal gain), with the former being considered much less destructive than the latter. We use an agent-based model to show that antisocial lying causes social networks to become increasingly fragmented. Antisocial dishonesty thus places strong constraints on the size and cohesion of social communities, providing a major hurdle that organisms have to overcome (e.g. by evolving counter-deception strategies) in order to evolve large, socially cohesive communities. In contrast, white lies can prove to be beneficial in smoothing the flow of interactions and facilitating a larger, more integrated network. Our results demonstrate that these group-level effects can arise as emergent properties of interactions at the dyadic level. The balance between prosocial and antisocial lies may set constraints on the structure of social networks, and hence the shape of society as a whole. PMID:25056625

  16. Influence of Reciprocal Links in Social Networks

    PubMed Central

    Zhu, Yu-Xiao; Zhang, Xiao-Guang; Sun, Gui-Quan; Tang, Ming; Zhou, Tao; Zhang, Zi-Ke

    2014-01-01

    How does reciprocal links affect the function of real social network? Does reciprocal link and non-reciprocal link play the same role? Previous researches haven't displayed a clear picture to us until now according to the best of our knowledge. Motivated by this, in this paper, we empirically study the influence of reciprocal links in two representative real datasets, Sina Weibo and Douban. Our results demonstrate that the reciprocal links play a more important role than non-reciprocal ones in information diffusion process. In particular, not only coverage but also the speed of the information diffusion can be significantly enhanced by considering the reciprocal effect. We give some possible explanations from the perspectives of network connectivity and efficiency. This work may shed some light on the in-depth understanding and application of the reciprocal effect in directed online social networks. PMID:25072242

  17. Social networks among Indigenous peoples in Mexico.

    PubMed

    Skoufias, Emmanuel; Lunde, Trine; Patrinos, Harry Anthony

    2010-01-01

    We examine the extent to which social networks among indigenous peoples in Mexico have a significant effect on a variety of human capital investment and economic activities, such as school attendance and work among teenage boys and girls, and migration, welfare participation, employment status, occupation, and sector of employment among adult males and females. Using data from the 10 percent population sample of the 2000 Population and Housing Census of Mexico and the empirical strategy that Bertrand, Luttmer, and Mullainathan (2000) propose, which allows us to take into account the role of municipality and language group fixed effects, we confirm empirically that social network effects play an important role in the economic decisions of indigenous people, especially in rural areas. Our analysis also provides evidence that better access to basic services such as water and electricity increases the size and strength of network effects in rural areas.

  18. Influence of reciprocal links in social networks.

    PubMed

    Zhu, Yu-Xiao; Zhang, Xiao-Guang; Sun, Gui-Quan; Tang, Ming; Zhou, Tao; Zhang, Zi-Ke

    2014-01-01

    How does reciprocal links affect the function of real social network? Does reciprocal link and non-reciprocal link play the same role? Previous researches haven't displayed a clear picture to us until now according to the best of our knowledge. Motivated by this, in this paper, we empirically study the influence of reciprocal links in two representative real datasets, Sina Weibo and Douban. Our results demonstrate that the reciprocal links play a more important role than non-reciprocal ones in information diffusion process. In particular, not only coverage but also the speed of the information diffusion can be significantly enhanced by considering the reciprocal effect. We give some possible explanations from the perspectives of network connectivity and efficiency. This work may shed some light on the in-depth understanding and application of the reciprocal effect in directed online social networks.

  19. Social Networks among Residents in Recovery Homes.

    PubMed

    Jason, Leonard; Stevens, Ed; Ferrari, Joseph R; Thompson, Erin; Legler, Ray

    2012-05-26

    Although evidence exists that substance abuse abstinence is enhanced when individuals in recovery are embedded in social networks that are cohesive, few studies examined the network structures underlying recovery home support systems. In two studies, we investigated the mechanisms through which social environments affect health outcomes among two samples of adult residents of recovery homes. Findings from Study 1 (n = 150) indicated that network size and the presence of relationships with other Oxford House (OH) residents both predicted future abstinence. Study 2 (n = 490) included individuals who lived in an OH residence for up to 6 months, and their personal relationship with other house residents predicted future abstinence. Implications of these findings are discussed.

