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Sample records for complex social networks

  1. 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. PMID:20798616

  2. Disease Surveillance on Complex Social Networks.

    PubMed

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

    2016-07-01

    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

  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. Bidirectional selection between two classes in complex social networks

    PubMed Central

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

    2014-01-01

    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. PMID:25524835

  5. Bidirectional selection between two classes in complex social networks

    NASA Astrophysics Data System (ADS)

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

    2014-12-01

    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. Social-network complexity in humans is associated with the neural response to social information.

    PubMed

    Dziura, Sarah L; Thompson, James C

    2014-11-01

    Humans have evolved to thrive in large and complex social groups, and it is likely that this increase in group complexity has come with a greater need to decode and respond to complex and uncertain communicatory signals. In this functional MRI study, we examined whether complexity of social networks in humans is related to the functioning of brain regions key to the perception of basic, nonverbal social stimuli. Greater activation to biological than to scrambled motion in the right posterior superior temporal sulcus (pSTS) and right amygdala were positively correlated with the diversity of social-network roles. In the pSTS, in particular, this association was not due to a relationship between network diversity and network size. These findings suggest that increased functioning of brain regions involved in decoding social signals might facilitate the detection and decoding of subtle signals encountered in varied social settings. PMID:25253279

  9. Rumor spreading model considering hesitating mechanism in complex social networks

    NASA Astrophysics Data System (ADS)

    Xia, Ling-Ling; Jiang, Guo-Ping; Song, Bo; Song, Yu-Rong

    2015-11-01

    The study of rumor spreading has become an important issue on complex social networks. On the basis of prior studies, we propose a modified ​susceptible-exposed-infected-removed (SEIR) model with hesitating mechanism by considering the attractiveness and fuzziness of the content of rumors. We derive mean-field equations to characterize the dynamics of SEIR model on both homogeneous and heterogeneous networks. Then a steady-state analysis is conducted to investigate the spreading threshold and the final rumor size. Simulations on both artificial and real networks show that a decrease of fuzziness can effectively increase the spreading threshold of the SEIR model and reduce the maximum rumor influence. In addition, the spreading threshold is independent of the attractiveness of rumor. Simulation results also show that the speed of rumor spreading obeys the relation "BA network > WS network", whereas the final scale of spreading obeys the opposite relation.

  10. Layered Social Network Analysis Reveals Complex Relationships in Kindergarteners.

    PubMed

    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

  11. 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

  12. 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. PMID:25863975

  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. 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.

  15. 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.

  16. 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. PMID:22317253

  17. 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…

  18. Visualization of Social Networks

    NASA Astrophysics Data System (ADS)

    Chen, Ing-Xiang; Yang, Cheng-Zen

    With the ubiquitous characteristic of the Internet, today many online social environments are provided to connect people. Various social relationships are thus created, connected, and migrated from our real lives to the Internet environment from different social groups. Many social communities and relationships are also quickly constructed and connected via instant personal messengers, blogs, Twitter, Facebook, and a great variety of online social services. Since social network visualizations can structure the complex relationships between different groups of individuals or organizations, they are helpful to analyze the social activities and relationships of actors, particularly over a large number of nodes. Therefore, many studies and visualization tools have been investigated to present social networks with graph representations. In this chapter, we will first review the background of social network analysis and visualization methods, and then introduce various novel visualization applications for social networks. Finally, the challenges and the future development of visualizing online social networks are discussed.

  19. Propagation, cascades, and agreement dynamics in complex communication and social networks

    NASA Astrophysics Data System (ADS)

    Lu, Qiming

    Many modern and important technological, social, information and infrastructure systems can be viewed as complex systems with a large number of interacting components. Models of complex networks and dynamical interactions, as well as their applications are of fundamental interests in many aspects. Here, several stylized models of multiplex propagation and opinion dynamics are investigated on complex and empirical social networks. We first investigate cascade dynamics in threshold-controlled (multiplex) propagation on random geometric networks. We find that such local dynamics can serve as an efficient, robust, and reliable prototypical activation protocol in sensor networks in responding to various alarm scenarios. We also consider the same dynamics on a modified network by adding a few long-range communication links, resulting in a small-world network. We find that such construction can further enhance and optimize the speed of the network's response, while keeping energy consumption at a manageable level. We also investigate a prototypical agent-based model, the Naming Game, on two-dimensional random geometric networks. The Naming Game [A. Baronchelli et al., J. Stat. Mech.: Theory Exp. (2006) P06014.] is a minimal model, employing local communications that captures the emergence of shared communication schemes (languages) in a population of autonomous semiotic agents. Implementing the Naming Games with local broadcasts on random geometric graphs, serves as a model for agreement dynamics in large-scale, autonomously operating wireless sensor networks. Further, it captures essential features of the scaling properties of the agreement process for spatially-embedded autonomous agents. Among the relevant observables capturing the temporal properties of the agreement process, we investigate the cluster-size distribution and the distribution of the agreement times, both exhibiting dynamic scaling. We also present results for the case when a small density of long

  20. Inhibiting diffusion of complex contagions in social networks: theoretical and experimental results

    PubMed Central

    Anil Kumar, V.S.; Marathe, Madhav V.; Ravi, S.S.; Rosenkrantz, Daniel J.

    2014-01-01

    We consider the problem of inhibiting undesirable contagions (e.g. rumors, spread of mob behavior) in social networks. Much of the work in this context has been carried out under the 1-threshold model, where diffusion occurs when a node has just one neighbor with the contagion. We study the problem of inhibiting more complex contagions in social networks where nodes may have thresholds larger than 1. The goal is to minimize the propagation of the contagion by removing a small number of nodes (called critical nodes) from the network. We study several versions of this problem and prove that, in general, they cannot even be efficiently approximated to within any factor ρ ≥ 1, unless P = NP. We develop efficient and practical heuristics for these problems and carry out an experimental study of their performance on three well known social networks, namely epinions, wikipedia and slashdot. Our results show that these heuristics perform significantly better than five other known methods. We also establish an efficiently computable upper bound on the number of nodes to which a contagion can spread and evaluate this bound on many real and synthetic networks. PMID:25750583

  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

    NASA Astrophysics Data System (ADS)

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

    2015-05-01

    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.

  3. 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

  4. 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.

  5. Synchronization in complex networks

    NASA Astrophysics Data System (ADS)

    Arenas, Alex; Díaz-Guilera, Albert; Kurths, Jurgen; Moreno, Yamir; Zhou, Changsong

    2008-12-01

    Synchronization processes in populations of locally interacting elements are the focus of intense research in physical, biological, chemical, technological and social systems. The many efforts devoted to understanding synchronization phenomena in natural systems now take 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 take an overview of the new emergent features coming out from the interplay between the structure and the function of the underlying patterns 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.

  6. 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. PMID:22984533

  7. 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

  8. 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

  9. 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…

  10. 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.

  11. The Analysis of Social Networks

    PubMed Central

    O’Malley, A. James; Marsden, Peter V.

    2009-01-01

    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. PMID:20046802

  12. 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

  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. 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.

  15. Local adaptive mechanism and hierarchic social entropy in opinion formation on complex networks

    NASA Astrophysics Data System (ADS)

    Cheng, Jie; Hu, Yanqing; Di, Zengru; Fan, Ying

    2010-10-01

    In this paper we study the opinion formation using co-evolution model, in which network's structure interacts with the nodes' opinion. A local adaptive model is proposed to investigate the effects of local information on the opinion formation, including local rewiring and influencing mechanism. The results show that under the local adaptive mechanism, systems could reach steady state of consensus or fragmentation. Considering the local influencing factor only, we find that transition occurs under proper condition and local parameter affects the transition point. At last, the diversity of opinions is considered, and hierarchic social entropy is used as a macroscopic measurement which is proved to be well.

  16. 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…

  17. 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

    2012-08-01

    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. Complex networks: Patterns of complexity

    NASA Astrophysics Data System (ADS)

    Pastor-Satorras, Romualdo; Vespignani, Alessandro

    2010-07-01

    The Turing mechanism provides a paradigm for the spontaneous generation of patterns in reaction-diffusion systems. A framework that describes Turing-pattern formation in the context of complex networks should provide a new basis for studying the phenomenon.

  19. 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

  20. Coupled adaptive complex networks

    NASA Astrophysics Data System (ADS)

    Shai, S.; Dobson, S.

    2013-04-01

    Adaptive networks, which combine topological evolution of the network with dynamics on the network, are ubiquitous across disciplines. Examples include technical distribution networks such as road networks and the internet, natural and biological networks, and social science networks. These networks often interact with or depend upon other networks, resulting in coupled adaptive networks. In this paper we study susceptible-infected-susceptible (SIS) epidemic dynamics on coupled adaptive networks, where susceptible nodes are able to avoid contact with infected nodes by rewiring their intranetwork connections. However, infected nodes can pass the disease through internetwork connections, which do not change with time: The dependencies between the coupled networks remain constant. We develop an analytical formalism for these systems and validate it using extensive numerical simulation. We find that stability is increased by increasing the number of internetwork links, in the sense that the range of parameters over which both endemic and healthy states coexist (both states are reachable depending on the initial conditions) becomes smaller. Finally, we find a new stable state that does not appear in the case of a single adaptive network but only in the case of weakly coupled networks, in which the infection is endemic in one network but neither becomes endemic nor dies out in the other. Instead, it persists only at the nodes that are coupled to nodes in the other network through internetwork links. We speculate on the implications of these findings.

  1. Decentralized Online Social Networks

    NASA Astrophysics Data System (ADS)

    Datta, Anwitaman; Buchegger, Sonja; Vu, Le-Hung; Strufe, Thorsten; Rzadca, Krzysztof

    Current Online social networks (OSN) are web services run on logically centralized infrastructure. Large OSN sites use content distribution networks and thus distribute some of the load by caching for performance reasons, nevertheless there is a central repository for user and application data. This centralized nature of OSNs has several drawbacks including scalability, privacy, dependence on a provider, need for being online for every transaction, and a lack of locality. There have thus been several efforts toward decentralizing OSNs while retaining the functionalities offered by centralized OSNs. A decentralized online social network (DOSN) is a distributed system for social networking with no or limited dependency on any dedicated central infrastructure. In this chapter we explore the various motivations of a decentralized approach to online social networking, discuss several concrete proposals and types of DOSN as well as challenges and opportunities associated with decentralization.

  2. Microbial "social networks"

    PubMed Central

    2015-01-01

    Background It is well understood that distinct communities of bacteria are present at different sites of the body, and that changes in the structure of these communities have strong implications for human health. Yet, challenges remain in understanding the complex interconnections between the bacterial taxa within these microbial communities and how they change during the progression of diseases. Many recent studies attempt to analyze the human microbiome using traditional ecological measures and cataloging differences in bacterial community membership. In this paper, we show how to push metagenomic analyses beyond mundane questions related to the bacterial taxonomic profiles that differentiate one sample from another. Methods We develop tools and techniques that help us to investigate the nature of social interactions in microbial communities, and demonstrate ways of compactly capturing extensive information about these networks and visually conveying them in an effective manner. We define the concept of bacterial "social clubs", which are groups of taxa that tend to appear together in many samples. More importantly, we define the concept of "rival clubs", entire groups that tend to avoid occurring together in many samples. We show how to efficiently compute social clubs and rival clubs and demonstrate their utility with the help of examples including a smokers' dataset and a dataset from the Human Microbiome Project (HMP). Results The tools developed provide a framework for analyzing relationships between bacterial taxa modeled as bacterial co-occurrence networks. The computational techniques also provide a framework for identifying clubs and rival clubs and for studying differences in the microbiomes (and their interactions) of two or more collections of samples. Conclusions Microbial relationships are similar to those found in social networks. In this work, we assume that strong (positive or negative) tendencies to co-occur or co-infect is likely to have

  3. 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.

  4. Immunization of complex networks

    NASA Astrophysics Data System (ADS)

    Pastor-Satorras, Romualdo; Vespignani, Alessandro

    2002-03-01

    Complex networks such as the sexual partnership web or the Internet often show a high degree of redundancy and heterogeneity in their connectivity properties. This peculiar connectivity provides an ideal environment for the spreading of infective agents. Here we show that the random uniform immunization of individuals does not lead to the eradication of infections in all complex networks. Namely, networks with scale-free properties do not acquire global immunity from major epidemic outbreaks even in the presence of unrealistically high densities of randomly immunized individuals. The absence of any critical immunization threshold is due to the unbounded connectivity fluctuations of scale-free networks. Successful immunization strategies can be developed only by taking into account the inhomogeneous connectivity properties of scale-free networks. In particular, targeted immunization schemes, based on the nodes' connectivity hierarchy, sharply lower the network's vulnerability to epidemic attacks.

  5. [Social networks and medicine].

    PubMed

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

    2015-11-01

    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. PMID:26685647

  6. Information Horizons in Complex Networks

    NASA Astrophysics Data System (ADS)

    Sneppen, Kim

    2005-03-01

    We investigate how the structure constrain specific communication in social-, man-made and biological networks. We find that human networks of governance and collaboration are predictable on teat-a-teat level, reflecting well defined pathways, but globally inefficient (1). In contrast, the Internet tends to have better overall communication abilities, more alternative pathways, and is therefore more robust. Between these extremes are the molecular network of living organisms. Further, for most real world networks we find that communication ability is favored by topology on small distances, but disfavored at larger distances (2,3,4). We discuss the topological implications in terms of modularity and the positioning of hubs in the networks (5,6). Finally we introduce some simple models which demonstarte how communication may shape the structure of in particular man made networks (7,8). 1) K. Sneppen, A. Trusina, M. Rosvall (2004). Hide and seek on complex networks [cond-mat/0407055] 2) M. Rosvall, A. Trusina, P. Minnhagen and K. Sneppen (2004). Networks and Cities: An Information Perspective [cond-mat/0407054]. In PRL. 3) A. Trusina, M. Rosvall, K. Sneppen (2004). Information Horizons in Networks. [cond-mat/0412064] 4) M. Rosvall, P. Minnhagen, K. Sneppen (2004). Navigating Networks with Limited Information. [cond-mat/0412051] 5) S. Maslov and K. Sneppen (2002). Specificity and stability in topology of protein networks Science 296, 910-913 [cond-mat/0205380]. 6) A. Trusina, S. Maslov, P. Minnhagen, K. Sneppen Hierarchy Measures in Complex Networks. Phys. Rev. Lett. 92, 178702 [cond-mat/0308339]. 7) M. Rosvall and K. Sneppen (2003). Modeling Dynamics of Information Networks. Phys. Rev. Lett. 91, 178701 [cond-mat/0308399]. 8) B-J. Kim, A. Trusina, P. Minnhagen, K. Sneppen (2003). Self Organized Scale-Free Networks from Merging and Regeneration. nlin.AO/0403006. In European Journal of Physics.

  7. Social Networks and Health.

    PubMed

    Perdiaris, Christos; Chardalias, Konstantinos; Magita, Andrianna; Mechili, Aggelos E; Diomidous, Marianna

    2015-01-01

    Nowadays the social networks have been developed into an advanced communications tool, which is important for all people to contact each other. These specific networks do offer lots of options as well as plenty of advantages and disadvantages. The social websites are many in number and titles, such as the facebook, the twitter, the bandoo etc. One of the most important function-mechanisms for the social network websites, are the marketing tools. The future goal is suggested to be the evolution of these programs. The development of these applications, which is going to lead into a new era for the social digital communication between the internet users, all around the globe. PMID:26153011

  8. 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.

  9. Controllability of Complex Networks

    NASA Astrophysics Data System (ADS)

    Liu, Yang; Slotine, Jean-Jacques; Barabasi, Albert-Laszlo

    2011-03-01

    The ultimate proof of our understanding of natural or technological systems is reflected in our ability to control them. While control theory offers mathematical tools to steer engineered systems towards a desired state, we lack a general framework to control complex self-organized systems, like the regulatory network of a cell or the Internet. Here we develop analytical tools to study the controllability of an arbitrary complex directed network, identifying the set of driver nodes whose time-dependent control can guide the system's dynamics. We apply these tools to real and model networks, finding that sparse inhomogeneous networks, which emerge in many real complex systems, are the most difficult to control. In contrast, dense and homogeneous networks can be controlled via a few driver nodes. Counterintuitively, we find that in both model and real systems the driver nodes tend to avoid the hubs. We show that the robustness of control to link failure is determined by a core percolation problem, helping us understand why many complex systems are relatively insensitive to link deletion. The developed approach offers a framework to address the controllability of an arbitrary network, representing a key step towards the eventual control of complex systems.

  10. 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.

  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. 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. PMID:19492691

  13. 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.

  14. 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.

  15. Bridge and brick motifs in complex networks

    NASA Astrophysics Data System (ADS)

    Huang, Chung-Yuan; Sun, Chuen-Tsai; Cheng, Chia-Ying; Hsieh, Ji-Lung

    2007-04-01

    Acknowledging the expanding role of complex networks in numerous scientific contexts, we examine significant functional and topological differences between bridge and brick motifs for predicting network behaviors and functions. After observing similarities between social networks and their genetic, ecological, and engineering counterparts, we identify a larger number of brick motifs in social networks and bridge motifs in the other three types. We conclude that bridge and brick motif content analysis can assist researchers in understanding the small-world and clustering properties of network structures when investigating network functions and behaviors.

  16. Order, chaos and complexity in landscape evolution: insights from systems theory and social network theory

    NASA Astrophysics Data System (ADS)

    van de Wiel, Marco

    2010-05-01

    Any physical system can exist in one of three possible states: ordered, chaotic or complex. A recently developed hypothesis relates the occurrence of these states to 1) the variability of the external conditions to which the system is subjected; and 2) the internal differentiation within the system to accommodate variations in the external conditions. This paper attempts to apply these concepts to landscapes as geomorphic systems, responding to external forces such as climate change. State variations in a geomorphic system can be observed from spatio-temporal variations in topography and morphology of a landscape, or can be inferred from temporal variations in major geomorphic metrics, such as average erosion rate or total sediment yield. Here, a numeric landscape evolution model is used to explore state variations of an idealized catchment in the context of the variability of an external signal (i.e. climate) and the internal differentiation of the catchment's geomorphic characteristics (i.e. elevation and sediment distribution).

  17. Statistical mechanics of complex networks

    NASA Astrophysics Data System (ADS)

    Waclaw, B.