  20. Social contagion theory: examining dynamic social networks and human behavior

    PubMed Central

    Christakis, Nicholas A.; Fowler, James H.

    2013-01-01

    Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a ‘three degrees of influence’ property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations. PMID:22711416

  1. Social contagion theory: examining dynamic social networks and human behavior.

    PubMed

    Christakis, Nicholas A; Fowler, James H

    2013-02-20

    Here, we review the research we have conducted on social contagion. We describe the methods we have employed (and the assumptions they have entailed) to examine several datasets with complementary strengths and weaknesses, including the Framingham Heart Study, the National Longitudinal Study of Adolescent Health, and other observational and experimental datasets that we and others have collected. We describe the regularities that led us to propose that human social networks may exhibit a 'three degrees of influence' property, and we review statistical approaches we have used to characterize interpersonal influence with respect to phenomena as diverse as obesity, smoking, cooperation, and happiness. We do not claim that this work is the final word, but we do believe that it provides some novel, informative, and stimulating evidence regarding social contagion in longitudinally followed networks. Along with other scholars, we are working to develop new methods for identifying causal effects using social network data, and we believe that this area is ripe for statistical development as current methods have known and often unavoidable limitations.

  2. Social encounter networks: characterizing Great Britain.

    PubMed

    Danon, Leon; Read, Jonathan M; House, Thomas A; Vernon, Matthew C; Keeling, Matt J

    2013-08-22

    A major goal of infectious disease epidemiology is to understand and predict the spread of infections within human populations, with the intention of better informing decisions regarding control and intervention. However, the development of fully mechanistic models of transmission requires a quantitative understanding of social interactions and collective properties of social networks. We performed a cross-sectional study of the social contacts on given days for more than 5000 respondents in England, Scotland and Wales, through postal and online survey methods. The survey was designed to elicit detailed and previously unreported measures of the immediate social network of participants relevant to infection spread. Here, we describe individual-level contact patterns, focusing on the range of heterogeneity observed and discuss the correlations between contact patterns and other socio-demographic factors. We find that the distribution of the number of contacts approximates a power-law distribution, but postulate that total contact time (which has a shorter-tailed distribution) is more epidemiologically relevant. We observe that children, public-sector and healthcare workers have the highest number of total contact hours and are therefore most likely to catch and transmit infectious disease. Our study also quantifies the transitive connections made between an individual's contacts (or clustering); this is a key structural characteristic of social networks with important implications for disease transmission and control efficacy. Respondents' networks exhibit high levels of clustering, which varies across social settings and increases with duration, frequency of contact and distance from home. Finally, we discuss the implications of these findings for the transmission and control of pathogens spread through close contact.

  3. The Social Network of a Burn Unit Team.

    PubMed

    Shoham, David A; Mundt, Marlon P; Gamelli, Richard L; McGaghie, William C

    2015-01-01

    Effective team communication is critical in complex settings like hospital intensive care units. A social network analysis study was conducted in a burn intensive care unit, assuming physicians and key members of the nursing and clinical staff would occupy the core of a robust communication network. Clinical team members reported the frequency (0 to 3+ times) of discussion about patient care issues with every other coworker during a 24-hour period. We calculated the proportion of clinical team members completing the questionnaire, created a map (sociogram) of the network, identified central positions, and searched for weak points. A total of 69 coworkers were listed by 48 clinical team members (70% completion rate). There were 626 connections (arcs) present (density = 13.3%). The analysis revealed three distinct sets of team members caring for two sets of patients. The five clinical team members most central to the network included three physicians, a social worker, and a dietitian. We established the feasibility of collecting social network data on this medical team and identified robust communication patterns. This study will be used to guide subsequent data collection from this and other clinical teams to correlate team structural features with clinical process outcomes and patient safety.