    2007-04-01

    The science of complex networks is a new interdisciplinary branch of science which has arisen recently on the interface of physics, biology, social and computer sciences, and others. Its main goal is to discover general laws governing the creation and growth as well as processes taking place on networks, like e.g. the Internet, transportation or neural networks. It turned out that most real-world networks cannot be simply reduced to a compound of some individual components. Fortunately, the statistical mechanics, being one of pillars of modern physics, provides us with a very powerful set of tools and methods for describing and understanding these systems. In this thesis, we would like to present a consistent approach to complex networks based on statistical mechanics, with the central role played by the concept of statistical ensemble of networks. We show how to construct such a theory and present some practical problems where it can be applied. Among them, we pay attention to the problem of finite-size corrections and the dynamics of a simple model of mass transport on networks.

  18. 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.

  19. 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. PMID:27120815

  20. 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…

  1. 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.

  2. Higher-order organization of complex networks.

    PubMed

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

    2016-07-01

    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

  3. 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.

  4. 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

  5. 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

  6. 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.

  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. 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.

  9. 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." PMID:21554117

  10. 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…

  11. 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. PMID:23679650

  12. 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.

  13. 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.

  14. Fundamental structures of dynamic social networks.

    PubMed

    Sekara, Vedran; Stopczynski, Arkadiusz; Lehmann, Sune

    2016-09-01

    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

  15. 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…

  16. 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.

  17. Attack vulnerability of complex networks

    NASA Astrophysics Data System (ADS)

    Holme, Petter; Kim, Beom Jun; Yoon, Chang No; Han, Seung Kee

    2002-05-01

    We study the response of complex networks subject to attacks on vertices and edges. Several existing complex network models as well as real-world networks of scientific collaborations and Internet traffic are numerically investigated, and the network performance is quantitatively measured by the average inverse geodesic length and the size of the largest connected subgraph. For each case of attacks on vertices and edges, four different attacking strategies are used: removals by the descending order of the degree and the betweenness centrality, calculated for either the initial network or the current network during the removal procedure. It is found that the removals by the recalculated degrees and betweenness centralities are often more harmful than the attack strategies based on the initial network, suggesting that the network structure changes as important vertices or edges are removed. Furthermore, the correlation between the betweenness centrality and the degree in complex networks is studied.

  18. 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…

  19. 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…

  20. 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

  1. Statistical mechanics of complex networks

    NASA Astrophysics Data System (ADS)

    Albert, Réka; Barabási, Albert-László

    2002-01-01

    Complex networks describe a wide range of systems in nature and society. Frequently cited examples include the cell, a network of chemicals linked by chemical reactions, and the Internet, a network of routers and computers connected by physical links. While traditionally these systems have been modeled as random graphs, it is increasingly recognized that the topology and evolution of real networks are governed by robust organizing principles. This article reviews the recent advances in the field of complex networks, focusing on the statistical mechanics of network topology and dynamics. After reviewing the empirical data that motivated the recent interest in networks, the authors discuss the main models and analytical tools, covering random graphs, small-world and scale-free networks, the emerging theory of evolving networks, and the interplay between topology and the network's robustness against failures and attacks.

  2. Entropy of dynamical social networks

    NASA Astrophysics Data System (ADS)

    Zhao, Kun; Karsai, Marton; Bianconi, Ginestra

    2012-02-01

    Dynamical social networks are evolving rapidly and are highly adaptive. Characterizing the information encoded in social networks is essential to gain insight into the structure, evolution, adaptability and dynamics. Recently entropy measures have been used to quantify the information in email correspondence, static networks and mobility patterns. Nevertheless, we still lack methods to quantify the information encoded in time-varying dynamical social networks. In this talk we present a model to quantify the entropy of dynamical social networks and use this model to analyze the data of phone-call communication. We show evidence that the entropy of the phone-call interaction network changes according to circadian rhythms. Moreover we show that social networks are extremely adaptive and are modified by the use of technologies such as mobile phone communication. Indeed the statistics of duration of phone-call is described by a Weibull distribution and is significantly different from the distribution of duration of face-to-face interactions in a conference. Finally we investigate how much the entropy of dynamical social networks changes in realistic models of phone-call or face-to face interactions characterizing in this way different type human social behavior.

  3. The price of complexity in financial networks.

    PubMed

    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. PMID:27555583

  4. 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.

  5. 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.

  6. Effective Augmentation of Complex Networks.

    PubMed

    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

  7. 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

  8. 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

  9. 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.

  10. 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

  11. 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.

  12. 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,…

  13. The architecture of complex weighted networks

    PubMed Central

    Barrat, A.; Barthélemy, M.; Pastor-Satorras, R.; Vespignani, A.

    2004-01-01

    Networked structures arise in a wide array of different contexts such as technological and transportation infrastructures, social phenomena, and biological systems. These highly interconnected systems have recently been the focus of a great deal of attention that has uncovered and characterized their topological complexity. Along with a complex topological structure, real networks display a large heterogeneity in the capacity and intensity of the connections. These features, however, have mainly not been considered in past studies where links are usually represented as binary states, i.e., either present or absent. Here, we study the scientific collaboration network and the world-wide air-transportation network, which are representative examples of social and large infrastructure systems, respectively. In both cases it is possible to assign to each edge of the graph a weight proportional to the intensity or capacity of the connections among the various elements of the network. We define appropriate metrics combining weighted and topological observables that enable us to characterize the complex statistical properties and heterogeneity of the actual strength of edges and vertices. This information allows us to investigate the correlations among weighted quantities and the underlying topological structure of the network. These results provide a better description of the hierarchies and organizational principles at the basis of the architecture of weighted networks. PMID:15007165

  14. Topological Strata of Weighted Complex Networks

    PubMed Central

    Petri, Giovanni; Scolamiero, Martina; Donato, Irene; Vaccarino, Francesco

    2013-01-01

    The statistical mechanical approach to complex networks is the dominant paradigm in describing natural and societal complex systems. The study of network properties, and their implications on dynamical processes, mostly focus on locally defined quantities of nodes and edges, such as node degrees, edge weights and –more recently– correlations between neighboring nodes. However, statistical methods quickly become cumbersome when dealing with many-body properties and do not capture the precise mesoscopic structure of complex networks. Here we introduce a novel method, based on persistent homology, to detect particular non-local structures, akin to weighted holes within the link-weight network fabric, which are invisible to existing methods. Their properties divide weighted networks in two broad classes: one is characterized by small hierarchically nested holes, while the second displays larger and longer living inhomogeneities. These classes cannot be reduced to known local or quasilocal network properties, because of the intrinsic non-locality of homological properties, and thus yield a new classification built on high order coordination patterns. Our results show that topology can provide novel insights relevant for many-body interactions in social and spatial networks. Moreover, this new method creates the first bridge between network theory and algebraic topology, which will allow to import the toolset of algebraic methods to complex systems. PMID:23805226

  15. Topological Strata of Weighted Complex Networks.

    PubMed

    Petri, Giovanni; Scolamiero, Martina; Donato, Irene; Vaccarino, Francesco

    2013-01-01

    The statistical mechanical approach to complex networks is the dominant paradigm in describing natural and societal complex systems. The study of network properties, and their implications on dynamical processes, mostly focus on locally defined quantities of nodes and edges, such as node degrees, edge weights and -more recently- correlations between neighboring nodes. However, statistical methods quickly become cumbersome when dealing with many-body properties and do not capture the precise mesoscopic structure of complex networks. Here we introduce a novel method, based on persistent homology, to detect particular non-local structures, akin to weighted holes within the link-weight network fabric, which are invisible to existing methods. Their properties divide weighted networks in two broad classes: one is characterized by small hierarchically nested holes, while the second displays larger and longer living inhomogeneities. These classes cannot be reduced to known local or quasilocal network properties, because of the intrinsic non-locality of homological properties, and thus yield a new classification built on high order coordination patterns. Our results show that topology can provide novel insights relevant for many-body interactions in social and spatial networks. Moreover, this new method creates the first bridge between network theory and algebraic topology, which will allow to import the toolset of algebraic methods to complex systems. PMID:23805226

  16. 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.

  17. Contagion on complex networks with persuasion.

    PubMed

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

    2016-01-01

    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. PMID:27029498

  18. Contagion on complex networks with persuasion

    PubMed Central

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

    2016-01-01

    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. PMID:27029498

  19. 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.

  20. Sampling from complex networks using distributed learning automata

    NASA Astrophysics Data System (ADS)

    Rezvanian, Alireza; Rahmati, Mohammad; Meybodi, Mohammad Reza

    2014-02-01

    A complex network provides a framework for modeling many real-world phenomena in the form of a network. In general, a complex network is considered as a graph of real world phenomena such as biological networks, ecological networks, technological networks, information networks and particularly social networks. Recently, major studies are reported for the characterization of social networks due to a growing trend in analysis of online social networks as dynamic complex large-scale graphs. Due to the large scale and limited access of real networks, the network model is characterized using an appropriate part of a network by sampling approaches. In this paper, a new sampling algorithm based on distributed learning automata has been proposed for sampling from complex networks. In the proposed algorithm, a set of distributed learning automata cooperate with each other in order to take appropriate samples from the given network. To investigate the performance of the proposed algorithm, several simulation experiments are conducted on well-known complex networks. Experimental results are compared with several sampling methods in terms of different measures. The experimental results demonstrate the superiority of the proposed algorithm over the others.

  1. 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…

  2. Stressing out the Social Network.

    PubMed

    Kirkby, Lowry A; Sohal, Vikaas S

    2016-07-20

    In this issue of Neuron, Hultman et al. (2016) find that stress-induced abnormal social behavior reflects aberrant prefrontal regulation of downstream limbic networks. This illustrates how linking aberrant network dynamics to neuropsychiatric disorders may lead to new circuit-based therapeutic interventions. PMID:27477012

  3. 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.

  4. Quantization Effects on Complex Networks.

    PubMed

    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

  5. Quantization Effects on Complex Networks

    NASA Astrophysics Data System (ADS)

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

    2016-05-01

    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.

  6. 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

  7. Core percolation on complex networks.

    PubMed

    Liu, Yang-Yu; Csóka, Endre; Zhou, Haijun; Pósfai, Márton

    2012-11-16

    We analytically solve the core percolation problem for complex networks with arbitrary degree distributions. We find that purely scale-free networks have no core for any degree exponents. We show that for undirected networks if core percolation occurs then it is continuous while for directed networks it is discontinuous (and hybrid) if the in- and out-degree distributions differ. We also find that core percolations on undirected and directed networks have completely different critical exponents associated with their critical singularities. PMID:23215509

  8. 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.

  9. 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.

  10. 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.

  11. 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.

  12. Assortative model for social networks.

    PubMed

    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. PMID:15524673

  13. 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.

  14. 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.

  15. 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

  16. Social networks and environmental outcomes.

    PubMed

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

    2016-06-01

    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

  17. Wealth distribution on complex networks

    NASA Astrophysics Data System (ADS)

    Ichinomiya, Takashi

    2012-12-01

    We study the wealth distribution of the Bouchaud-Mézard model on complex networks. It is known from numerical simulations that this distribution depends on the topology of the network; however, no one has succeeded in explaining it. Using “adiabatic” and “independent” assumptions along with the central-limit theorem, we derive equations that determine the probability distribution function. The results are compared to those of simulations for various networks. We find good agreement between our theory and the simulations, except for the case of Watts-Strogatz networks with a low rewiring rate due to the breakdown of independent assumption.

  18. Griffiths Phases on Complex Networks

    NASA Astrophysics Data System (ADS)

    Muñoz, Miguel A.; Juhász, Róbert; Castellano, Claudio; Ódor, Géza

    2010-09-01

    Quenched disorder is known to play a relevant role in dynamical processes and phase transitions. Its effects on the dynamics of complex networks have hardly been studied. Aimed at filling this gap, we analyze the contact process, i.e., the simplest propagation model, with quenched disorder on complex networks. We find Griffiths phases and other rare-region effects, leading rather generically to anomalously slow (algebraic, logarithmic, …) relaxation, on Erdős-Rényi networks. Similar effects are predicted to exist for other topologies with a finite percolation threshold. More surprisingly, we find that Griffiths phases can also emerge in the absence of quenched disorder, as a consequence of topological heterogeneity in networks with finite topological dimension. These results have a broad spectrum of implications for propagation phenomena and other dynamical processes on networks.

  19. 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

  20. Measuring distances between complex networks

    NASA Astrophysics Data System (ADS)

    Andrade, Roberto F. S.; Miranda, José G. V.; Pinho, Suani T. R.; Lobão, Thierry Petit

    2008-08-01

    A previously introduced concept of higher order neighborhoods in complex networks, [R.F.S. Andrade, J.G.V. Miranda, T.P. Lobão, Phys. Rev. E 73 (2006) 046101] is used to define a distance between networks with the same number of nodes. With such measure, expressed in terms of the matrix elements of the neighborhood matrices of each network, it is possible to compare, in a quantitative way, how far apart in the space of neighborhood matrices two networks are. The distance between these matrices depends on both the network topologies and the adopted node numberings. While the numbering of one network is fixed, a Monte Carlo algorithm is used to find the best numbering of the other network, in the sense that it minimizes the distance between the matrices. The minimal value found for the distance reflects differences in the neighborhood structures of the two networks that arise only from distinct topologies. This procedure ends up by providing a projection of the first network on the pattern of the second one. Examples are worked out allowing for a quantitative comparison for distances among distinct networks, as well as among distinct realizations of random networks.

  1. 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.

  2. 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

  3. 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.

  4. Control efficacy of complex networks.

    PubMed

    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

  5. 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.

  6. 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.

  7. The Ultimatum Game in complex networks

    NASA Astrophysics Data System (ADS)

    Sinatra, R.; Iranzo, J.; Gómez-Gardeñes, J.; Floría, L. M.; Latora, V.; Moreno, Y.

    2009-09-01

    We address the problem of how cooperative (altruistic-like) behavior arises in natural and social systems by analyzing an Ultimatum Game in complex networks. Specifically, players of three types are considered: (a) empathetic, whose aspiration levels, and offers, are equal, (b) pragmatic, who do not distinguish between the different roles and aim to obtain the same benefit, and (c) agents whose aspiration levels, and offers, are independent. We analyze the asymptotic behavior of pure populations with different topologies using two kinds of strategic update rules: natural selection, which relies on replicator dynamics, and social penalty, inspired by the Bak-Sneppen dynamics, in which players are subject to a social selection rule penalizing not only the less fit individuals, but also their first neighbors. We discuss the emergence of fairness in the different settings and network topologies.

  8. 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…

  9. 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

  10. Measuring importance in complex networks

    NASA Astrophysics Data System (ADS)

    Morrison, Greg; Dudte, Levi; Mahadevan, L.

    2013-03-01

    A variety of centrality measures can be defined on a network to determine the global `importance' of a node i. However, the inhomogeneity of complex networks implies that not all nodes j will consider i equally important. In this talk, we use a linearized form of the Generalized Erdos numbers [Morrison and Mahadevan EPL 93 40002 (2011)] to define a pairwise measure of the importance of a node i from the perspective of node j which incorporates the global network topology. This localized importance can be used to define a global measure of centrality that is consistent with other well-known centrality measures. We illustrate the use of the localized importance in both artificial and real-world networks with a complex global topology.

  11. Socially Aware Heterogeneous Wireless Networks.

    PubMed

    Kosmides, Pavlos; Adamopoulou, Evgenia; Demestichas, Konstantinos; Theologou, Michael; Anagnostou, Miltiades; Rouskas, Angelos

    2015-01-01

    The development of smart cities has been the epicentre of many researchers' efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users' locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation. PMID:26110402

  12. Socially Aware Heterogeneous Wireless Networks

    PubMed Central

    Kosmides, Pavlos; Adamopoulou, Evgenia; Demestichas, Konstantinos; Theologou, Michael; Anagnostou, Miltiades; Rouskas, Angelos

    2015-01-01

    The development of smart cities has been the epicentre of many researchers’ efforts during the past decade. One of the key requirements for smart city networks is mobility and this is the reason stable, reliable and high-quality wireless communications are needed in order to connect people and devices. Most research efforts so far, have used different kinds of wireless and sensor networks, making interoperability rather difficult to accomplish in smart cities. One common solution proposed in the recent literature is the use of software defined networks (SDNs), in order to enhance interoperability among the various heterogeneous wireless networks. In addition, SDNs can take advantage of the data retrieved from available sensors and use them as part of the intelligent decision making process contacted during the resource allocation procedure. In this paper, we propose an architecture combining heterogeneous wireless networks with social networks using SDNs. Specifically, we exploit the information retrieved from location based social networks regarding users’ locations and we attempt to predict areas that will be crowded by using specially-designed machine learning techniques. By recognizing possible crowded areas, we can provide mobile operators with recommendations about areas requiring datacell activation or deactivation. PMID:26110402

  13. Social network analysis and agent-based modeling in social epidemiology

    PubMed Central

    2012-01-01

    The past five years have seen a growth in the interest in systems approaches in epidemiologic research. These approaches may be particularly appropriate for social epidemiology. Social network analysis and agent-based models (ABMs) are two approaches that have been used in the epidemiologic literature. Social network analysis involves the characterization of social networks to yield inference about how network structures may influence risk exposures among those in the network. ABMs can promote population-level inference from explicitly programmed, micro-level rules in simulated populations over time and space. In this paper, we discuss the implementation of these models in social epidemiologic research, highlighting the strengths and weaknesses of each approach. Network analysis may be ideal for understanding social contagion, as well as the influences of social interaction on population health. However, network analysis requires network data, which may sacrifice generalizability, and causal inference from current network analytic methods is limited. ABMs are uniquely suited for the assessment of health determinants at multiple levels of influence that may couple with social interaction to produce population health. ABMs allow for the exploration of feedback and reciprocity between exposures and outcomes in the etiology of complex diseases. They may also provide the opportunity for counterfactual simulation. However, appropriate implementation of ABMs requires a balance between mechanistic rigor and model parsimony, and the precision of output from complex models is limited. Social network and agent-based approaches are promising in social epidemiology, but continued development of each approach is needed. PMID:22296660

  14. 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).

  15. 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

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

    PubMed

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

    2011-01-01

    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

  17. 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.

  18. 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. PMID:26726910

  19. Speed of complex network synchronization

    NASA Astrophysics Data System (ADS)

    Grabow, C.; Grosskinsky, S.; Timme, M.

    2011-12-01

    Synchrony is one of the most common dynamical states emerging on networks. The speed of convergence towards synchrony provides a fundamental collective time scale for synchronizing systems. Here we study the asymptotic synchronization times for directed networks with topologies ranging from completely ordered, grid-like, to completely disordered, random, including intermediate, partially disordered topologies. We extend the approach of master stability functions to quantify synchronization times. We find that the synchronization times strongly and systematically depend on the network topology. In particular, at fixed in-degree, stronger topological randomness induces faster synchronization, whereas at fixed path length, synchronization is slowest for intermediate randomness in the small-world regime. Randomly rewiring real-world neural, social and transport networks confirms this picture.

  20. 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

  1. 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

  2. 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…

  3. 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…

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

    PubMed

    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

  5. 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

  6. 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

  7. 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.

  8. NEXCADE: perturbation analysis for complex networks.

    PubMed

    Yadav, Gitanjali; Babu, Suresh

    2012-01-01

    Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the 'robust, yet fragile' nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS) can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html. PMID:22870252

  9. NEXCADE: Perturbation Analysis for Complex Networks

    PubMed Central

    Yadav, Gitanjali; Babu, Suresh

    2012-01-01

    Recent advances in network theory have led to considerable progress in our understanding of complex real world systems and their behavior in response to external threats or fluctuations. Much of this research has been invigorated by demonstration of the ‘robust, yet fragile’ nature of cellular and large-scale systems transcending biology, sociology, and ecology, through application of the network theory to diverse interactions observed in nature such as plant-pollinator, seed-dispersal agent and host-parasite relationships. In this work, we report the development of NEXCADE, an automated and interactive program for inducing disturbances into complex systems defined by networks, focusing on the changes in global network topology and connectivity as a function of the perturbation. NEXCADE uses a graph theoretical approach to simulate perturbations in a user-defined manner, singly, in clusters, or sequentially. To demonstrate the promise it holds for broader adoption by the research community, we provide pre-simulated examples from diverse real-world networks including eukaryotic protein-protein interaction networks, fungal biochemical networks, a variety of ecological food webs in nature as well as social networks. NEXCADE not only enables network visualization at every step of the targeted attacks, but also allows risk assessment, i.e. identification of nodes critical for the robustness of the system of interest, in order to devise and implement context-based strategies for restructuring a network, or to achieve resilience against link or node failures. Source code and license for the software, designed to work on a Linux-based operating system (OS) can be downloaded at http://www.nipgr.res.in/nexcade_download.html. In addition, we have developed NEXCADE as an OS-independent online web server freely available to the scientific community without any login requirement at http://www.nipgr.res.in/nexcade.html. PMID:22870252

  10. 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 emotional…

  11. 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.

  12. 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.

  13. 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.

  14. 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.

  15. 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.

  16. Topological evolution of virtual social networks by modeling social activities

    NASA Astrophysics Data System (ADS)

    Sun, Xin; Dong, Junyu; Tang, Ruichun; Xu, Mantao; Qi, Lin; Cai, Yang

    2015-09-01

    With the development of Internet and wireless communication, virtual social networks are becoming increasingly important in the formation of nowadays' social communities. Topological evolution model is foundational and critical for social network related researches. Up to present most of the related research experiments are carried out on artificial networks, however, a study of incorporating the actual social activities into the network topology model is ignored. This paper first formalizes two mathematical abstract concepts of hobbies search and friend recommendation to model the social actions people exhibit. Then a social activities based topology evolution simulation model is developed to satisfy some well-known properties that have been discovered in real-world social networks. Empirical results show that the proposed topology evolution model has embraced several key network topological properties of concern, which can be envisioned as signatures of real social networks.

  17. Complexity matching in neural networks

    NASA Astrophysics Data System (ADS)

    Usefie Mafahim, Javad; Lambert, David; Zare, Marzieh; Grigolini, Paolo

    2015-01-01

    In the wide literature on the brain and neural network dynamics the notion of criticality is being adopted by an increasing number of researchers, with no general agreement on its theoretical definition, but with consensus that criticality makes the brain very sensitive to external stimuli. We adopt the complexity matching principle that the maximal efficiency of communication between two complex networks is realized when both of them are at criticality. We use this principle to establish the value of the neuronal interaction strength at which criticality occurs, yielding a perfect agreement with the adoption of temporal complexity as criticality indicator. The emergence of a scale-free distribution of avalanche size is proved to occur in a supercritical regime. We use an integrate-and-fire model where the randomness of each neuron is only due to the random choice of a new initial condition after firing. The new model shares with that proposed by Izikevich the property of generating excessive periodicity, and with it the annihilation of temporal complexity at supercritical values of the interaction strength. We find that the concentration of inhibitory links can be used as a control parameter and that for a sufficiently large concentration of inhibitory links criticality is recovered again. Finally, we show that the response of a neural network at criticality to a harmonic stimulus is very weak, in accordance with the complexity matching principle.

  18. Partition signed social networks via clustering dynamics

    NASA Astrophysics Data System (ADS)

    Wu, Jianshe; Zhang, Long; Li, Yong; Jiao, Yang

    2016-02-01

    Inspired by the dynamics phenomenon occurred in social networks, the WJJLGS model is modified to imitate the clustering dynamics of signed social networks. Analyses show that the clustering dynamics of the model can be applied to partition signed social networks. Traditionally, blockmodel is applied to partition signed networks. In this paper, a detailed dynamics-based algorithm for signed social networks (DBAS) is presented. Simulations on several typical real-world and illustrative networks that have been analyzed by the blockmodel verify the correctness of the proposed algorithm. The efficiency of the algorithm is verified on large scale synthetic networks.

  19. 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.

  20. Leveraging social networks for toxicovigilance.

    PubMed

    Chary, Michael; Genes, Nicholas; McKenzie, Andrew; Manini, Alex F

    2013-06-01

    The landscape of drug abuse is shifting. Traditional means of characterizing these changes, such as national surveys or voluntary reporting by frontline clinicians, can miss changes in usage the emergence of novel drugs. Delays in detecting novel drug usage patterns make it difficult to evaluate public policy aimed at altering drug abuse. Increasingly, newer methods to inform frontline providers to recognize symptoms associated with novel drugs or methods of administration are needed. The growth of social networks may address this need. The objective of this manuscript is to introduce tools for using data from social networks to characterize drug abuse. We outline a structured approach to analyze social media in order to capture emerging trends in drug abuse by applying powerful methods from artificial intelligence, computational linguistics, graph theory, and agent-based modeling. First, we describe how to obtain data from social networks such as Twitter using publicly available automated programmatic interfaces. Then, we discuss how to use artificial intelligence techniques to extract content useful for purposes of toxicovigilance. This filtered content can be employed to generate real-time maps of drug usage across geographical regions. Beyond describing the real-time epidemiology of drug abuse, techniques from computational linguistics can uncover ways that drug discussions differ from other online conversations. Next, graph theory can elucidate the structure of networks discussing drug abuse, helping us learn what online interactions promote drug abuse and whether these interactions differ among drugs. Finally, agent-based modeling relates online interactions to psychological archetypes, providing a link between epidemiology and behavior. An analysis of social media discussions about drug abuse patterns with computational linguistics, graph theory, and agent-based modeling permits the real-time monitoring and characterization of trends of drugs of abuse. These

  1. 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…

  2. 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…

  3. Synchronization in complex dynamical networks coupled with complex chaotic system

    NASA Astrophysics Data System (ADS)

    Wei, Qiang; Xie, Cheng-Jun; Wang, Bo

    2015-11-01

    This paper investigates synchronization in complex dynamical networks with time delay and perturbation. The node of complex dynamical networks is composed of complex chaotic system. A complex feedback controller is designed to realize different component of complex state variable synchronize up to different scaling complex function when complex dynamical networks realize synchronization. The synchronization scaling function is changed from real field to complex field. Synchronization in complex dynamical networks with constant delay and time-varying coupling delay are investigated, respectively. Numerical simulations show the effectiveness of the proposed method.

  4. Googling Social Interactions: Web Search Engine Based Social Network Construction

    PubMed Central

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

    2010-01-01

    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. PMID:20657762

  5. Traffic gridlock on complex networks

    NASA Astrophysics Data System (ADS)

    Mendes, G. A.; da Silva, L. R.; Herrmann, H. J.

    2012-01-01

    Here we study how a traffic jam spreads on complex networks when driven by an increasing flux between certain initial and final points. For that purpose, we developed two new traffic models based on vehicular traffic and applied them on the Apollonian network and the Swiss road network. The first model is an electrical analog, using ohmic and non-ohmic resistors which is a classical approach in Physics while the second one which we call the herding model, is based on human driving behavior. For both models, we study the sequence of clogged roads up to the traffic gridlock and display the fragilities of the network. In the electrical model, by increasing the external potential, resistors burn out, as the voltage drop between the ends increases above a certain threshold. Analyzing both models, we observed some power-law functions that occur only near a traffic gridlock as well as the dependence on topological features of the network and influence on flux and the robustness in Apollonian networks of different generations.

  6. 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.

  7. 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

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

    USGS Publications Warehouse

    Fletcher, R.J., Jr.; 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.

  9. 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.

  10. 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.

  11. 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.

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

    NASA Astrophysics Data System (ADS)

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

    2015-03-01

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

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

    PubMed Central

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

    2015-01-01

    Identifying communities in complex networks is an effective means for analyzing complex systems, with applications in diverse areas such as social science, engineering, biology and medicine. Finding communities of nodes and finding communities of links are two popular schemes for network analysis. These schemes, however, have inherent drawbacks and are inadequate to capture complex organizational structures in real networks. We introduce a new scheme and an effective approach for identifying complex mixture structures of node and link communities, called hybrid node-link communities. A central piece of our approach is a probabilistic model that accommodates node, link and hybrid node-link communities. Our extensive experiments on various real-world networks, including a large protein-protein interaction network and a large network of semantically associated words, illustrated that the scheme for hybrid communities is superior in revealing network characteristics. Moreover, the new approach outperformed the existing methods for finding node or link communities separately. PMID:25728010

  14. Self-sustained oscillations of complex genomic regulatory networks

    NASA Astrophysics Data System (ADS)

    Ye, Weiming; Huang, Xiaodong; Huang, Xuhui; Li, Pengfei; Xia, Qinzhi; Hu, Gang

    2010-05-01

    Recently, self-sustained oscillations in complex networks consisting of non-oscillatory nodes have attracted great interest in diverse natural and social fields. Oscillatory genomic regulatory networks are one of the most typical examples of this kind. Given an oscillatory genomic network, it is important to reveal the central structure generating the oscillation. However, if the network consists of large numbers of genes and interactions, the oscillation generator is deeply hidden in the complicated interactions. We apply the dominant phase-advanced driving path method proposed in Qian et al. (2010) [1] to reduce complex genomic regulatory networks to one-dimensional and unidirectionally linked network graphs where negative regulatory loops are explored to play as the central generators of the oscillations, and oscillation propagation pathways in the complex networks are clearly shown by tree branches radiating from the loops. Based on the above understanding we can control oscillations of genomic networks with high efficiency.

  15. Community Detection in Quantum Complex Networks

    NASA Astrophysics Data System (ADS)

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

    2014-10-01

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

  16. 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.

  17. 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.

  18. 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…

  19. 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…

  20. 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

  1. 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…

  2. Poor people, poor places, and poor health: the mediating role of social networks and social capital.

    PubMed

    Cattell, V

    2001-05-01

    This paper explores the dynamics between poverty and exclusion; neighbourhood, and health and well being by considering the role of social networks and social capital in the social processes involved. It is based on qualitative research taking two deprived areas as exemplary case studies, and involving depth interviews with residents. Neighbourhood influences on networks and social capital were explored, network typologies developed reflecting structural and cultural aspects of individual's networks, and pathways implicated in health effects considered. The complexity of social capital is addressed. The role of three factors in influencing social networks and social capital are demonstrated: neighbourhood characteristics and perceptions; poverty and social exclusion, and social consciousness. Perceptions of inequality could be a source of social capital as well as demoralisation. Different network structures-dense and weak, homogeneous and heterogeneous- were involved in the creation of social capital and had implications for well being. Coping, enjoyment of life and hope are identified as benefits. Although participation in organisations was confirmed as beneficial, it is suggested that today's heterogeneous neighbourhoods also require regenerated local work opportunities to develop bridging ties necessary for the genesis of inclusive social capital and better health. Despite the capacity of social capital to buffer its harsher effects, the concept is not wholly adequate for explaining the deleterious effects of poverty on health and well being. PMID:11314847

  3. Complex Networks and Critical Infrastructures

    NASA Astrophysics Data System (ADS)

    Setola, Roberto; de Porcellinis, Stefano

    The term “Critical Infrastructures” indicates all those technological infrastructures such as: electric grids, telecommunication networks, railways, healthcare systems, financial circuits, etc. that are more and more relevant for the welfare of our countries. Each one of these infrastructures is a complex, highly non-linear, geographically dispersed cluster of systems, that interact with their human owners, operators, users and with the other infrastructures. Their augmented relevance and the actual political and technological scenarios, which have increased their exposition to accidental failure and deliberate attacks, demand for different and innovative protection strategies (generally indicate as CIP - Critical Infrastructure Protection). To this end it is mandatory to understand the mechanisms that regulate the dynamic of these infrastructures. In this framework, an interesting approach is those provided by the complex networks. In this paper we illustrate some results achieved considering structural and functional properties of the corresponding topological networks both when each infrastructure is assumed as an autonomous system and when we take into account also the dependencies existing among the different infrastructures.

  4. Privacy in Social Networks: A Survey

    NASA Astrophysics Data System (ADS)

    Zheleva, Elena; Getoor, Lise

    In this chapter, we survey the literature on privacy in social networks. We focus both on online social networks and online affiliation networks. We formally define the possible privacy breaches and describe the privacy attacks that have been studied. We present definitions of privacy in the context of anonymization together with existing anonymization techniques.

  5. 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 and…

  6. Three lines to view language network. Comment on "Approaching human language with complex networks" by Cong and Liu

    NASA Astrophysics Data System (ADS)

    Zhao, Yiyi

    2014-12-01

    Language is a kind of complex dynamic network [1-3]. The complexity of language system embodies the interaction and evolution of various languages symbols. The neural network is the physiological basis of language generating and understanding. It also provides a basis to researches on language system by adopting complex network technology and social network analysis. The review [4] gives us three lines to view researches of language network in recent years.

  7. An evolutionary model of social networks

    NASA Astrophysics Data System (ADS)

    Ludwig, M.; Abell, P.

    2007-07-01

    Social networks in communities, markets, and societies self-organise through the interactions of many individuals. In this paper we use a well-known mechanism of social interactions — the balance of sentiment in triadic relations — to describe the development of social networks. Our model contrasts with many existing network models, in that people not only establish but also break up relations whilst the network evolves. The procedure generates several interesting network features such as a variety of degree distributions and degree correlations. The resulting network converges under certain conditions to a steady critical state where temporal disruptions in triangles follow a power-law distribution.

  8. Social Network Closure and Child Adjustment.

    ERIC Educational Resources Information Center

    Fletcher, Anne C.; Newsome, Deborah; Nickerson, Pamela; Bazley, Ronda

    2001-01-01

    Identified fourth graders' peer groups and measured social network closure--extent to which meaningful social relationships exist between children and their friends' parents and among parents whose children are friends. Found that higher social network closure related to higher academic achievement and lower parent-reported externalizing…

  9. 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…

  10. 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.

  11. Relation between structure and size in social networks.

    PubMed

    López, Luis; Sanjuán, Miguel A F

    2002-03-01

    In the context of complex network systems, we model social networks with the property that there is certain degradation of the information flowing through the network. We analyze different kinds of networks, from regular lattices to random graphs. We define an average coordination degree for the network, which can be associated with a certain notion of efficiency. Assuming that there is a limit to the information a person may handle, we show that there exists a close relationship between the structure of the network and its maximum size. PMID:11909165

  12. Propagation dynamics on networks featuring complex topologies

    NASA Astrophysics Data System (ADS)

    Hébert-Dufresne, Laurent; Noël, Pierre-André; Marceau, Vincent; Allard, Antoine; Dubé, Louis J.

    2010-09-01

    Analytical description of propagation phenomena on random networks has flourished in recent years, yet more complex systems have mainly been studied through numerical means. In this paper, a mean-field description is used to coherently couple the dynamics of the network elements (such as nodes, vertices, individuals, etc.) on the one hand and their recurrent topological patterns (such as subgraphs, groups, etc.) on the other hand. In a susceptible-infectious-susceptible (SIS) model of epidemic spread on social networks with community structure, this approach yields a set of ordinary differential equations for the time evolution of the system, as well as analytical solutions for the epidemic threshold and equilibria. The results obtained are in good agreement with numerical simulations and reproduce the behavior of random networks in the appropriate limits which highlights the influence of topology on the processes. Finally, it is demonstrated that our model predicts higher epidemic thresholds for clustered structures than for equivalent random topologies in the case of networks with zero degree correlation.

  13. 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.

  14. 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). PMID:24750076

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

    PubMed

    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. PMID:26172751

  16. 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.

  17. Robust classification of salient links in complex networks.

    PubMed

    Grady, Daniel; Thiemann, Christian; Brockmann, Dirk

    2012-01-01

    Complex networks in natural, social and technological systems generically exhibit an abundance of rich information. Extracting meaningful structural features from data is one of the most challenging tasks in network theory. Many methods and concepts have been proposed to address this problem such as centrality statistics, motifs, community clusters and backbones, but such schemes typically rely on external and arbitrary parameters. It is unknown whether generic networks permit the classification of elements without external intervention. Here we show that link salience is a robust approach to classifying network elements based on a consensus estimate of all nodes. A wide range of empirical networks exhibit a natural, network-implicit classification of links into qualitatively distinct groups, and the salient skeletons have generic statistical properties. Salience also predicts essential features of contagion phenomena on networks, and points towards a better understanding of universal features in empirical networks that are masked by their complexity. PMID:22643891

  18. Robust classification of salient links in complex networks

    NASA Astrophysics Data System (ADS)

    Grady, Daniel; Thiemann, Christian; Brockmann, Dirk

    2012-05-01

    Complex networks in natural, social and technological systems generically exhibit an abundance of rich information. Extracting meaningful structural features from data is one of the most challenging tasks in network theory. Many methods and concepts have been proposed to address this problem such as centrality statistics, motifs, community clusters and backbones, but such schemes typically rely on external and arbitrary parameters. It is unknown whether generic networks permit the classification of elements without external intervention. Here we show that link salience is a robust approach to classifying network elements based on a consensus estimate of all nodes. A wide range of empirical networks exhibit a natural, network-implicit classification of links into qualitatively distinct groups, and the salient skeletons have generic statistical properties. Salience also predicts essential features of contagion phenomena on networks, and points towards a better understanding of universal features in empirical networks that are masked by their complexity.

  19. Social Networks and Social Influences in Adolescence.

    ERIC Educational Resources Information Center

    Cotterell, John

    Young people are concerned with making and keeping friends, and they invest a great deal of energy in group social life to do so. This book charts the interactions of young people both in and out of school and the role of peers and friends in strengthening social attachments and in establishing social identities. It describes how social identities…

  20. 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.