  4. Dynamic social network analysis using conversational dynamics in social networking and microblogging environments

    NASA Astrophysics Data System (ADS)

    Stocco, Gabriel; Savell, Robert; Cybenko, George

    2010-04-01

    In many security environments, the textual content of communications may be unavailable. In these instances, it is often desirable to infer the status of the network and its component entities from patterns of communication flow. Conversational dynamics among entities in the network may provide insight into important aspects of the underlying social network such as the formational dynamics of group structures, the active state of these groups, individuals' roles within groups, and the likelihood of individual participation in conversations. To gain insight into the use of conversational dynamics to facilitate Dynamic Social Network Analysis, we explore the use of interevent timings to associate entities in the Twitter social networking and micro-blogging environment. Specifically, we use message timings to establish inter-nodal relationships among participants. In addition, we demonstrate a new visualization technique for tracking levels of coordination or synchronization within the community via measures of socio-temporal coherence of the participants.

  5. Centrality properties of directed module members in social networks

    NASA Astrophysics Data System (ADS)

    Pollner, Péter; Palla, Gergely; Ábel, Dániel; Vicsek, András; Farkas, Illés J.; Derényi, Imre; Vicsek, Tamás

    2008-08-01

    Several recent studies of complex networks have suggested algorithms for locating network communities, also called modules or clusters, which are mostly defined as groups of nodes with dense internal connections. Along with the rapid development of these clustering techniques, the ability of revealing overlaps between communities has become very important as well. An efficient search technique for locating overlapping modules is provided by the Clique Percolation Method (CPM) and its extension to directed graphs, the CPMd algorithm. Here we investigate the centrality properties of directed module members in social networks obtained from e-mail exchanges and from sociometric questionnaires. Our results indicate that nodes in the overlaps between modules play a central role in the studied systems. Furthermore, the two different types of networks show interesting differences in the relation between the centrality measures and the role of the nodes in the directed modules.

  6. Realizing Wisdom Theory in Complex Learning Networks

    ERIC Educational Resources Information Center

    Kok, Ayse

    2009-01-01

    The word "wisdom" is rarely seen in contemporary technology and learning discourse. This conceptual paper aims to provide some clear principles that answer the question: How can we establish wisdom in complex learning networks? By considering the nature of contemporary calls for wisdom the paper provides a metatheoretial framework to evaluate the…

  7. Social networking profile correlates of schizotypy.

    PubMed

    Martin, Elizabeth A; Bailey, Drew H; Cicero, David C; Kerns, John G

    2012-12-30

    Social networking sites, such as Facebook, are extremely popular and have become a primary method for socialization and communication. Despite a report of increased use among those on the schizophrenia-spectrum, few details are known about their actual practices. In the current research, undergraduate participants completed measures of schizotypy and personality, and provided access to their Facebook profiles. Information from the profiles were then systematically coded and compared to the questionnaire data. As predicted, social anhedonia (SocAnh) was associated with a decrease in social participation variables, including a decrease in number of friends and number of photos, and an increase in length of time since communication with a friend, but SocAnh was also associated with an increase in profile length. Also, SocAnh was highly correlated with extraversion. Relatedly, extraversion uniquely predicted the number of friends and photos and length of time since communication with a friend. In addition, perceptual aberration/magical ideation (PerMag) was associated with an increased number of "black outs" on Facebook profile print-outs, a measure of paranoia. Overall, results from this naturalistic-like study show that SocAnh and extraversion are associated with decreased social participation and PerMag with increased paranoia related to information on social networking sites.

  8. Social networking profile correlates of schizotypy

    PubMed Central

    Martin, Elizabeth A.; Bailey, Drew H.; Cicero, David C.; Kerns, John G.