  1. Minimum complexity echo state network.

    PubMed

    Rodan, Ali; Tino, Peter

    2011-01-01

    Reservoir computing (RC) refers to a new class of state-space models with a fixed state transition structure (the reservoir) and an adaptable readout form the state space. The reservoir is supposed to be sufficiently complex so as to capture a large number of features of the input stream that can be exploited by the reservoir-to-output readout mapping. The field of RC has been growing rapidly with many successful applications. However, RC has been criticized for not being principled enough. Reservoir construction is largely driven by a series of randomized model-building stages, with both researchers and practitioners having to rely on a series of trials and errors. To initialize a systematic study of the field, we concentrate on one of the most popular classes of RC methods, namely echo state network, and ask: What is the minimal complexity of reservoir construction for obtaining competitive models and what is the memory capacity (MC) of such simplified reservoirs? On a number of widely used time series benchmarks of different origin and characteristics, as well as by conducting a theoretical analysis we show that a simple deterministically constructed cycle reservoir is comparable to the standard echo state network methodology. The (short-term) MC of linear cyclic reservoirs can be made arbitrarily close to the proved optimal value. PMID:21075721

  2. The Social Side of Information Networking.

    ERIC Educational Resources Information Center

    Katz, James E.

    1997-01-01

    Explores the social issues, including manners, security, crime (fraud), and social control associated with information networking, with emphasis on the Internet. Also addresses the influence of cellular phones, the Internet and other information technologies on society. (GR)

  3. 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. PMID:24102569

  4. 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…

  5. Epidemic dynamics and endemic states in complex networks

    SciTech Connect

    Pastor-Satorras, Romualdo; Vespignani, Alessandro

    2001-06-01

    We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.

  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. 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.

  8. Controlling synchronous patterns in complex networks

    NASA Astrophysics Data System (ADS)

    Lin, Weijie; Fan, Huawei; Wang, Ying; Ying, Heping; Wang, Xingang

    2016-04-01

    Although the set of permutation symmetries of a complex network could be very large, few of them give rise to stable synchronous patterns. Here we present a general framework and develop techniques for controlling synchronization patterns in complex network of coupled chaotic oscillators. Specifically, according to the network permutation symmetry, we design a small-size and weighted network, namely the control network, and use it to control the large-size complex network by means of pinning coupling. We argue mathematically that for any of the network symmetries, there always exists a critical pinning strength beyond which the unstable synchronous pattern associated to this symmetry can be stabilized. The feasibility of the control method is verified by numerical simulations of both artificial and real-world networks and demonstrated experimentally in systems of coupled chaotic circuits. Our studies show the controllability of synchronous patterns in complex networks of coupled chaotic oscillators.

  9. 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

  10. Competition between global and local online social networks.

    PubMed

    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

  11. 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

  12. 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.

  13. 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.

  14. 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…

  15. Multilayer weighted social network model

    NASA Astrophysics Data System (ADS)

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

    2014-11-01

    Recent empirical studies using large-scale data sets have validated the Granovetter hypothesis on the structure of the society in that there are strongly wired communities connected by weak ties. However, as interaction between individuals takes place in diverse contexts, these communities turn out to be overlapping. This implies that the society has a multilayered structure, where the layers represent the different contexts. To model this structure we begin with a single-layer weighted social network (WSN) model showing the Granovetterian structure. We find that when merging such WSN models, a sufficient amount of interlayer correlation is needed to maintain the relationship between topology and link weights, while these correlations destroy the enhancement in the community overlap due to multiple layers. To resolve this, we devise a geographic multilayer WSN model, where the indirect interlayer correlations due to the geographic constraints of individuals enhance the overlaps between the communities and, at the same time, the Granovetterian structure is preserved.

  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. Hypergraph topological quantities for tagged social networks.

    PubMed

    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. PMID:19905191

  18. 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.

  19. Social Networks and Political Participation: How Do Networks Matter?

    ERIC Educational Resources Information Center

    Lim, Chaeyoon

    2008-01-01

    Despite great interest in the role of social networks as channels of political mobilization, few studies have examined which types of social networks work more effectively in recruiting political activists. Using the Citizen Participation Study data, this study shows that contrary to the conventional wisdom in the literature, there is little…

  20. Trust transitivity in social networks.

    PubMed

    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

  1. 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.

  2. 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

  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. 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…

  5. Minority Traders in Thai Village Social Networks.

    ERIC Educational Resources Information Center

    Foster, Brian L.

    1980-01-01

    Examines social networks in three villages in rural Thailand. Demonstrates that Mon (merchant group) villagers, despite their cultural similarity to other Thais, are less strongly linked into the networks of villagers in which they trade. Suggests that ethnicity provides a vehicle for social distance which is beneficial for commerce. (Author/GC)

  6. Gender Differences in Using Social Networks

    ERIC Educational Resources Information Center

    Mazman, S. Guzin; Usluel, Yasemin Kocak

    2011-01-01

    The purpose of this study is to determine individuals' usage purposes of social networks with a focus on the possible differences between females and males. Facebook, which is one the most popular and being most widely used social network, is investigated in this study. The study group consisted of 870 Facebook users who responded to an online…

  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. Offering a Job: Meritocracy and Social Networks.

    ERIC Educational Resources Information Center

    Petersen, Trond; Saporta, Ishak; Seidel, Marc-David L.

    2000-01-01

    Focuses on the impact of sex, race, and social networks in the hiring processes of a midsize, high-technology organization using information about applicants (n=35,229) from 1985-94. Reports that for gender, age and education account for all sex differences; for ethnic minorities, the hiring process is partly reliant on social networks. (CMK)

  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. 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. PMID:25707189

  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. 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. PMID:25003525

  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. PMID:18599659

  15. 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

  16. 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…

  17. 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

  18. 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

  19. 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. PMID:26858671

  20. Exploring the Morphospace of Communication Efficiency in Complex Networks

    PubMed Central

    Goñi, Joaquín; Avena-Koenigsberger, Andrea; Velez de Mendizabal, Nieves; van den Heuvel, Martijn P.; Betzel, Richard F.; Sporns, Olaf

    2013-01-01

    Graph theoretical analysis has played a key role in characterizing global features of the topology of complex networks, describing diverse systems such as protein interactions, food webs, social relations and brain connectivity. How system elements communicate with each other depends not only on the structure of the network, but also on the nature of the system's dynamics which are constrained by the amount of knowledge and resources available for communication processes. Complementing widely used measures that capture efficiency under the assumption that communication preferentially follows shortest paths across the network (“routing”), we define analytic measures directed at characterizing network communication when signals flow in a random walk process (“diffusion”). The two dimensions of routing and diffusion efficiency define a morphospace for complex networks, with different network topologies characterized by different combinations of efficiency measures and thus occupying different regions of this space. We explore the relation of network topologies and efficiency measures by examining canonical network models, by evolving networks using a multi-objective optimization strategy, and by investigating real-world network data sets. Within the efficiency morphospace, specific aspects of network topology that differentially favor efficient communication for routing and diffusion processes are identified. Charting regions of the morphospace that are occupied by canonical, evolved or real networks allows inferences about the limits of communication efficiency imposed by connectivity and dynamics, as well as the underlying selection pressures that have shaped network topology. PMID:23505455

  1. Introduction to focus issue: mesoscales in complex networks.

    PubMed

    Almendral, Juan A; Criado, Regino; Leyva, Inmaculada; Buldú, Javier M; Sendiña-Nadal, Irene

    2011-03-01

    Although the functioning of real complex networks is greatly determined by modularity, the majority of articles have focused, until recently, on either their local scale structure or their macroscopical properties. However, neither of these descriptions can adequately describe the important features that complex networks exhibit due to their organization in modules. This Focus Issue precisely presents the state of the art on the study of complex networks at that intermediate level. The reader will find out why this mesoscale level has become an important topic of research through the latest advances carried out to improve our understanding of the dynamical behavior of modular networks. The contributions presented here have been chosen to cover, from different viewpoints, the many open questions in the field as different aspects of community definition and detection algorithms, moduli overlapping, dynamics on modular networks, interplay between scales, and applications to biological, social, and technological fields. PMID:21456843

  2. Introduction to Focus Issue: Mesoscales in Complex Networks

    NASA Astrophysics Data System (ADS)

    Almendral, Juan A.; Criado, Regino; Leyva, Inmaculada; Buldú, Javier M.; Sendiña-Nadal, Irene

    2011-03-01

    Although the functioning of real complex networks is greatly determined by modularity, the majority of articles have focused, until recently, on either their local scale structure or their macroscopical properties. However, neither of these descriptions can adequately describe the important features that complex networks exhibit due to their organization in modules. This Focus Issue precisely presents the state of the art on the study of complex networks at that intermediate level. The reader will find out why this mesoscale level has become an important topic of research through the latest advances carried out to improve our understanding of the dynamical behavior of modular networks. The contributions presented here have been chosen to cover, from different viewpoints, the many open questions in the field as different aspects of community definition and detection algorithms, moduli overlapping, dynamics on modular networks, interplay between scales, and applications to biological, social, and technological fields.

  3. Adaptive bridge control strategy for opinion evolution on social networks

    NASA Astrophysics Data System (ADS)

    Qian, Cheng; Cao, Jinde; Lu, Jianquan; Kurths, Jürgen

    2011-06-01

    In this paper, we present an efficient opinion control strategy for complex networks, in particular, for social networks. The proposed adaptive bridge control (ABC) strategy calls for controlling a special kind of nodes named bridge and requires no knowledge of the node degrees or any other global or local knowledge, which are necessary for some other immunization strategies including targeted immunization and acquaintance immunization. We study the efficiency of the proposed ABC strategy on random networks, small-world networks, scale-free networks, and the random networks adjusted by the edge exchanging method. Our results show that the proposed ABC strategy is efficient for all of these four kinds of networks. Through an adjusting clustering coefficient by the edge exchanging method, it is found out that the efficiency of our ABC strategy is closely related with the clustering coefficient. The main contributions of this paper can be listed as follows: (1) A new high-order social network is proposed to describe opinion dynamic. (2) An algorithm, which does not require the knowledge of the nodes' degree and other global/local network structure information, is proposed to control the "bridges" more accurately and further control the opinion dynamics of the social networks. The efficiency of our ABC strategy is illustrated by numerical examples. (3) The numerical results indicate that our ABC strategy is more efficient for networks with higher clustering coefficient.

  4. 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

  5. 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.

  6. 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.

  7. Supported Employment: A Route to Social Networks

    ERIC Educational Resources Information Center

    Forrester-Jones, Rachel; Jones, Samantha; Heason, Sophie; Di'Terlizzi, Michele

    2004-01-01

    Background: Evidence suggests that social networks mediate social functioning, self-esteem, mental health and quality of life. This paper presents findings concerning changes in the social lives, skills, behaviour and life experiences of a group of people with intellectual disabilities (n = 18), who gained support from an employment agency to find…

  8. Models of social networks based on social distance attachment

    NASA Astrophysics Data System (ADS)

    Boguñá, Marián; Pastor-Satorras, Romualdo; Díaz-Guilera, Albert; Arenas, Alex

    2004-11-01

    We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by extensive numerical simulations), showing that the model reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities. The model is confronted with the social network formed by people that shares confidential information using the Pretty Good Privacy (PGP) encryption algorithm, the so-called web of trust of PGP.

  9. Models of social networks based on social distance attachment.

    PubMed

    Boguñá, Marián; Pastor-Satorras, Romualdo; Díaz-Guilera, Albert; Arenas, Alex

    2004-11-01

    We propose a class of models of social network formation based on a mathematical abstraction of the concept of social distance. Social distance attachment is represented by the tendency of peers to establish acquaintances via a decreasing function of the relative distance in a representative social space. We derive analytical results (corroborated by extensive numerical simulations), showing that the model reproduces the main statistical characteristics of real social networks: large clustering coefficient, positive degree correlations, and the emergence of a hierarchy of communities. The model is confronted with the social network formed by people that shares confidential information using the Pretty Good Privacy (PGP) encryption algorithm, the so-called web of trust of PGP. PMID:15600707

  10. Complex networks in brain electrical activity

    NASA Astrophysics Data System (ADS)

    Ray, C.; Ruffini, G.; Marco-Pallarés, J.; Fuentemilla, L.; Grau, C.

    2007-08-01

    This letter reports a method to extract a functional network of the human brain from electroencephalogram measurements. A network analysis was performed on the resultant network and the statistics of the cluster coefficient, node degree, path length, and physical distance of the links, were studied. Even given the low electrode count of the experimental data the method was able to extract networks with network parameters that clearly depend on the type of stimulus presented to the subject. This type of analysis opens a door to studying the cerebral networks underlying brain electrical activity, and links the fields of complex networks and cognitive neuroscience.

  11. 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.

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

    PubMed

    Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

    2015-03-01

    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

  13. 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

  14. Complex networks: A winning strategy

    NASA Astrophysics Data System (ADS)

    D'Souza, Raissa M.

    2013-04-01

    Introducing connections between two distinct networks can tip the balance of power -- at times enhancing the weaker system. The properties of the nodes that are linked together often determine which network claims the competitive advantage.

  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. Social network analysis for program implementation.

    PubMed

    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

  17. Temporal fidelity in dynamic social networks

    NASA Astrophysics Data System (ADS)

    Stopczynski, Arkadiusz; Sapiezynski, Piotr; Pentland, Alex `Sandy'; Lehmann, Sune

    2015-10-01

    It has recently become possible to record detailed social interactions in large social systems with high resolution. As we study these datasets, human social interactions display patterns that emerge at multiple time scales, from minutes to months. On a fundamental level, understanding of the network dynamics can be used to inform the process of measuring social networks. The details of measurement are of particular importance when considering dynamic processes where minute-to-minute details are important, because collection of physical proximity interactions with high temporal resolution is difficult and expensive. Here, we consider the dynamic network of proximity-interactions between approximately 500 individuals participating in the Copenhagen Networks Study. We show that in order to accurately model spreading processes in the network, the dynamic processes that occur on the order of minutes are essential and must be included in the analysis.

  18. Benford's Law Applies to Online Social Networks.

    PubMed

    Golbeck, Jennifer

    2015-01-01

    Benford's Law states that, in naturally occurring systems, the frequency of numbers' first digits is not evenly distributed. Numbers beginning with a 1 occur roughly 30% of the time, and are six times more common than numbers beginning with a 9. We show that Benford's Law applies to social and behavioral features of users in online social networks. Using social data from five major social networks (Facebook, Twitter, Google Plus, Pinterest, and LiveJournal), we show that the distribution of first significant digits of friend and follower counts for users in these systems follow Benford's Law. The same is true for the number of posts users make. We extend this to egocentric networks, showing that friend counts among the people in an individual's social network also follows the expected distribution. We discuss how this can be used to detect suspicious or fraudulent activity online and to validate datasets. PMID:26308716

  19. 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).

  20. Analysis of Social Networks by Tensor Decomposition

    NASA Astrophysics Data System (ADS)

    Sizov, Sergej; Staab, Steffen; Franz, Thomas

    The Social Web fosters novel applications targeting a more efficient and satisfying user guidance in modern social networks, e.g., for identifying thematically focused communities, or finding users with similar interests. Large scale and high diversity of users in social networks poses the challenging question of appropriate relevance/authority ranking, for producing fine-grained and rich descriptions of available partners, e.g., to guide the user along most promising groups of interest. Existing methods for graph-based authority ranking lack support for fine-grained latent coherence between user relations and content (i.e., support for edge semantics in graph-based social network models). We present TweetRank, a novel approach for faceted authority ranking in the context of social networks. TweetRank captures the additional latent semantics of social networks by means of statistical methods in order to produce richer descriptions of user relations. We model the social network by a 3-dimensional tensor that enables the seamless representation of arbitrary semantic relations. For the analysis of that model, we apply the PARAFAC decomposition, which can be seen as a multi-modal counterpart to common Web authority ranking with HITS. The result are groupings of users and terms, characterized by authority and navigational (hub) scores with respect to the identified latent topics. Sample experiments with life data of the Twitter community demonstrate the ability of TweetRank to produce richer and more comprehensive contact recommendations than other existing methods for social authority ranking.

  1. The Dynamics of Coalition Formation on Complex Networks.

    PubMed

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

    2015-01-01

    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. PMID:26303622

  2. 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.

  3. 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.

  4. Structural permeability of complex networks to control signals

    NASA Astrophysics Data System (ADS)

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

    2015-09-01

    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.

  5. 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

  6. Minimum-cost control of complex networks

    NASA Astrophysics Data System (ADS)

    Li, Guoqi; Hu, Wuhua; Xiao, Gaoxi; Deng, Lei; Tang, Pei; Pei, Jing; Shi, Luping

    2016-01-01

    Finding the solution for driving a complex network at the minimum energy cost with a given number of controllers, known as the minimum-cost control problem, is critically important but remains largely open. We propose a projected gradient method to tackle this problem, which works efficiently in both synthetic and real-life networks. The study is then extended to the case where each controller can only be connected to a single network node to have the lowest connection complexity. We obtain the interesting insight that such connections basically avoid high-degree nodes of the network, which is in resonance with recent observations on controllability of complex networks. Our results provide the first technical path to enabling minimum-cost control of complex networks, and contribute new insights to locating the key nodes from a minimum-cost control perspective.

  7. 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.

  8. The independent spreaders involved SIR Rumor model in complex networks

    NASA Astrophysics Data System (ADS)

    Qian, Zhen; Tang, Shaoting; Zhang, Xiao; Zheng, Zhiming

    2015-07-01

    Recent studies of rumor or information diffusion process in complex networks show that in contrast to traditional comprehension, individuals who participate in rumor spreading within one network do not always get the rumor from their neighbors. They can obtain the rumor from different sources like online social networks and then publish it on their personal sites. In our paper, we discuss this phenomenon in complex networks by adopting the concept of independent spreaders. Rather than getting the rumor from neighbors, independent spreaders learn it from other channels. We further develop the classic "ignorant-spreaders-stiflers" or SIR model of rumor diffusion process in complex networks. A steady-state analysis is conducted to investigate the final spectrum of the rumor spreading under various spreading rate, stifling rate, density of independent spreaders and average degree of the network. Results show that independent spreaders effectively enhance the rumor diffusion process, by delivering the rumor to regions far away from the current rumor infected regions. And though the rumor spreading process in SF networks is faster than that in ER networks, the final size of rumor spreading in ER networks is larger than that in SF networks.