    2015-01-01

    Social networking sites, such as Facebook, are extremely popular and have become a primary method for socialization and communication. Despite a report of increased use among those on the schizophrenia-spectrum, few details are known about their actual practices. In the current research, undergraduate participants completed measures of schizotypy and personality, and provided access to their Facebook profiles. Information from the profiles were then systematically coded and compared to the questionnaire data. As predicted, social anhedonia (SocAnh) was associated with a decrease in social participation variables, including a decrease in number of friends and number of photos, and an increase in length of time since communication with a friend, but SocAnh was also associated with an increase in profile length. Also, SocAnh was highly correlated with extraversion. Relatedly, extraversion uniquely predicted the number of friends and photos and length of time since communication with a friend. In addition, perceptual aberration/magical ideation (PerMag) was associated with an increased number of “black outs” on Facebook profile print-outs, a measure of paranoia. Overall, results from this naturalistic-like study show that SocAnh and extraversion are associated with decreased social participation and PerMag with increased paranoia related to information on social networking sites. PMID:22796101

  9. Controlling extreme events on complex networks

    PubMed Central

    Chen, Yu-Zhong; Huang, Zi-Gang; Lai, Ying-Cheng

    2014-01-01

    Extreme events, a type of collective behavior in complex networked dynamical systems, often can have catastrophic consequences. To develop effective strategies to control extreme events is of fundamental importance and practical interest. Utilizing transportation dynamics on complex networks as a prototypical setting, we find that making the network “mobile” can effectively suppress extreme events. A striking, resonance-like phenomenon is uncovered, where an optimal degree of mobility exists for which the probability of extreme events is minimized. We derive an analytic theory to understand the mechanism of control at a detailed and quantitative level, and validate the theory numerically. Implications of our finding to current areas such as cybersecurity are discussed. PMID:25131344

  10. Amplitude dynamics favors synchronization in complex networks

    PubMed Central

    Gambuzza, Lucia Valentina; Gómez-Gardeñes, Jesus; Frasca, Mattia

    2016-01-01

    In this paper we study phase synchronization in random complex networks of coupled periodic oscillators. In particular, we show that, when amplitude dynamics is not negligible, phase synchronization may be enhanced. To illustrate this, we compare the behavior of heterogeneous units with both amplitude and phase dynamics and pure (Kuramoto) phase oscillators. We find that in small network motifs the behavior crucially depends on the topology and on the node frequency distribution. Surprisingly, the microscopic structures for which the amplitude dynamics improves synchronization are those that are statistically more abundant in random complex networks. Thus, amplitude dynamics leads to a general lowering of the synchronization threshold in arbitrary random topologies. Finally, we show that this synchronization enhancement is generic of oscillators close to Hopf bifurcations. To this aim we consider coupled FitzHugh-Nagumo units modeling neuron dynamics. PMID:27108847

  11. Amplitude dynamics favors synchronization in complex networks

    NASA Astrophysics Data System (ADS)

    Gambuzza, Lucia Valentina; Gómez-Gardeñes, Jesus; Frasca, Mattia

    2016-04-01

    In this paper we study phase synchronization in random complex networks of coupled periodic oscillators. In particular, we show that, when amplitude dynamics is not negligible, phase synchronization may be enhanced. To illustrate this, we compare the behavior of heterogeneous units with both amplitude and phase dynamics and pure (Kuramoto) phase oscillators. We find that in small network motifs the behavior crucially depends on the topology and on the node frequency distribution. Surprisingly, the microscopic structures for which the amplitude dynamics improves synchronization are those that are statistically more abundant in random complex networks. Thus, amplitude dynamics leads to a general lowering of the synchronization threshold in arbitrary random topologies. Finally, we show that this synchronization enhancement is generic of oscillators close to Hopf bifurcations. To this aim we consider coupled FitzHugh-Nagumo units modeling neuron dynamics.

  12. Emergence of fractal scaling in complex networks.

    PubMed

    Wei, Zong-Wen; Wang, Bing-Hong

    2016-09-01

    Some real-world networks are shown to be fractal or self-similar. It is widespread that such a phenomenon originates from the repulsion between hubs or disassortativity. Here we show that this common belief fails to capture the causality. Our key insight to address it is to pinpoint links critical to fractality. Those links with small edge betweenness centrality (BC) constitute a special architecture called fractal reference system, which gives birth to the fractal structure of those reported networks. In contrast, a small amount of links with high BC enable small-world effects, hiding the intrinsic fractality. With enough of such links removed, fractal scaling spontaneously arises from nonfractal networks. Our results provide a multiple-scale view on the structure and dynamics and place fractality as a generic organizing principle of complex networks on a firmer ground.