  9. Investigation of a protein complex network

    NASA Astrophysics Data System (ADS)

    Mashaghi, A. R.; Ramezanpour, A.; Karimipour, V.

    2004-09-01

    The budding yeast Saccharomyces cerevisiae is the first eukaryote whose genome has been completely sequenced. It is also the first eukaryotic cell whose proteome (the set of all proteins) and interactome (the network of all mutual interactions between proteins) has been analyzed. In this paper we study the structure of the yeast protein complex network in which weighted edges between complexes represent the number of shared proteins. It is found that the network of protein complexes is a small world network with scale free behavior for many of its distributions. However we find that there are no strong correlations between the weights and degrees of neighboring complexes. To reveal non-random features of the network we also compare it with a null model in which the complexes randomly select their proteins. Finally we propose a simple evolutionary model based on duplication and divergence of proteins.

  10. 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. PMID:26039072

  11. 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. PMID:26263036

  12. Locating privileged spreaders on an online social network.

    PubMed

    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. PMID:23005178

  13. 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.

  14. 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. PMID:25798551

  15. 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.

  16. Learning in social networks and contraceptive choice.

    PubMed

    Kohler, H P

    1997-08-01

    A puzzling observation in the diffusion of modern fertility control is the persistent diversity in contraceptive practices across communities or social strata. I propose a model of "learning in social networks" to explain this diversity with the random dynamics of word-of-mouth communication. Women are uncertain about the merits of modern contraception and estimate the different qualities of available method based on imprecise information from network partners. Their contraceptive choices are determined by this estimate and by private knowledge about one's personal characteristics. This process of social learning leads to path-dependent adoption of fertility control within, and diversity in contraceptive practices across villages or social strata. PMID:9275246

  17. Geographies of an Online Social Network

    PubMed Central

    Lengyel, Balázs; Varga, Attila; Ságvári, Bence; Jakobi, Ákos; Kertész, János

    2015-01-01

    How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the “death of distance”, physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resemble to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected. PMID:26359668

  18. Geographies of an Online Social Network.

    PubMed

    Lengyel, Balázs; Varga, Attila; Ságvári, Bence; Jakobi, Ákos; Kertész, János

    2015-01-01

    How is online social media activity structured in the geographical space? Recent studies have shown that in spite of earlier visions about the "death of distance", physical proximity is still a major factor in social tie formation and maintenance in virtual social networks. Yet, it is unclear, what are the characteristics of the distance dependence in online social networks. In order to explore this issue the complete network of the former major Hungarian online social network is analyzed. We find that the distance dependence is weaker for the online social network ties than what was found earlier for phone communication networks. For a further analysis we introduced a coarser granularity: We identified the settlements with the nodes of a network and assigned two kinds of weights to the links between them. When the weights are proportional to the number of contacts we observed weakly formed, but spatially based modules resemble to the borders of macro-regions, the highest level of regional administration in the country. If the weights are defined relative to an uncorrelated null model, the next level of administrative regions, counties are reflected. PMID:26359668

  19. 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.

  20. Community structure from spectral properties in complex networks

    NASA Astrophysics Data System (ADS)

    Servedio, V. D. P.; Colaiori, F.; Capocci, A.; Caldarelli, G.

    2005-06-01

    We analyze the spectral properties of complex networks focusing on their relation to the community structure, and develop an algorithm based on correlations among components of different eigenvectors. The algorithm applies to general weighted networks, and, in a suitably modified version, to the case of directed networks. Our method allows to correctly detect communities in sharply partitioned graphs, however it is useful to the analysis of more complex networks, without a well defined cluster structure, as social and information networks. As an example, we test the algorithm on a large scale data-set from a psychological experiment of free word association, where it proves to be successful both in clustering words, and in uncovering mental association patterns.

  1. Social Networks in Improvement of Health Care

    PubMed Central

    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

  2. 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

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

    NASA Astrophysics Data System (ADS)

    Fan, W.; Yeung, K. H.

    2014-07-01

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

  4. Viewing Attractiveness Socialization from a Social Network Perspective.

    ERIC Educational Resources Information Center

    Downs, A. Chris

    Providing a framework for a symposium exploring the influence of physical attractiveness on the socialization process, this paper (1) offers a working definition of physical attractiveness, (2) reviews stereotypes associated with attractiveness, and (3) discusses a social network perspective on the influence of attractiveness. Physical…

  5. 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.

  6. 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

  7. Revealing the hidden language of complex networks.

    PubMed

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

    2014-01-01

    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. PMID:24686408

  8. 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.

  9. Revealing the Hidden Language of Complex Networks

    PubMed Central

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

    2014-01-01

    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. PMID:24686408

  10. Competitive dynamics on complex networks.

    PubMed

    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

  11. 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.

  12. 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. PMID:23564989

  13. 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.

  14. Dynamical robustness analysis of weighted complex networks

    NASA Astrophysics Data System (ADS)

    He, Zhiwei; Liu, Shuai; Zhan, Meng

    2013-09-01

    Robustness of weighted complex networks is analyzed from nonlinear dynamical point of view and with focus on different roles of high-degree and low-degree nodes. We find that the phenomenon for the low-degree nodes being the key nodes in the heterogeneous networks only appears in weakly weighted networks and for weak coupling. For all other parameters, the heterogeneous networks are always highly vulnerable to the failure of high-degree nodes; this point is the same as in the structural robustness analysis. We also find that with random inactivation, heterogeneous networks are always more robust than the corresponding homogeneous networks with the same average degree except for one special parameter. Thus our findings give an integrated picture for the dynamical robustness analysis on complex networks.

  15. A random interacting network model for complex networks

    NASA Astrophysics Data System (ADS)

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

    2015-12-01

    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.

  16. 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

  17. A random interacting network model for complex networks.

    PubMed

    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

  18. Joint Modeling of Multiple Social Networks to Elucidate Primate Social Dynamics: I. Maximum Entropy Principle and Network-Based Interactions

    PubMed Central

    Chan, Stephanie; Fushing, Hsieh; Beisner, Brianne A.; McCowan, Brenda

    2013-01-01

    In a complex behavioral system, such as an animal society, the dynamics of the system as a whole represent the synergistic interaction among multiple aspects of the society. We constructed multiple single-behavior social networks for the purpose of approximating from multiple aspects a single complex behavioral system of interest: rhesus macaque society. Instead of analyzing these networks individually, we describe a new method for jointly analyzing them in order to gain comprehensive understanding about the system dynamics as a whole. This method of jointly modeling multiple networks becomes valuable analytical tool for studying the complex nature of the interaction among multiple aspects of any system. Here we develop a bottom-up, iterative modeling approach based upon the maximum entropy principle. This principle is applied to a multi-dimensional link-based distributional framework, which is derived by jointly transforming the multiple directed behavioral social network data, for extracting patterns of synergistic inter-behavioral relationships. Using a rhesus macaque group as a model system, we jointly modeled and analyzed four different social behavioral networks at two different time points (one stable and one unstable) from a rhesus macaque group housed at the California National Primate Research Center (CNPRC). We report and discuss the inter-behavioral dynamics uncovered by our joint modeling approach with respect to social stability. PMID:23468833

  19. 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.

  20. The Dynamics of Coalition Formation on Complex Networks

    PubMed Central

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

    2015-01-01

    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. PMID:26303622

  1. 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.

  2. 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

  3. The District Social-Pedagogical Complex.

    ERIC Educational Resources Information Center

    Lebedev, O. E.; Fedorets, N. A.

    1992-01-01

    Explores the role of the regional organizational-pedagogical system in the former Soviet Union. Suggests that the organization of social interaction can be addressed on the district level. Discusses a project in the Dnestrovskii district in which a social pedagogical complex has been organized to strengthen schools' material base, develop…

  4. Quantifying networks complexity from information geometry viewpoint

    SciTech Connect

    Felice, Domenico Mancini, Stefano; Pettini, Marco

    2014-04-15

    We consider a Gaussian statistical model whose parameter space is given by the variances of random variables. Underlying this model we identify networks by interpreting random variables as sitting on vertices and their correlations as weighted edges among vertices. We then associate to the parameter space a statistical manifold endowed with a Riemannian metric structure (that of Fisher-Rao). Going on, in analogy with the microcanonical definition of entropy in Statistical Mechanics, we introduce an entropic measure of networks complexity. We prove that it is invariant under networks isomorphism. Above all, considering networks as simplicial complexes, we evaluate this entropy on simplexes and find that it monotonically increases with their dimension.

  5. Sustainable growth in complex networks

    NASA Astrophysics Data System (ADS)

    Tessone, C. J.; Geipel, M. M.; Schweitzer, F.

    2011-12-01

    Based on the analysis of the dependency network in 18 Java projects, we develop a novel model of network growth which considers both preferential attachment and the addition of new nodes with a heterogeneous distribution of their initial degree, k0. Empirically we find that the cumulative distributions of initial and final degrees in the network follow power law behaviours: 1-P(k0)~k01-α, and 1-P(k)~k1-γ, respectively. For the total number of links as a function of the network size, we find empirically K(N)~Nβ, where βin[1.25, 2] (for small N), while converging to β~1 for large N. This indicates a transition from a growth regime with increasing network density towards a sustainable regime, which prevents a collapse due to ever increasing dependencies. Our theoretical framework allows us to predict relations between the exponents α, β, γ, which also link issues of software engineering and developer activity. These relations are verified by means of computer simulations and empirical investigations. They indicate that the growth of real Open Source Software networks occurs on the edge between two regimes, which are dominated either by the initial degree distribution of added nodes, or by the preferential attachment mechanism. Hence, the heterogeneous degree distribution of newly added nodes, found empirically, is essential to describe the laws of sustainable growth in networks.

  6. 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

  7. 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. PMID:24288449

  8. Social Network Formation of Entering College Freshmen.

    ERIC Educational Resources Information Center

    Perl, Harold I.

    The examination of the functioning of social networks has been used to understand how individual and environmental characteristics can mediate the availability of social support. To examine the relationship between personal attributes, psychosocial environmental attributes, and the interaction between these variables, 92 entering college freshmen…

  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. 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…

  12. 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…

  13. 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…

  14. 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…

  15. Online Social Networking: Usage in Adolescents

    ERIC Educational Resources Information Center

    Raju, Nevil Johnson; Valsaraj, Blessy Prabha; Noronha, Judith

    2015-01-01

    Online social networking (OSN) has played a significant role on the relationship among college students. It is becoming a popular medium for socializing online and tools to facilitate friendship. Young adults and adolescents are the most prolific users of OSN sites. The frequent use of OSN sites results in addiction toward these sites and…

  16. 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. PMID:26211717

  17. 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.

  18. 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. PMID:20366458

  19. Structure of Mutualistic Complex Networks

    NASA Astrophysics Data System (ADS)

    Hwang, Jun Kyung; Maeng, Seong Eun; Cha, Moon Yong; Lee, Jae Woo

    We consider the structures of six plant-pollinator mutualistic networks. The plants and pollinators are linked by the plant-pollinating relation. We assigned the visiting frequency of pollinators to a plant as a weight of each link. We calculated the cumulative distribution functions of the degree and strength for the networks. We observed a power-law, linear, and stretched exponential dependence of the cumulative distribution function. We also calculated the disparity and the strength of the nodes s(k) with degree k. We observed that the plant-pollinator networks exhibit an disassortative behaviors and nonlinear dependence of the strength on the nodes. In mutualistic networks links with large weight are connected to the neighbors with small degrees.

  20. Link prediction in complex networks: A survey

    NASA Astrophysics Data System (ADS)

    Lü, Linyuan; Zhou, Tao

    2011-03-01

    Link prediction in complex networks has attracted increasing attention from both physical and computer science communities. The algorithms can be used to extract missing information, identify spurious interactions, evaluate network evolving mechanisms, and so on. This article summaries recent progress about link prediction algorithms, emphasizing on the contributions from physical perspectives and approaches, such as the random-walk-based methods and the maximum likelihood methods. We also introduce three typical applications: reconstruction of networks, evaluation of network evolving mechanism and classification of partially labeled networks. Finally, we introduce some applications and outline future challenges of link prediction algorithms.

  1. 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.

  2. Localized recovery of complex networks against failure.

    PubMed

    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

  3. 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.

  4. Vulnerability analysis for complex networks using aggressive abstraction.

    SciTech Connect

    Colbaugh, Richard; Glass, Kristin L.

    2010-06-01

    Large, complex networks are ubiquitous in nature and society, and there is great interest in developing rigorous, scalable methods for identifying and characterizing their vulnerabilities. This paper presents an approach for analyzing the dynamics of complex networks in which the network of interest is first abstracted to a much simpler, but mathematically equivalent, representation, the required analysis is performed on 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 vulnerability-preserving, finite state abstractions, and develop efficient algorithms for computing these abstractions. We then propose a vulnerability analysis methodology which combines these finite state abstractions with formal analytics from theoretical computer science to yield a comprehensive vulnerability analysis process for networks of realworld scale and complexity. The potential of the proposed approach is illustrated with a case study involving a realistic electric power grid model and also with brief discussions of biological and social network examples.

  5. 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.

  6. 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

  7. Online social networking: a primer for radiology.

    PubMed

    Prasanna, Prasanth M; Seagull, F Jacob; Nagy, Paul

    2011-10-01

    Online social networking is an immature, but rapidly evolving industry of web-based technologies that allow individuals to develop online relationships. News stories populate the headlines about various websites which can facilitate patient and doctor interaction. There remain questions about protecting patient confidentiality and defining etiquette in order to preserve the doctor/patient relationship and protect physicians. How much social networking-based communication or other forms of E-communication is effective? What are the potential benefits and pitfalls of this form of communication? Physicians are exploring how social networking might provide a forum for interacting with their patients, and advance collaborative patient care. Several organizations and institutions have set forth policies to address these questions and more. Though still in its infancy, this form of media has the power to revolutionize the way physicians interact with their patients and fellow health care workers. In the end, physicians must ask what value is added by engaging patients or other health care providers in a social networking format. Social networks may flourish in health care as a means of distributing information to patients or serve mainly as support groups among patients. Physicians must tread a narrow path to bring value to interactions in these networks while limiting their exposure to unwanted liability. PMID:21360214

  8. Artificial Market Simulation with Embedded Complex Network Structures

    NASA Astrophysics Data System (ADS)

    Uchida, Makoto; Shirayama, Susumu

    We investigate a factor of the `network effect' that affects on communication service markets by a multi-agent based simulation approach. The network effect is one of a market characteristic, whereby the benefit of a service or a product increase with use. So far, the network effect has been studied in terms of macroscopic metrics, and interaction patterns of consumers in the market were often ignored. To investigate an infulence of structures of the interaction patterns, we propose a multi-agent based model for a communication serivce market, in which embedded complex network structures are considered as an interaction pattern of agents. Using several complex network models as the interaction patterns, we study the dynamics of a market in which two providers are competing. By a series of simulations, we show that the structural properties of the complex networks, such as the clustering coefficient and degree correlations, are the major factors of the network effect. We also discuss an adequate model of the interaction pattern for reproducing the market dynamics in the real world by performing simulations exploiting with a real data of social network.

  9. Correlations between Community Structure and Link Formation in Complex Networks

    PubMed Central

    Liu, Zhen; He, Jia-Lin; Kapoor, Komal; Srivastava, Jaideep

    2013-01-01

    Background Links in complex networks commonly represent specific ties between pairs of nodes, such as protein-protein interactions in biological networks or friendships in social networks. However, understanding the mechanism of link formation in complex networks is a long standing challenge for network analysis and data mining. Methodology/Principal Findings Links in complex networks have a tendency to cluster locally and form so-called communities. This widely existed phenomenon reflects some underlying mechanism of link formation. To study the correlations between community structure and link formation, we present a general computational framework including a theory for network partitioning and link probability estimation. Our approach enables us to accurately identify missing links in partially observed networks in an efficient way. The links having high connection likelihoods in the communities reveal that links are formed preferentially to create cliques and accordingly promote the clustering level of the communities. The experimental results verify that such a mechanism can be well captured by our approach. Conclusions/Significance Our findings provide a new insight into understanding how links are created in the communities. The computational framework opens a wide range of possibilities to develop new approaches and applications, such as community detection and missing link prediction. PMID:24039818

  10. How do online social networks grow?

    PubMed

    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

  11. Tsallis information dimension of complex networks

    NASA Astrophysics Data System (ADS)

    Zhang, Qi; Luo, Chuanhai; Li, Meizhu; Deng, Yong; Mahadevan, Sankaran

    2015-02-01

    The fractal and self-similarity properties are revealed in many complex networks. The information dimension is a useful method to describe the fractal and self-similarity properties of the complex networks. In order to show the influence of different parts in the complex networks to the information dimension, we have proposed a new information dimension based on the Tsallis entropy namely the Tsallis information dimension. The proposed information dimension is changed according to the scale which is described by the nonextensivity parameter q, and it is inverse with the nonextensivity parameter q. The existing information dimension is a special case of the Tsallis information dimension when q = 1. The Tsallis information dimension is a generalized information dimension of the complex networks.

  12. 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

  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. 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

  15. 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

  16. 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.

  17. 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.

  18. Applications of Complex Networks on Analysis of World Trade Network

    NASA Astrophysics Data System (ADS)

    Lee, Jae Woo; Maeng, Seong Eun; Ha, Gyeong-Gyun; Hyeok Lee, Moon; Cho, Eun Seong

    2013-02-01

    We consider the wealth and the money flow of the world trade data. We analyze the world trade data from year 1948 to 2000 which include the total amounts of the import and export for every country per year. We apply the analyzing methods of the complex networks to the world trade network. We define the wealth as the gross domestic products (GDP) of each country. We defined the backbone network of the world trade network. We generate the backbone network keeping the link with the largest wealth flowing out each country by the import and deleting all remaining links. We observed that the wealth was transferred from the poorer countries to the wealthier countries. We found the asymmetry of the world trade flow by the disparity of the networks. From the backbone network of the world trade we can identify the regional economic connections and wealth flow among the countries.

  19. Minimum structural controllability problems of complex networks

    NASA Astrophysics Data System (ADS)

    Yin, Hongli; Zhang, Siying

    2016-02-01

    Controllability of complex networks has been one of the attractive research areas for both network and control community, and has yielded many promising and significant results in minimum inputs and minimum driver vertices. However, few studies have been devoted to studying the minimum controlled vertex set through which control over the network with arbitrary structure can be achieved. In this paper, we prove that the minimum driver vertices driven by different inputs are not sufficient to ensure the full control of the network when the associated graph contains the inaccessible strongly connected component which has perfect matching and propose an algorithm to identify a minimum controlled vertex set for network with arbitrary structure using convenient graph and mathematical tools. And the simulation results show that the controllability of network is correlated to the number of inaccessible strongly connected components which have perfect matching and these results promote us to better understand the relationship between the network's structural characteristics and its control.

  20. Core organization of directed complex networks

    NASA Astrophysics Data System (ADS)

    Azimi-Tafreshi, N.; Dorogovtsev, S. N.; Mendes, J. F. F.

    2013-03-01

    The recursive removal of leaves (dead end vertices) and their neighbors from an undirected network results, when this pruning algorithm stops, in a so-called core of the network. This specific subgraph should be distinguished from k-cores, which are principally different subgraphs in networks. If the vertex mean degree of a network is sufficiently large, the core is a giant cluster containing a finite fraction of vertices. We find that generalization of this pruning algorithm to directed networks provides a significantly more complex picture of cores. By implementing a rate equation approach to this pruning procedure for directed uncorrelated networks, we identify a set of cores progressively embedded into each other in a network and describe their birth points and structure.

  1. 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.

  2. Dynamics on Complex Networks and Applications

    NASA Astrophysics Data System (ADS)

    Motter, Adilson E.; Matías, Manuel A.; Kurths, Jürgen; Ott, Edward

    2006-12-01

    At the eight-year anniversary of Watts and Strogatz’s work on the collective dynamics of small-world networks and seven years after Barabási and Albert’s discovery of scale-free networks, the area of dynamical processes on complex networks is at the forefront of the current research on nonlinear dynamics and complex systems. This volume brings together a selection of original contributions in complementary topics of statistical physics, nonlinear dynamics and biological sciences, and is expected to provide the reader with a comprehensive up-to-date representation of this rapidly developing area.

  3. Mapping dynamical systems onto complex networks

    NASA Astrophysics Data System (ADS)

    Borges, E. P.; Cajueiro, D. O.; Andrade, R. F. S.

    2007-08-01

    The objective of this study is to design a procedure to characterize chaotic dynamical systems, in which they are mapped onto a complex network. The nodes represent the regions of space visited by the system, while the edges represent the transitions between these regions. Parameters developed to quantify the properties of complex networks, including those related to higher order neighbourhoods, are used in the analysis. The methodology is tested on the logistic map, focusing on the onset of chaos and chaotic regimes. The corresponding networks were found to have distinct features that are associated with the particular type of dynamics that generated them.

  4. 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. PMID:26505473

  5. 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

  6. 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.

  7. Constant Communities in Complex Networks

    NASA Astrophysics Data System (ADS)

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

    2013-05-01

    Identifying community structure is a fundamental problem in network analysis. Most community detection algorithms are based on optimizing a combinatorial parameter, for example modularity. This optimization is generally NP-hard, thus merely changing the vertex order can alter their assignments to the community. However, there has been less study on how vertex ordering influences the results of the community detection algorithms. Here we identify and study the properties of invariant groups of vertices (constant communities) whose assignment to communities are, quite remarkably, not affected by vertex ordering. The percentage of constant communities can vary across different applications and based on empirical results we propose metrics to evaluate these communities. Using constant communities as a pre-processing step, one can significantly reduce the variation of the results. Finally, we present a case study on phoneme network and illustrate that constant communities, quite strikingly, form the core functional units of the larger communities.

  8. 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.

  9. Heat diffusion: thermodynamic depth complexity of networks.

    PubMed

    Escolano, Francisco; Hancock, Edwin R; Lozano, Miguel A

    2012-03-01

    In this paper we use the Birkhoff-von Neumann decomposition of the diffusion kernel to compute a polytopal measure of graph complexity. We decompose the diffusion kernel into a series of weighted Birkhoff combinations and compute the entropy associated with the weighting proportions (polytopal complexity). The maximum entropy Birkhoff combination can be expressed in terms of matrix permanents. This allows us to introduce a phase-transition principle that links our definition of polytopal complexity to the heat flowing through the network at a given diffusion time. The result is an efficiently computed complexity measure, which we refer to as flow complexity. Moreover, the flow complexity measure allows us to analyze graphs and networks in terms of the thermodynamic depth. We compare our method with three alternative methods described in the literature (Estrada's heterogeneity index, the Laplacian energy, and the von Neumann entropy). Our study is based on 217 protein-protein interaction (PPI) networks including histidine kinases from several species of bacteria. We find a correlation between structural complexity and phylogeny (more evolved species have statistically more complex PPIs). Although our methods outperform the alternatives, we find similarities with Estrada's heterogeneity index in terms of network size independence and predictive power. PMID:22587160

  10. Heat diffusion: Thermodynamic depth complexity of networks

    NASA Astrophysics Data System (ADS)

    Escolano, Francisco; Hancock, Edwin R.; Lozano, Miguel A.

    2012-03-01

    In this paper we use the Birkhoff-von Neumann decomposition of the diffusion kernel to compute a polytopal measure of graph complexity. We decompose the diffusion kernel into a series of weighted Birkhoff combinations and compute the entropy associated with the weighting proportions (polytopal complexity). The maximum entropy Birkhoff combination can be expressed in terms of matrix permanents. This allows us to introduce a phase-transition principle that links our definition of polytopal complexity to the heat flowing through the network at a given diffusion time. The result is an efficiently computed complexity measure, which we refer to as flow complexity. Moreover, the flow complexity measure allows us to analyze graphs and networks in terms of the thermodynamic depth. We compare our method with three alternative methods described in the literature (Estrada's heterogeneity index, the Laplacian energy, and the von Neumann entropy). Our study is based on 217 protein-protein interaction (PPI) networks including histidine kinases from several species of bacteria. We find a correlation between structural complexity and phylogeny (more evolved species have statistically more complex PPIs). Although our methods outperform the alternatives, we find similarities with Estrada's heterogeneity index in terms of network size independence and predictive power.

  11. Quantum navigation and ranking in complex networks.

    PubMed

    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

  12. 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

  13. 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

  14. Shock waves on complex networks.

    PubMed

    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

  15. 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.

  16. 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…

  17. Social network predictors of latrine ownership.

    PubMed

    Shakya, Holly B; Christakis, Nicholas A; Fowler, James H

    2015-01-01

    Poor sanitation, including the lack of clean functioning toilets, is a major factor contributing to morbidity and mortality from infectious diseases in the developing world. We examine correlates of latrine ownership in rural India with a focus on social network predictors. Participants from 75 villages provided the names of their social contacts as well as their own relevant demographic and household characteristics. Using these measures, we test whether the latrine ownership of an individual's social contacts is a significant predictor of individual latrine ownership. We also investigate whether network centrality significantly predicts latrine ownership, and if so, whether it moderates the relationship between the latrine ownership of the individual and that of her social contacts. Our results show that, controlling for the standard predictors of latrine ownership such as caste, education, and income, individuals are more likely to own latrines if their social contacts own latrines. Interaction models suggest that this relationship is stronger among those of the same caste, the same education, and those with stronger social ties. We also find that more central individuals are more likely to own latrines, but the correlation in latrine ownership between social contacts is strongest among individuals on the periphery of the network. Although more data is needed to determine how much the clustering of latrine ownership may be caused by social influence, the results here suggest that interventions designed to promote latrine ownership should consider focusing on those at the periphery of the network. The reason is that they are 1) less likely to own latrines and 2) more likely to exhibit the same behavior as their social contacts, possibly as a result of the spread of latrine adoption from one person to another. PMID:24726688

  18. 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.

  19. Group Colocation Behavior in Technological Social Networks

    PubMed Central

    Brown, Chloë; Lathia, Neal; Mascolo, Cecilia; Noulas, Anastasios; Blondel, Vincent

    2014-01-01

    We analyze two large datasets from technological networks with location and social data: user location records from an online location-based social networking service, and anonymized telecommunications data from a European cellphone operator, in order to investigate the differences between individual and group behavior with respect to physical location. We discover agreements between the two datasets: firstly, that individuals are more likely to meet with one friend at a place they have not visited before, but tend to meet at familiar locations when with a larger group. We also find that groups of individuals are more likely to meet at places that their other friends have visited, and that the type of a place strongly affects the propensity for groups to meet there. These differences between group and solo mobility has potential technological applications, for example, in venue recommendation in location-based social networks. PMID:25148037

  20. 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. PMID:25192305

  1. 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

  2. The Analysis of Duocentric Social Networks: A Primer

    PubMed Central

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

    2016-01-01

    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. PMID:27182084

  3. 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.

  4. 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. PMID:23982106

  5. 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. PMID:24061930

  6. 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

  7. Cascade defense via routing in complex networks

    NASA Astrophysics Data System (ADS)

    Xu, Xiao-Lan; Du, Wen-Bo; Hong, Chen

    2015-05-01

    As the cascading failures in networked traffic systems are becoming more and more serious, research on cascade defense in complex networks has become a hotspot in recent years. In this paper, we propose a traffic-based cascading failure model, in which each packet in the network has its own source and destination. When cascade is triggered, packets will be redistributed according to a given routing strategy. Here, a global hybrid (GH) routing strategy, which uses the dynamic information of the queue length and the static information of nodes' degree, is proposed to defense the network cascade. Comparing GH strategy with the shortest path (SP) routing, efficient routing (ER) and global dynamic (GD) routing strategies, we found that GH strategy is more effective than other routing strategies in improving the network robustness against cascading failures. Our work provides insight into the robustness of networked traffic systems.

  8. Dynamic information routing in complex networks.

    PubMed

    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

  9. 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.

  10. Distinction and connection between contact network, social network, and disease transmission network.

    PubMed

    Chen, Shi; Lanzas, Cristina

    2016-09-01

    In this paper we discuss the distinction and connection between three closely related networks in animal ecology and epidemiology studies: the contact, social, and disease transmission networks. We provide a robust theoretical definition and interpretation of these three networks, demonstrate that social and disease transmission networks can be derived as spanning subgraphs of contact network, and show examples based on real-world high-resolution cattle contact structure data. Furthermore, we establish a modeling framework to track potential disease transmission dynamics and construct transmission network based on the observed animal contact network. PMID:27544246

  11. 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. PMID:25106397

  12. Disease Dynamics in a Dynamic Social Network

    PubMed Central

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

    2010-01-01

    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. PMID:20563303

  13. The "Majority Illusion" in Social Networks.

    PubMed

    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

  14. 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

  15. Social Trust Prediction Using Heterogeneous Networks

    PubMed Central

    HUANG, JIN; NIE, FEIPING; HUANG, HENG; TU, YI-CHENG; LEI, YU

    2014-01-01

    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. PMID:24729776

  16. 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.

  17. The Influence of Social Networks on the Use of Health Services.

    ERIC Educational Resources Information Center

    Reeder, Sharon; And Others

    This paper describes the effect of a person's social network upon his or her use of health services. A "social network" consists of complex strands of affiliations radiating from an individual to his/her close associates (kin and friends) and then to the larger society beyond. Health services include hospitals, clinics, doctors' offices, and…

  18. 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

  19. 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

  20. 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. PMID:25964337

  1. 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. PMID:20566337

  2. 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

  3. Pain tolerance predicts human social network size.

    PubMed

    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

  4. 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…

  5. 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…

  6. A Developmental Analysis of Children's Social Support Networks.

    ERIC Educational Resources Information Center

    Kriegler, Julie A.; Bogat, G. Anne

    Although much investigation of adult social support networks has been done, little attention has been paid to children's social support networks. Childhood patterns of social support probably influence adult patterns. A study was conducted to describe the social networks of third through sixth grade children. It also tests the validity of a new…

  7. Assembly of complex plant–fungus networks

    PubMed Central

    Toju, Hirokazu; Guimarães, Paulo R.; Olesen, Jens M.; Thompson, John N.

    2014-01-01

    Species in ecological communities build complex webs of interaction. Although revealing the architecture of these networks is fundamental to understanding ecological and evolutionary dynamics in nature, it has been difficult to characterize the structure of most species-rich ecological systems. By overcoming this limitation through next-generation sequencing technology, we herein uncover the network architecture of below-ground plant–fungus symbioses, which are ubiquitous to terrestrial ecosystems. The examined symbiotic network of a temperate forest in Japan includes 33 plant species and 387 functionally and phylogenetically diverse fungal taxa, and the overall network architecture differs fundamentally from that of other ecological networks. In contrast to results for other ecological networks and theoretical predictions for symbiotic networks, the plant–fungus network shows moderate or relatively low levels of interaction specialization and modularity and an unusual pattern of ‘nested’ network architecture. These results suggest that species-rich ecological networks are more architecturally diverse than previously recognized. PMID:25327887

  8. Information filtering in complex weighted networks

    NASA Astrophysics Data System (ADS)

    Radicchi, Filippo; Ramasco, José J.; Fortunato, Santo

    2011-04-01

    Many systems in nature, society, and technology can be described as networks, where the vertices are the system’s elements, and edges between vertices indicate the interactions between the corresponding elements. Edges may be weighted if the interaction strength is measurable. However, the full network information is often redundant because tools and techniques from network analysis do not work or become very inefficient if the network is too dense, and some weights may just reflect measurement errors and need to be be discarded. Moreover, since weight distributions in many complex weighted networks are broad, most of the weight is concentrated among a small fraction of all edges. It is then crucial to properly detect relevant edges. Simple thresholding would leave only the largest weights, disrupting the multiscale structure of the system, which is at the basis of the structure of complex networks and ought to be kept. In this paper we propose a weight-filtering technique based on a global null model [Global Statistical Significance (GloSS) filter], keeping both the weight distribution and the full topological structure of the network. The method correctly quantifies the statistical significance of weights assigned independently to the edges from a given distribution. Applications to real networks reveal that the GloSS filter is indeed able to identify relevant connections between vertices.

  9. Self-determined mechanisms in complex networks

    NASA Astrophysics Data System (ADS)

    Liu, Yang; Yuan, Jian; Shan, Xiuming; Ren, Yong; Ma, Zhengxin

    2008-03-01

    Self-organized networks are pervasive in communication systems such as the Internet, overlay networks, peer-to-peer networks, and cluster-based services. These networks evolve into complex topologies, under specific driving forces, i.e. user demands, technological innovations, design objectives and so on. Our study focuses on the driving forces behind individual evolutions of network components, and their stimulation and domination to the self-organized networks which are defined as self-determined mechanisms in this paper. Understanding forces underlying the evolution of networks should enable informed design decisions and help to avoid unwanted surprises, such as congestion collapse. A case study on the macroscopic evolution of the Internet topology of autonomous systems under a specific driving force is then presented. Using computer simulations, it is found that the power-law degree distribution can originate from a connection preference to larger numbers of users, and that the small-world property can be caused by rapid growth in the number of users. Our results provide a new feasible perspective to understand intrinsic fundamentals in the topological evolution of complex networks.

  10. Aging in complex interdependency networks.

    PubMed

    Vural, Dervis C; Morrison, Greg; Mahadevan, L

    2014-02-01

    Although species longevity is subject to a diverse range of evolutionary forces, the mortality curves of a wide variety of organisms are rather similar. Here we argue that qualitative and quantitative features of aging can be reproduced by a simple model based on the interdependence of fault-prone agents on one other. In addition to fitting our theory to the empiric mortality curves of six very different organisms, we establish the dependence of lifetime and aging rate on initial conditions, damage and repair rate, and system size. We compare the size distributions of disease and death and see that they have qualitatively different properties. We show that aging patterns are independent of the details of interdependence network structure, which suggests that aging is a many-body effect, and that the qualitative and quantitative features of aging are not sensitively dependent on the details of dependency structure or its formation. PMID:25353538

  11. 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.

  12. 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.

  13. 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.

  14. 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…

  15. 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.

  16. 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.

  17. 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.

  18. Complex networks with scale-free nature and hierarchical modularity

    NASA Astrophysics Data System (ADS)

    Shekatkar, Snehal M.; Ambika, G.

    2015-09-01

    Generative mechanisms which lead to empirically observed structure of networked systems from diverse fields like biology, technology and social sciences form a very important part of study of complex networks. The structure of many networked systems like biological cell, human society and World Wide Web markedly deviate from that of completely random networks indicating the presence of underlying processes. Often the main process involved in their evolution is the addition of links between existing nodes having a common neighbor. In this context we introduce an important property of the nodes, which we call mediating capacity, that is generic to many networks. This capacity decreases rapidly with increase in degree, making hubs weak mediators of the process. We show that this property of nodes provides an explanation for the simultaneous occurrence of the observed scale-free structure and hierarchical modularity in many networked systems. This also explains the high clustering and small-path length seen in real networks as well as non-zero degree-correlations. Our study also provides insight into the local process which ultimately leads to emergence of preferential attachment and hence is also important in understanding robustness and control of real networks as well as processes happening on real networks.

  19. 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

  20. 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.

  1. Inference, simulation, modeling, and analysis of complex networks, with special emphasis on complex networks in systems biology

    NASA Astrophysics Data System (ADS)

    Christensen, Claire Petra

    Across diverse fields ranging from physics to biology, sociology, and economics, the technological advances of the past decade have engendered an unprecedented explosion of data on highly complex systems with thousands, if not millions of interacting components. These systems exist at many scales of size and complexity, and it is becoming ever-more apparent that they are, in fact, universal, arising in every field of study. Moreover, they share fundamental properties---chief among these, that the individual interactions of their constituent parts may be well-understood, but the characteristic behaviour produced by the confluence of these interactions---by these complex networks---is unpredictable; in a nutshell, the whole is more than the sum of its parts. There is, perhaps, no better illustration of this concept than the discoveries being made regarding complex networks in the biological sciences. In particular, though the sequencing of the human genome in 2003 was a remarkable feat, scientists understand that the "cellular-level blueprints" for the human being are cellular-level parts lists, but they say nothing (explicitly) about cellular-level processes. The challenge of modern molecular biology is to understand these processes in terms of the networks of parts---in terms of the interactions among proteins, enzymes, genes, and metabolites---as it is these processes that ultimately differentiate animate from inanimate, giving rise to life! It is the goal of systems biology---an umbrella field encapsulating everything from molecular biology to epidemiology in social systems---to understand processes in terms of fundamental networks of core biological parts, be they proteins or people. By virtue of the fact that there are literally countless complex systems, not to mention tools and techniques used to infer, simulate, analyze, and model these systems, it is impossible to give a truly comprehensive account of the history and study of complex systems. The author

  2. 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.