  13. Emergence of fractal scaling in complex networks

    NASA Astrophysics Data System (ADS)

    Wei, Zong-Wen; Wang, Bing-Hong

    2016-09-01

    Some real-world networks are shown to be fractal or self-similar. It is widespread that such a phenomenon originates from the repulsion between hubs or disassortativity. Here we show that this common belief fails to capture the causality. Our key insight to address it is to pinpoint links critical to fractality. Those links with small edge betweenness centrality (BC) constitute a special architecture called fractal reference system, which gives birth to the fractal structure of those reported networks. In contrast, a small amount of links with high BC enable small-world effects, hiding the intrinsic fractality. With enough of such links removed, fractal scaling spontaneously arises from nonfractal networks. Our results provide a multiple-scale view on the structure and dynamics and place fractality as a generic organizing principle of complex networks on a firmer ground.

  14. Mixed-method Exploration of Social Network Links to Participation

    PubMed Central

    Kreider, Consuelo M.; Bendixen, Roxanna M.; Mann, William C.; Young, Mary Ellen; McCarty, Christopher

    2015-01-01

    The people who regularly interact with an adolescent form that youth's social network, which may impact participation. We investigated the relationship of social networks to participation using personal network analysis and individual interviews. The sample included 36 youth, age 11 – 16 years. Nineteen had diagnoses of learning disability, attention disorder, or high-functioning autism and 17 were typically developing. Network analysis yielded 10 network variables, of which 8 measured network composition and 2 measured network structure, with significant links to at least one measure of participation using the Children's Assessment of Participation and Enjoyment (CAPE). Interviews from youth in the clinical group yielded description of strategies used to negotiate social interactions, as well as processes and reasoning used to remain engaged within social networks. Findings contribute to understanding the ways social networks are linked to youth participation and suggest the potential of social network factors for predicting rehabilitation outcomes. PMID:26594737

  15. Discovery of Information Diffusion Process in Social Networks

    NASA Astrophysics Data System (ADS)

    Kim, Kwanho; Jung, Jae-Yoon; Park, Jonghun

    Information diffusion analysis in social networks is of significance since it enables us to deeply understand dynamic social interactions among users. In this paper, we introduce approaches to discovering information diffusion process in social networks based on process mining. Process mining techniques are applied from three perspectives: social network analysis, process discovery and community recognition. We then present experimental results by using a real-life social network data. The proposed techniques are expected to employ as new analytical tools in online social networks such as blog and wikis for company marketers, politicians, news reporters and online writers.

  16. Spread of academic success in a high school social network.

    PubMed

    Blansky, Deanna; Kavanaugh, Christina; Boothroyd, Cara; Benson, Brianna; Gallagher, Julie; Endress, John; Sayama, Hiroki

    2013-01-01

    Application of social network analysis to education has revealed how social network positions of K-12 students correlate with their behavior and academic achievements. However, no study has been conducted on how their social network influences their academic progress over time. Here we investigated correlations between high school students' academic progress over one year and the social environment that surrounds them in their friendship network. We found that students whose friends' average GPA (Grade Point Average) was greater (or less) than their own had a higher tendency toward increasing (or decreasing) their academic ranking over time, indicating social contagion of academic success taking place in their social network.