  3. Detecting hidden nodes in complex networks from time series.

    PubMed

    Su, Ri-Qi; Wang, Wen-Xu; Lai, Ying-Cheng

    2012-06-01

    We develop a general method to detect hidden nodes in complex networks, using only time series from nodes that are accessible to external observation. Our method is based on compressive sensing and we formulate a general framework encompassing continuous- and discrete-time and the evolutionary-game type of dynamical systems as well. For concrete demonstration, we present an example of detecting hidden nodes from an experimental social network. Our paradigm for detecting hidden nodes is expected to find applications in a variety of fields where identifying hidden or black-boxed objects based on a limited amount of data is of interest. PMID:23005153

  4. Situation analysis of relationship in social networks based on link entropy

    NASA Astrophysics Data System (ADS)

    Li, Ke; Li, Hui-Jia; Wang, Hao

    2015-05-01

    Since the existence of certain and uncertain characteristics of the relationships between nodes in social network, the study of social features is expanded by combining the set pair analysis and social computing. In this paper, a new method is created to describe nodes relationship situation in social network, i.e. set pair relationship situation, including generalized set pair relationship situation, generalized set pair close situation and generalized set pair loosen situation. In order to analyze the situation in social network, each kind of set pair relation situation are classified. Combining with the complexity of the social network system and the features of connection entropy, generalized connection entropy which used to express the complexity of social networks is proposed. It includes the generalized same entropy, the generalized difference entropy, and the generalized opposite entropy. These different types of entropies can be used to analyze the social network relationship stability from a more theoretical view. Then a situation analysis model and the corresponding algorithm is proposed. Finally the effectiveness of this method in analyzing the relationships in social networks is proved. Thus, our model can be used to reveal the relationship between social network and node state stability efficiently.

  5. 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

  6. 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

  7. Information Spread of Emergency Events: Path Searching on Social Networks

    PubMed Central

    Hu, Hongzhi; Wu, Tunan

    2014-01-01

    Emergency has attracted global attentions of government and the public, and it will easily trigger a series of serious social problems if it is not supervised effectively in the dissemination process. In the Internet world, people communicate with each other and form various virtual communities based on social networks, which lead to a complex and fast information spread pattern of emergency events. This paper collects Internet data based on data acquisition and topic detection technology, analyzes the process of information spread on social networks, describes the diffusions and impacts of that information from the perspective of random graph, and finally seeks the key paths through an improved IBF algorithm. Application cases have shown that this algorithm can search the shortest spread paths efficiently, which may help us to guide and control the information dissemination of emergency events on early warning. PMID:24600323

  8. 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

  9. Network representations of immune system complexity.

    PubMed

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

    2015-01-01

    The mammalian immune system is a dynamic multiscale 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 nonlinear behaviors arising from dynamic, feedback-regulated interactions among many components. One of the major goals of systems immunology is to quantitatively measure these complex multiscale 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

  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. Micro-macro analysis of complex networks.

    PubMed

    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

  12. 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

  13. 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.

  14. Structurally robust control of complex networks.

    PubMed

    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. PMID:25679675

  15. 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...

  16. 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.

  17. 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

  18. 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.

  19. 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.

  20. Cascade control and defense in complex networks.

    PubMed

    Motter, Adilson E

    2004-08-27

    Complex networks with a heterogeneous distribution of loads may undergo a global cascade of overload failures when highly loaded nodes or edges are removed due to attacks or failures. Since a small attack or failure has the potential to trigger a global cascade, a fundamental question regards the possible strategies of defense to prevent the cascade from propagating through the entire network. Here we introduce and investigate a costless strategy of defense based on a selective further removal of nodes and edges, right after the initial attack or failure. This intentional removal of network elements is shown to drastically reduce the size of the cascade. PMID:15447153

  1. 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.

  2. An improved sampling method of complex network

    NASA Astrophysics Data System (ADS)

    Gao, Qi; Ding, Xintong; Pan, Feng; Li, Weixing

    2014-12-01

    Sampling subnet is an important topic of complex network research. Sampling methods influence the structure and characteristics of subnet. Random multiple snowball with Cohen (RMSC) process sampling which combines the advantages of random sampling and snowball sampling is proposed in this paper. It has the ability to explore global information and discover the local structure at the same time. The experiments indicate that this novel sampling method could keep the similarity between sampling subnet and original network on degree distribution, connectivity rate and average shortest path. This method is applicable to the situation where the prior knowledge about degree distribution of original network is not sufficient.

  3. 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…

  4. 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…

  5. 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…

  6. Exploring Social Networking: Developing Critical Literacies

    ERIC Educational Resources Information Center

    Watson, Pauline

    2012-01-01

    While schools have been using computers within their classrooms for years now, there has been a purposeful ignoring of the growing power of social networks such as Facebook and Twitter. Many schools ban students from accessing and using sites such as Facebook at school and many English and literacy teachers ignore or deny their value as a teaching…

  7. 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…

  8. 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…

  9. 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…

  10. 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.…

  11. 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…

  12. 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…

  13. 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…

  14. Security and Privacy in Online Social Networks

    NASA Astrophysics Data System (ADS)

    Cutillo, Leucio Antonio; Manulis, Mark; Strufe, Thorsten

    Social Network Services (SNS) are currently drastically revolutionizing the way people interact, thus becoming de facto a predominant service on the web, today.1 The impact of this paradigm change on socioeconomic and technical aspects of collaboration and interaction is comparable to that caused by the deployment of World Wide Web in the 1990s.

  15. 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.

  16. 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,…

  17. 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…

  18. 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…

  19. 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.…

  20. Early Adolescent Social Networks and Computer Use.

    ERIC Educational Resources Information Center

    Orleans, Myron; Laney, Margaret C.

    A research project was conducted to examine the interactions between the social networks of young adolescents and their computer usage. Particular attention was focused upon whether computers tend to isolate youthful users. Adult anxiety regarding the damaging effects of computers on children was assessed. Parental involvement, orientation to…

  1. "Cloudworks": Social Networking for Learning Design

    ERIC Educational Resources Information Center

    Conole, Grainne; Culver, Juliette

    2009-01-01

    Can we apply the best of Web 2.0 principles to an educational context? More specifically can we use this as a means of shifting teaching practice to a culture of sharing learning ideas and designs? This paper describes a new social networking site, "Cloudworks", which aims to provide a mechanism for sharing, discussing and finding learning and…

  2. 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…

  3. 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…

  4. 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…

  5. 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.

  6. 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.

  7. Social determinants in an Australian urban region: a 'complexity' lens.

    PubMed

    Fisher, Matthew; Milos, Danijela; Baum, Frances; Friel, Sharon

    2016-03-01

    Area-based strategies have been widely employed in efforts to improve population health and take action on social determinants of health (SDH) and health inequities, including in urban areas where many of the social, economic and environmental factors converge to influence health. Increasingly, these factors are recognized as being part of a complex system, where population health outcomes are shaped by multiple, interacting factors operating at different levels of social organization. This article reports on research to assess the extent to which an alliance of health and human service networks is able to promote action on SDH within an Australian urban region, using a complex systems frame. We found that such an alliance was able to promote some effective action which takes into account complex interactions between social factors affecting health, but also identified significant potential barriers to other forms of desired action identified by alliance members. We found that a complex systems lens was useful in assessing a collaborative intervention to address SDH within an urban region. PMID:25107921

  8. 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

  9. 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. PMID:25072242

  10. 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

  11. Managing Trust in Online Social Networks

    NASA Astrophysics Data System (ADS)

    Bhuiyan, Touhid; Josang, Audun; Xu, Yue

    In recent years, there is a dramatic growth in number and popularity of online social networks. There are many networks available with more than 100 million registered users such as Facebook, MySpace, QZone, Windows Live Spaces etc. People may connect, discover and share by using these online social networks. The exponential growth of online communities in the area of social networks attracts the attention of the researchers about the importance of managing trust in online environment. Users of the online social networks may share their experiences and opinions within the networks about an item which may be a product or service. The user faces the problem of evaluating trust in a service or service provider before making a choice. Recommendations may be received through a chain of friends network, so the problem for the user is to be able to evaluate various types of trust opinions and recommendations. This opinion or recommendation has a great influence to choose to use or enjoy the item by the other user of the community. Collaborative filtering system is the most popular method in recommender system. The task in collaborative filtering is to predict the utility of items to a particular user based on a database of user rates from a sample or population of other users. Because of the different taste of different people, they rate differently according to their subjective taste. If two people rate a set of items similarly, they share similar tastes. In the recommender system, this information is used to recommend items that one participant likes, to other persons in the same cluster. But the collaborative filtering system performs poor when there is insufficient previous common rating available between users; commonly known as cost start problem. To overcome the cold start problem and with the dramatic growth of online social networks, trust based approach to recommendation has emerged. This approach assumes a trust network among users and makes recommendations

  12. 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.

  13. 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. PMID:22711416

  14. 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

  15. 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. PMID:22796101

  16. 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

  17. 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.

  18. 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…

  19. 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

  20. Controlling extreme events on complex networks.

    PubMed

    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

  1. 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

  2. Particle competition for complex network community detection

    NASA Astrophysics Data System (ADS)

    Quiles, Marcos G.; Zhao, Liang; Alonso, Ronaldo L.; Romero, Roseli A. F.

    2008-09-01

    In many real situations, randomness is considered to be uncertainty or even confusion which impedes human beings from making a correct decision. Here we study the combined role of randomness and determinism in particle dynamics for complex network community detection. In the proposed model, particles walk in the network and compete with each other in such a way that each of them tries to possess as many nodes as possible. Moreover, we introduce a rule to adjust the level of randomness of particle walking in the network, and we have found that a portion of randomness can largely improve the community detection rate. Computer simulations show that the model has good community detection performance and at the same time presents low computational complexity.

  3. 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

  4. Functional networks in emotional moral and nonmoral social judgments.

    PubMed

    Moll, Jorge; de Oliveira-Souza, Ricardo; Bramati, Ivanei E; Grafman, Jordan

    2002-07-01

    Reading daily newspaper articles often evokes opinions and social judgments about the characters and stories. Social and moral judgments rely on the proper functioning of neural circuits concerned with complex cognitive and emotional processes. To examine whether dissociable neural systems mediate emotionally charged moral and nonmoral social judgments, we used a visual sentence verification task in conjunction with functional magnetic resonance imaging (fMRI). We found that a network comprising the medial orbitofrontal cortex, the temporal pole and the superior temporal sulcus of the left hemisphere was specifically activated by moral judgments. In contrast, judgment of emotionally evocative, but non-moral statements activated the left amygdala, lingual gyri, and the lateral orbital gyrus. These findings provide new evidence that the orbitofrontal cortex has dedicated subregions specialized in processing specific forms of social behavior. PMID:12169253

  5. 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.

  6. Unfavorable Individuals in Social Gaming Networks.

    PubMed

    Zhang, Yichao; Chen, Guanrong; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

    2015-01-01

    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. PMID:26648549

  7. Unfavorable Individuals in Social Gaming Networks

    NASA Astrophysics Data System (ADS)

    Zhang, Yichao; Chen, Guanrong; Guan, Jihong; Zhang, Zhongzhi; Zhou, Shuigeng

    2015-12-01

    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.

  8. Information spreading on dynamic social networks

    NASA Astrophysics Data System (ADS)

    Liu, Chuang; Zhang, Zi-Ke

    2014-04-01

    Nowadays, information spreading on social networks has triggered an explosive attention in various disciplines. Most of previous works in this area mainly focus on discussing the effects of spreading probability or immunization strategy on static networks. However, in real systems, the peer-to-peer network structure changes constantly according to frequently social activities of users. In order to capture this dynamical property and study its impact on information spreading, in this paper, a link rewiring strategy based on the Fermi function is introduced. In the present model, the informed individuals tend to break old links and reconnect to their second-order friends with more uninformed neighbors. Simulation results on the susceptible-infected-recovered (SIR) model with fixed recovery time T=1 indicate that the information would spread more faster and broader with the proposed rewiring strategy. Extensive analyses of the information cascade size distribution show that the spreading process of the initial steps plays a very important role, that is to say, the information will spread out if it is still survival at the beginning time. The proposed model may shed some light on the in-depth understanding of information spreading on dynamical social networks.

  9. 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. PMID:15768681

  10. Dynamic Social Networks in Recovery Homes

    PubMed Central

    Jason, Leonard A.; Light, John M.; Stevens, Edward B.; Beers, Kimberly

    2013-01-01

    Acute treatment aftercare in the form of sober living environments—i.e., recovery houses—provide an inexpensive and effective medium-term treatment alternative for many with substance use disorders. Limited evidence suggests that house-situated social relationships and associated social support are critical determinants of how successful these residential experiences are for their members, but little is known about the mechanisms underlying these relationships. This study explored the feasibility of using dynamic social network modeling to understand house-situated longitudinal associations among individual Alcoholics Anonymous (AA) related recovery behaviors, length of residence, dyadic interpersonal trust, and dyadic confidant relationship formation processes. Trust and confidant relationships were measured 3 months apart in U.S. urban-area recovery houses, all of which were part of a network of substance use recovery homes. A stochastic actor-based model was successfully estimated from this data set. Results suggest that confidant relationships are predicted by trust, while trust is affected by recovery behaviors and length of residence. Conceptualizing recovery houses as a set of independent, evolving social networks that can be modeled jointly appears to be a promising direction for research. PMID:24217855

  11. Exploratory community sensing in social networks

    NASA Astrophysics Data System (ADS)

    Khrabrov, Alexy; Stocco, Gabriel; Cybenko, George

    2010-04-01

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

  12. Understanding Classrooms through Social Network Analysis: A Primer for Social Network Analysis in Education Research

    ERIC Educational Resources Information Center

    Grunspan, Daniel Z.; Wiggins, Benjamin L.; Goodreau, Steven M.

    2014-01-01

    Social interactions between students are a major and underexplored part of undergraduate education. Understanding how learning relationships form in undergraduate classrooms, as well as the impacts these relationships have on learning outcomes, can inform educators in unique ways and improve educational reform. Social network analysis (SNA)…

  13. Reciprocity and the Emergence of Power Laws in Social Networks

    NASA Astrophysics Data System (ADS)

    Schnegg, Michael

    Research in network science has shown that many naturally occurring and technologically constructed networks are scale free, that means a power law degree distribution emerges from a growth model in which each new node attaches to the existing network with a probability proportional to its number of links (= degree). Little is known about whether the same principles of local attachment and global properties apply to societies as well. Empirical evidence from six ethnographic case studies shows that complex social networks have significantly lower scaling exponents γ ~ 1 than have been assumed in the past. Apparently humans do not only look for the most prominent players to play with. Moreover cooperation in humans is characterized through reciprocity, the tendency to give to those from whom one has received in the past. Both variables — reciprocity and the scaling exponent — are negatively correlated (r = -0.767, sig = 0.075). If we include this effect in simulations of growing networks, degree distributions emerge that are much closer to those empirically observed. While the proportion of nodes with small degrees decreases drastically as we introduce reciprocity, the scaling exponent is more robust and changes only when a relatively large proportion of attachment decisions follow this rule. If social networks are less scale free than previously assumed this has far reaching implications for policy makers, public health programs and marketing alike.

  14. Factors Determining Nestedness in Complex Networks

    PubMed Central

    Jonhson, Samuel; Domínguez-García, Virginia; Muñoz, Miguel A.

    2013-01-01

    Understanding the causes and effects of network structural features is a key task in deciphering complex systems. In this context, the property of network nestedness has aroused a fair amount of interest as regards ecological networks. Indeed, Bastolla et al. introduced a simple measure of network nestedness which opened the door to analytical understanding, allowing them to conclude that biodiversity is strongly enhanced in highly nested mutualistic networks. Here, we suggest a slightly refined version of such a measure of nestedness and study how it is influenced by the most basic structural properties of networks, such as degree distribution and degree-degree correlations (i.e. assortativity). We find that most of the empirically found nestedness stems from heterogeneity in the degree distribution. Once such an influence has been discounted – as a second factor – we find that nestedness is strongly correlated with disassortativity and hence – as random networks have been recently found to be naturally disassortative – they also tend to be naturally nested just as the result of chance. PMID:24069264

  15. Kinetic analysis of complex metabolic networks

    SciTech Connect

    Stephanopoulos, G.

    1996-12-31

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

  16. Using Social Networks to Create Powerful Learning Communities

    ERIC Educational Resources Information Center

    Lenox, Marianne; Coleman, Maurice

    2010-01-01

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

  17. Differentiating Specialists and Generalists Within College Students' Social Support Networks.

    ERIC Educational Resources Information Center

    Bogat, G. Anne; And Others

    1985-01-01

    An alternative scoring method for the Social Support Questionnaire was used to examine relationships among social network structure, support types and satisfaction determinants. College students' social networks consisted of nuclear, and other, family; friends; and others. Proportion of support network occupied by nuclear family was positively…

  18. Social Networking: Boundaries and Limits Part 1: Ethics

    ERIC Educational Resources Information Center

    Aragon, Antonette; AlDoubi, Suzan; Kaminski, Karen; Anderson, Sharon K.; Isaacs, Nelda

    2014-01-01

    The number of educators, administrators, and institutions that utilize social networking has increased dramatically. Many have adopted social networking in order to be up-to-date and connected with their students' learning beyond the boundaries of the classroom. However, this increase in the use of social networking in academia presents many…

  19. Exploring Educational and Cultural Adaptation through Social Networking Sites

    ERIC Educational Resources Information Center

    Ryan, Sherry D.; Magro, Michael J.; Sharp, Jason H.

    2011-01-01

    Social networking sites have seen tremendous growth and are widely used around the world. Nevertheless, the use of social networking sites in educational contexts is an under explored area. This paper uses a qualitative methodology, autoethnography, to investigate how social networking sites, specifically Facebook[TM], can help first semester…

  20. Rapid innovation diffusion in social networks

    PubMed Central

    Kreindler, Gabriel E.; Young, H. Peyton

    2014-01-01

    Social and technological innovations often spread through social networks as people respond to what their neighbors are doing. Previous research has identified specific network structures, such as local clustering, that promote rapid diffusion. Here we derive bounds that are independent of network structure and size, such that diffusion is fast whenever the payoff gain from the innovation is sufficiently high and the agents’ responses are sufficiently noisy. We also provide a simple method for computing an upper bound on the expected time it takes for the innovation to become established in any finite network. For example, if agents choose log-linear responses to what their neighbors are doing, it takes on average less than 80 revision periods for the innovation to diffuse widely in any network, provided that the error rate is at least 5% and the payoff gain (relative to the status quo) is at least 150%. Qualitatively similar results hold for other smoothed best-response functions and populations that experience heterogeneous payoff shocks. PMID:25024191

  1. Ants (Formicidae): models for social complexity.

    PubMed

    Smith, Chris R; Dolezal, Adam; Eliyahu, Dorit; Holbrook, C Tate; Gadau, Jürgen

    2009-07-01

    The family Formicidae (ants) is composed of more than 12,000 described species that vary greatly in size, morphology, behavior, life history, ecology, and social organization. Ants occur in most terrestrial habitats and are the dominant animals in many of them. They have been used as models to address fundamental questions in ecology, evolution, behavior, and development. The literature on ants is extensive, and the natural history of many species is known in detail. Phylogenetic relationships for the family, as well as within many subfamilies, are known, enabling comparative studies. Their ease of sampling and ecological variation makes them attractive for studying populations and questions relating to communities. Their sociality and variation in social organization have contributed greatly to an understanding of complex systems, division of labor, and chemical communication. Ants occur in colonies composed of tens to millions of individuals that vary greatly in morphology, physiology, and behavior; this variation has been used to address proximate and ultimate mechanisms generating phenotypic plasticity. Relatedness asymmetries within colonies have been fundamental to the formulation and empirical testing of kin and group selection theories. Genomic resources have been developed for some species, and a whole-genome sequence for several species is likely to follow in the near future; comparative genomics in ants should provide new insights into the evolution of complexity and sociogenomics. Future studies using ants should help establish a more comprehensive understanding of social life, from molecules to colonies. PMID:20147200

  2. Social selection and peer influence in an online social network.

    PubMed

    Lewis, Kevin; Gonzalez, Marco; Kaufman, Jason

    2012-01-01

    Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends-except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes. PMID:22184242

  3. Social selection and peer influence in an online social network

    PubMed Central

    Lewis, Kevin; Gonzalez, Marco; Kaufman, Jason

    2012-01-01

    Disentangling the effects of selection and influence is one of social science's greatest unsolved puzzles: Do people befriend others who are similar to them, or do they become more similar to their friends over time? Recent advances in stochastic actor-based modeling, combined with self-reported data on a popular online social network site, allow us to address this question with a greater degree of precision than has heretofore been possible. Using data on the Facebook activity of a cohort of college students over 4 years, we find that students who share certain tastes in music and in movies, but not in books, are significantly likely to befriend one another. Meanwhile, we find little evidence for the diffusion of tastes among Facebook friends—except for tastes in classical/jazz music. These findings shed light on the mechanisms responsible for observed network homogeneity; provide a statistically rigorous assessment of the coevolution of cultural tastes and social relationships; and suggest important qualifications to our understanding of both homophily and contagion as generic social processes. PMID:22184242

  4. Vertical Transmission of Social Roles Drives Resilience to Poaching in Elephant Networks.

    PubMed

    Goldenberg, Shifra Z; Douglas-Hamilton, Iain; Wittemyer, George

    2016-01-11

    Network resilience to perturbation is fundamental to functionality in systems ranging from synthetic communication networks to evolved social organization [1]. While theoretical work offers insight into causes of network robustness, examination of natural networks can identify evolved mechanisms of resilience and how they are related to the selective pressures driving structure. Female African elephants (Loxodonta africana) exhibit complex social networks with node heterogeneity in which older individuals serve as connectivity hubs [2, 3]. Recent ivory poaching targeting older elephants in a well-studied population has mirrored the targeted removal of highly connected nodes in the theoretical literature that leads to structural collapse [4, 5]. Here we tested the response of this natural network to selective knockouts. We find that the hierarchical network topology characteristic of elephant societies was highly conserved across the 16-year study despite ∼70% turnover in individual composition of the population. At a population level, the oldest available individuals persisted to fill socially central positions in the network. For analyses using known mother-daughter pairs, social positions of daughters during the disrupted period were predicted by those of their mothers in years prior, were unrelated to individual histories of family mortality, and were actively built. As such, daughters replicated the social network roles of their mothers, driving the observed network resilience. Our study provides a rare bridge between network theory and an evolved system, demonstrating social redundancy to be the mechanism by which resilience to perturbation occurred in this socially advanced species. PMID:26711491

  5. Motif structure and cooperation in real-world complex networks

    NASA Astrophysics Data System (ADS)

    Salehi, Mostafa; Rabiee, Hamid R.; Jalili, Mahdi

    2010-12-01

    Networks of dynamical nodes serve as generic models for real-world systems in many branches of science ranging from mathematics to physics, technology, sociology and biology. Collective behavior of agents interacting over complex networks is important in many applications. The cooperation between selfish individuals is one of the most interesting collective phenomena. In this paper we address the interplay between the motifs’ cooperation properties and their abundance in a number of real-world networks including yeast protein-protein interaction, human brain, protein structure, email communication, dolphins’ social interaction, Zachary karate club and Net-science coauthorship networks. First, the amount of cooperativity for all possible undirected subgraphs with three to six nodes is calculated. To this end, the evolutionary dynamics of the Prisoner’s Dilemma game is considered and the cooperativity of each subgraph is calculated as the percentage of cooperating agents at the end of the simulation time. Then, the three- to six-node motifs are extracted for each network. The significance of the abundance of a motif, represented by a Z-value, is obtained by comparing them with some properly randomized versions of the original network. We found that there is always a group of motifs showing a significant inverse correlation between their cooperativity amount and Z-value, i.e. the more the Z-value the less the amount of cooperativity. This suggests that networks composed of well-structured units do not have good cooperativity properties.

  6. Universal resilience patterns in complex networks.

    PubMed

    Gao, Jianxi; Barzel, Baruch; Barabási, Albert-László

    2016-02-18

    Resilience, a system's ability to adjust its activity to retain its basic functionality when errors, failures and environmental changes occur, is a defining property of many complex systems. Despite widespread consequences for human health, the economy and the environment, events leading to loss of resilience--from cascading failures in technological systems to mass extinctions in ecological networks--are rarely predictable and are often irreversible. These limitations are rooted in a theoretical gap: the current analytical framework of resilience is designed to treat low-dimensional models with a few interacting components, and is unsuitable for multi-dimensional systems consisting of a large number of components that interact through a complex network. Here we bridge this theoretical gap by developing a set of analytical tools with which to identify the natural control and state parameters of a multi-dimensional complex system, helping us derive effective one-dimensional dynamics that accurately predict the system's resilience. The proposed analytical framework allows us systematically to separate the roles of the system's dynamics and topology, collapsing the behaviour of different networks onto a single universal resilience function. The analytical results unveil the network characteristics that can enhance or diminish resilience, offering ways to prevent the collapse of ecological, biological or economic systems, and guiding the design of technological systems resilient to both internal failures and environmental changes. PMID:26887493

  7. Privacy policies for health social networking sites

    PubMed Central

    Li, Jingquan

    2013-01-01

    Health social networking sites (HSNS), virtual communities where users connect with each other around common problems and share relevant health data, have been increasingly adopted by medical professionals and patients. The growing use of HSNS like Sermo and PatientsLikeMe has prompted public concerns about the risks that such online data-sharing platforms pose to the privacy and security of personal health data. This paper articulates a set of privacy risks introduced by social networking in health care and presents a practical example that demonstrates how the risks might be intrinsic to some HSNS. The aim of this study is to identify and sketch the policy implications of using HSNS and how policy makers and stakeholders should elaborate upon them to protect the privacy of online health data. PMID:23599228

  8. Resolving social dilemmas on evolving random networks

    NASA Astrophysics Data System (ADS)

    Szolnoki, Attila; Perc, Matjaž

    2009-05-01

    We show that strategy-independent adaptations of random interaction networks can induce powerful mechanisms, ranging from the Red Queen to group selection, which promote cooperation in evolutionary social dilemmas. These two mechanisms emerge spontaneously as dynamical processes due to deletions and additions of links, which are performed whenever players adopt new strategies and after a certain number of game iterations, respectively. The potency of cooperation promotion, as well as the mechanism responsible for it, can thereby be tuned via a single parameter determining the frequency of link additions. We thus demonstrate that coevolving random networks may evoke an appropriate mechanism for each social dilemma, such that cooperation prevails even in highly unfavorable conditions.

  9. Detecting complex network modularity by dynamical clustering

    NASA Astrophysics Data System (ADS)

    Boccaletti, S.; Ivanchenko, M.; Latora, V.; Pluchino, A.; Rapisarda, A.

    2007-04-01

    Based on cluster desynchronization properties of phase oscillators, we introduce an efficient method for the detection and identification of modules in complex networks. The performance of the algorithm is tested on computer generated and real-world networks whose modular structure is already known or has been studied by means of other methods. The algorithm attains a high level of precision, especially when the modular units are very mixed and hardly detectable by the other methods, with a computational effort O(KN) on a generic graph with N nodes and K links.

  10. Integrating social networks and human social motives to achieve social influence at scale.

    PubMed

    Contractor, Noshir S; DeChurch, Leslie A

    2014-09-16

    The innovations of science often point to ideas and behaviors that must spread and take root in communities to have impact. Ideas, practices, and behaviors need to go from accepted truths on the part of a few scientists to commonplace beliefs and norms in the minds of the many. Moving from scientific discoveries to public good requires social influence. We introduce a structured influence process (SIP) framework to explain how social networks (i.e., the structure of social influence) and human social motives (i.e., the process of social influence wherein one person's attitudes and behaviors affect another's) are used collectively to enact social influence within a community. The SIP framework advances the science of scientific communication by positing social influence events that consider both the "who" and the "how" of social influence. This framework synthesizes core ideas from two bodies of research on social influence. The first is network research on social influence structures, which identifies who are the opinion leaders and who among their network of peers shapes their attitudes and behaviors. The second is research on social influence processes in psychology, which explores how human social motives such as the need for accuracy or the need for affiliation stimulate behavior change. We illustrate the practical implications of the SIP framework by applying it to the case of reducing neonatal mortality in India. PMID:25225373

  11. Integrating social networks and human social motives to achieve social influence at scale

    PubMed Central

    Contractor, Noshir S.; DeChurch, Leslie A.

    2014-01-01

    The innovations of science often point to ideas and behaviors that must spread and take root in communities to have impact. Ideas, practices, and behaviors need to go from accepted truths on the part of a few scientists to commonplace beliefs and norms in the minds of the many. Moving from scientific discoveries to public good requires social influence. We introduce a structured influence process (SIP) framework to explain how social networks (i.e., the structure of social influence) and human social motives (i.e., the process of social influence wherein one person’s attitudes and behaviors affect another’s) are used collectively to enact social influence within a community. The SIP framework advances the science of scientific communication by positing social influence events that consider both the “who” and the “how” of social influence. This framework synthesizes core ideas from two bodies of research on social influence. The first is network research on social influence structures, which identifies who are the opinion leaders and who among their network of peers shapes their attitudes and behaviors. The second is research on social influence processes in psychology, which explores how human social motives such as the need for accuracy or the need for affiliation stimulate behavior change. We illustrate the practical implications of the SIP framework by applying it to the case of reducing neonatal mortality in India. PMID:25225373

  12. Analysing the Correlation between Social Network Analysis Measures and Performance of Students in Social Network-Based Engineering Education

    ERIC Educational Resources Information Center

    Putnik, Goran; Costa, Eric; Alves, Cátia; Castro, Hélio; Varela, Leonilde; Shah, Vaibhav

    2016-01-01

    Social network-based engineering education (SNEE) is designed and implemented as a model of Education 3.0 paradigm. SNEE represents a new learning methodology, which is based on the concept of social networks and represents an extended model of project-led education. The concept of social networks was applied in the real-life experiment,…

  13. Social Learning Strategies in Networked Groups

    ERIC Educational Resources Information Center

    Wisdom, Thomas N.; Song, Xianfeng; Goldstone, Robert L.

    2013-01-01

    When making decisions, humans can observe many kinds of information about others' activities, but their effects on performance are not well understood. We investigated social learning strategies using a simple problem-solving task in which participants search a complex space, and each can view and imitate others' solutions. Results…

  14. Behavioural phenotype affects social interactions in an animal network

    PubMed Central

    Pike, Thomas W; Samanta, Madhumita; Lindström, Jan; Royle, Nick J

    2008-01-01

    Animal social networks can be extremely complex and are characterized by highly non-random interactions between group members. However, very little is known about the underlying factors affecting interaction preferences, and hence network structure. One possibility is that behavioural differences between individuals, such as how bold or shy they are, can affect the frequency and distribution of their interactions within a network. We tested this using individually marked three-spined sticklebacks (Gasterosteus aculeatus), and found that bold individuals had fewer overall interactions than shy fish, but tended to distribute their interactions more evenly across all group members. Shy fish, on the other hand, tended to associate preferentially with a small number of other group members, leading to a highly skewed distribution of interactions. This was mediated by the reduced tendency of shy fish to move to a new location within the tank when they were interacting with another individual; bold fish showed no such tendency and were equally likely to move irrespective of whether they were interacting or not. The results show that animal social network structure can be affected by the behavioural composition of group members and have important implications for understanding the spread of information and disease in social groups. PMID:18647713

  15. Magnitude Characterization Using Complex Networks in Central Chile

    NASA Astrophysics Data System (ADS)

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

    2013-12-01

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

  16. Post Disaster Governance, Complexity and Network Theory

    PubMed Central

    Lassa, Jonatan A.

    2015-01-01

    This research aims to understand the organizational network typology of large­-scale disaster intervention in developing countries and to understand the complexity of post-­disaster intervention, through the use of network theory based on empirical data from post-­tsunami reconstruction in Aceh, Indonesia, during 2005/­2007. The findings suggest that the ‘ degrees of separation’ (or network diameter) between any two organizations in the field is 5, thus reflecting ‘small­ world’ realities and therefore making no significant difference with the real human networks, as found in previous experiments. There are also significant loops in the network reflecting the fact that some actors tend to not cooperate, which challenges post­ disaster coordination. The findings show the landscape of humanitarian actors is not randomly distributed. Many actors were connected to each other through certain hubs, while hundreds of actors make ‘scattered’ single ‘principal-­client’ links. The paper concludes that by understanding the distribution of degree, centrality, ‘degrees of separation’ and visualization of the network, authorities can improve their understanding of the realities of coordination, from macro to micro scales. PMID:26236562

  17. Modified localized attack on complex network

    NASA Astrophysics Data System (ADS)

    Dong, Gaogao; Du, Ruijin; Hao, Huifang; Tian, Lixin

    2016-01-01

    Since a shell structure contains a wealth of information, it is not only very important for understanding the transport properties of the network, but also essential to identify influential spreaders in complex networks. Nodes within each shell can be classified into two categories: protected nodes and unprotected nodes. In this paper, we propose a generalization of the localized attack, modified localized attack, which means that when a randomly chosen node (root node) is under attack, protected nodes will not be removed, but unprotected nodes in the nearest shells will fail. We numerically and analytically study the system robustness under this attack by taking an Erdös-Rényi (ER) network, a regular random (RR) network and a scale-free (SF) network as examples. Moreover, a fraction of nodes belonging to giant component S and a critical threshold q c , where S approaches to zero, are given. The result implies that increasing connection density has been found to be useful to significantly improve network robustness.

  18. Robust Multiobjective Controllability of Complex Neuronal Networks.

    PubMed

    Tang, Yang; Gao, Huijun; Du, Wei; Lu, Jianquan; Vasilakos, Athanasios V; Kurths, Jurgen

    2016-01-01

    This paper addresses robust multiobjective identification of driver nodes in the neuronal network of a cat's brain, in which uncertainties in determination of driver nodes and control gains are considered. A framework for robust multiobjective controllability is proposed by introducing interval uncertainties and optimization algorithms. By appropriate definitions of robust multiobjective controllability, a robust nondominated sorting adaptive differential evolution (NSJaDE) is presented by means of the nondominated sorting mechanism and the adaptive differential evolution (JaDE). The simulation experimental results illustrate the satisfactory performance of NSJaDE for robust multiobjective controllability, in comparison with six statistical methods and two multiobjective evolutionary algorithms (MOEAs): nondominated sorting genetic algorithms II (NSGA-II) and nondominated sorting composite differential evolution. It is revealed that the existence of uncertainties in choosing driver nodes and designing control gains heavily affects the controllability of neuronal networks. We also unveil that driver nodes play a more drastic role than control gains in robust controllability. The developed NSJaDE and obtained results will shed light on the understanding of robustness in controlling realistic complex networks such as transportation networks, power grid networks, biological networks, etc. PMID:26441452

  19. Qualitative Analysis of Commercial Social Network Profiles

    NASA Astrophysics Data System (ADS)

    Melendez, Lester; Wolfson, Ouri; Adjouadi, Malek; Rishe, Naphtali

    Social-networking sites have become an integral part of many users' daily internet routine. Commercial enterprises have been quick to recognize this and are subsequently creating profiles for many of their products and services. Commercial enterprises use social network profiles to target and interact with potential customers as well as to provide a gateway for users of the product or service to interact with each other. Many commercial enterprises use the statistics from their product or service's social network profile to tout the popularity and success of the product or service being showcased. They will use statistics such as number of friends, number of daily visits, number of interactions, and other similar measurements to quantify their claims. These statistics are often not a clear indication of the true popularity and success of the product. In this chapter the term product is used to refer to any tangible or intangible product, service, celebrity, personality, film, book, or other entity produced by a commercial enterprise.

  20. Defining nodes in complex brain networks

    PubMed Central

    Stanley, Matthew L.; Moussa, Malaak N.; Paolini, Brielle M.; Lyday, Robert G.; Burdette, Jonathan H.; Laurienti, Paul J.

    2013-01-01

    Network science holds great promise for expanding our understanding of the human brain in health, disease, development, and aging. Network analyses are quickly becoming the method of choice for analyzing functional MRI data. However, many technical issues have yet to be confronted in order to optimize results. One particular issue that remains controversial in functional brain network analyses is the definition of a network node. In functional brain networks a node represents some predefined collection of brain tissue, and an edge measures the functional connectivity between pairs of nodes. The characteristics of a node, chosen by the researcher, vary considerably in the literature. This manuscript reviews the current state of the art based on published manuscripts and highlights the strengths and weaknesses of three main methods for defining nodes. Voxel-wise networks are constructed by assigning a node to each, equally sized brain area (voxel). The fMRI time-series recorded from each voxel is then used to create the functional network. Anatomical methods utilize atlases to define the nodes based on brain structure. The fMRI time-series from all voxels within the anatomical area are averaged and subsequently used to generate the network. Functional activation methods rely on data from traditional fMRI activation studies, often from databases, to identify network nodes. Such methods identify the peaks or centers of mass from activation maps to determine the location of the nodes. Small (~10–20 millimeter diameter) spheres located at the coordinates of the activation foci are then applied to the data being used in the network analysis. The fMRI time-series from all voxels in the sphere are then averaged, and the resultant time series is used to generate the network. We attempt to clarify the discussion and move the study of complex brain networks forward. While the “correct” method to be used remains an open, possibly unsolvable question that deserves