  17. A Summary of Sociological Concepts Related to Social Network and Its Techniques for Quantifying Social Cohesion, Social Position, Social Distance

    DTIC Science & Technology

    2013-01-16

    the context of social networking sites , email communications, and virtual worlds, etc. They also, however, form indirect communities via the development...science. In the spring of 2011, access to social networking sites was largely hailed as a prime facilitator of individuals across North Africa and...at an astounding rate, particularly the use of social networking sites . The number of adult internet users in the United States doubled be- tween 2008

  18. A practical guide to social networks.

    PubMed

    Cross, Rob; Liedtka, Jeanne; Weiss, Leigh

    2005-03-01

    Saying that networks are important is stating the obvious. But harnessing the power of these seemingly invisible groups to achieve organizational goals is an elusive undertaking. Most efforts to promote collaboration are haphazard and built on the implicit philosophy that more connectivity is better. In truth, networks create relational demands that sap people's time and energy and can bog down entire organizations. It's crucial for executives to learn how to promote connectivity only where it benefits an organization or individual and to decrease unnecessary connections. In this article, the authors introduce three types of social networks, each of which delivers unique value. The customized response network excels at framing the ambiguous problems involved in innovation. Strategy consulting firms and new-product development groups rely on this format. By contrast, surgical teams and law firms rely mostly on the modular response network, which works best when components of the problem are known but the sequence of those components in the solution is unknown. And the routine response network is best suited for organizations like call centers, where the problems and solutions are fairly predictable but collaboration is still needed. Executives shouldn't simply hope that collaboration will spontaneously occur in the right places atthe right times in their organization. They need to develop a strategic, nuanced view of collaboration, and they must take steps to ensure that their companies support the types of social networks that best fit their goals. Drawing on examples from Novartis, the FAA, and Sallie Mae, the authors offer managers the tools they need to determine which network will deliver the best results for their organizations and which strategic investments will nurture the right degree of connectivity.

  19. Cooperative behavior cascades in human social networks

    PubMed Central

    Fowler, James H.; Christakis, Nicholas A.

    2010-01-01

    Theoretical models suggest that social networks influence the evolution of cooperation, but to date there have been few experimental studies. Observational data suggest that a wide variety of behaviors may spread in human social networks, but subjects in such studies can choose to befriend people with similar behaviors, posing difficulty for causal inference. Here, we exploit a seminal set of laboratory experiments that originally showed that voluntary costly punishment can help sustain cooperation. In these experiments, subjects were randomly assigned to a sequence of different groups to play a series of single-shot public goods games with strangers; this feature allowed us to draw networks of interactions to explore how cooperative and uncooperative behaviors spread from person to person to person. We show that, in both an ordinary public goods game and in a public goods game with punishment, focal individuals are influenced by fellow group members’ contribution behavior in future interactions with other individuals who were not a party to the initial interaction. Furthermore, this influence persists for multiple periods and spreads up to three degrees of separation (from person to person to person to person). The results suggest that each additional contribution a subject makes to the public good in the first period is tripled over the course of the experiment by other subjects who are directly or indirectly influenced to contribute more as a consequence. These results show experimentally that cooperative behavior cascades in human social networks. PMID:20212120

  20. Unfavorable Individuals in Social Gaming Networks.

    PubMed

    Zhang, Yichao; Chen, Guanrong; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

    2015-12-09

    In social gaming networks, the current research focus has been on the origin of widespread reciprocal behaviors when individuals play non-cooperative games. In this paper, we investigate the topological properties of unfavorable individuals in evolutionary games. The unfavorable individuals are defined as the individuals gaining the lowest average payoff in a round of game. Since the average payoff is normally considered as a measure of fitness, the unfavorable individuals are very likely to be eliminated or change their strategy updating rules from a Darwinian perspective. Considering that humans can hardly adopt a unified strategy to play with their neighbors, we propose a divide-and-conquer game model, where individuals can interact with their neighbors in the network with appropriate strategies. We test and compare a series of highly rational strategy updating rules. In the tested scenarios, our analytical and simulation results surprisingly reveal that the less-connected individuals in degree-heterogeneous networks are more likely to become the unfavorable individuals. Our finding suggests that the connectivity of individuals as a social capital fundamentally changes the gaming environment. Our model, therefore, provides a theoretical framework for further understanding the social gaming networks.