Aperiodic dynamics in a deterministic adaptive network model of attitude formation in social groups

NASA Astrophysics Data System (ADS)

Ward, Jonathan A.; Grindrod, Peter

2014-07-01

Adaptive network models, in which node states and network topology coevolve, arise naturally in models of social dynamics that incorporate homophily and social influence. Homophily relates the similarity between pairs of nodes' states to their network coupling strength, whilst social influence causes coupled nodes' states to convergence. In this paper we propose a deterministic adaptive network model of attitude formation in social groups that includes these effects, and in which the attitudinal dynamics are represented by an activato-inhibitor process. We illustrate that consensus, corresponding to all nodes adopting the same attitudinal state and being fully connected, may destabilise via Turing instability, giving rise to aperiodic dynamics with sensitive dependence on initial conditions. These aperiodic dynamics correspond to the formation and dissolution of sub-groups that adopt contrasting attitudes. We discuss our findings in the context of cultural polarisation phenomena. Social influence. This reflects the fact that people tend to modify their behaviour and attitudes in response to the opinions of others [22-26]. We model social influence via diffusion: agents adjust their state according to a weighted sum (dictated by the evolving network) of the differences between their state and the states of their neighbours. Homophily. This relates the similarity of individuals' states to their frequency and strength of interaction [27]. Thus in our model, homophily drives the evolution of the weighted ‘social' network. A precise formulation of our model is given in Section 2. Social influence and homophily underpin models of social dynamics [21], which cover a wide range of sociological phenomena, including the diffusion of innovations [28-32], complex contagions [33-36], collective action [37-39], opinion dynamics [19,20,40,10,11,13,15,41,16], the emergence of social norms [42-44], group stability [45], social differentiation [46] and, of particular relevance here, cultural dissemination [47,12,48].Combining the effects of social influence and homophily naturally gives rise to an adaptive network, since social influence causes the states of agents that are strongly connected to become more similar, while homophily strengthens connections between agents whose states are already similar.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.

The Social Origins of Networks and Diffusion.

PubMed

Centola, Damon

2015-03-01

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

Dynamics of social balance on networks

NASA Astrophysics Data System (ADS)

Antal, T.; Krapivsky, P. L.; Redner, S.

2005-09-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad—a triangular loop with one or three unfriendly links—is reversed to make the triad balanced. With this dynamics, an infinite network undergoes a dynamic phase transition from a steady state to “paradise”—all links are friendly—as the propensity p for friendly links in an update event passes through 1/2 . A finite network always falls into a socially balanced absorbing state where no imbalanced triads remain. If the additional constraint that the number of imbalanced triads in the network not increase in an update is imposed, then the network quickly reaches a balanced final state.

Identifying and tracking dynamic processes in social networks

NASA Astrophysics Data System (ADS)

Chung, Wayne; Savell, Robert; Schütt, Jan-Peter; Cybenko, George

2006-05-01

The detection and tracking of embedded malicious subnets in an active social network can be computationally daunting due to the quantity of transactional data generated in the natural interaction of large numbers of actors comprising a network. In addition, detection of illicit behavior may be further complicated by evasive strategies designed to camouflage the activities of the covert subnet. In this work, we move beyond traditional static methods of social network analysis to develop a set of dynamic process models which encode various modes of behavior in active social networks. These models will serve as the basis for a new application of the Process Query System (PQS) to the identification and tracking of covert dynamic processes in social networks. We present a preliminary result from application of our technique in a real-world data stream-- the Enron email corpus.

Linking Behavior in the Physics Education Research Coauthorship Network

ERIC Educational Resources Information Center

Anderson, Katharine A.; Crespi, Matthew; Sayre, Eleanor C.

2017-01-01

There is considerable long-term interest in understanding the dynamics of collaboration networks, and how these networks form and evolve over time. Most of the work done on the dynamics of social networks focuses on well-established communities. Work examining emerging social networks is rarer, simply because data are difficult to obtain in real…

Simple deterministic models and applications. Comment on "Coupled disease-behavior dynamics on complex networks: A review" by Z. Wang et al.

NASA Astrophysics Data System (ADS)

Yang, Hyun Mo

2015-12-01

Currently, discrete modellings are largely accepted due to the access to computers with huge storage capacity and high performance processors and easy implementation of algorithms, allowing to develop and simulate increasingly sophisticated models. Wang et al. [7] present a review of dynamics in complex networks, focusing on the interaction between disease dynamics and human behavioral and social dynamics. By doing an extensive review regarding to the human behavior responding to disease dynamics, the authors briefly describe the complex dynamics found in the literature: well-mixed populations networks, where spatial structure can be neglected, and other networks considering heterogeneity on spatially distributed populations. As controlling mechanisms are implemented, such as social distancing due 'social contagion', quarantine, non-pharmaceutical interventions and vaccination, adaptive behavior can occur in human population, which can be easily taken into account in the dynamics formulated by networked populations.

Dynamic social networks based on movement

USGS Publications Warehouse

Scharf, Henry; Hooten, Mevin B.; Fosdick, Bailey K.; Johnson, Devin S.; London, Joshua M.; Durban, John W.

2016-01-01

Network modeling techniques provide a means for quantifying social structure in populations of individuals. Data used to define social connectivity are often expensive to collect and based on case-specific, ad hoc criteria. Moreover, in applications involving animal social networks, collection of these data is often opportunistic and can be invasive. Frequently, the social network of interest for a given population is closely related to the way individuals move. Thus, telemetry data, which are minimally invasive and relatively inexpensive to collect, present an alternative source of information. We develop a framework for using telemetry data to infer social relationships among animals. To achieve this, we propose a Bayesian hierarchical model with an underlying dynamic social network controlling movement of individuals via two mechanisms: an attractive effect and an aligning effect. We demonstrate the model and its ability to accurately identify complex social behavior in simulation, and apply our model to telemetry data arising from killer whales. Using auxiliary information about the study population, we investigate model validity and find the inferred dynamic social network is consistent with killer whale ecology and expert knowledge.

Coevolutionary dynamics of opinion propagation and social balance: The key role of small-worldness

NASA Astrophysics Data System (ADS)

Chen, Yan; Chen, Lixue; Sun, Xian; Zhang, Kai; Zhang, Jie; Li, Ping

2014-03-01

The propagation of various opinions in social networks, which influences human inter-relationships and even social structure, and hence is a most important part of social life. We have incorporated social balance into opinion propagation in social networks are influenced by social balance. The edges in networks can represent both friendly or hostile relations, and change with the opinions of individual nodes. We introduce a model to characterize the coevolutionary dynamics of these two dynamical processes on Watts-Strogatz (WS) small-world network. We employ two distinct evolution rules (i) opinion renewal; and (ii) relation adjustment. By changing the rewiring probability, and thus the small-worldness of the WS network, we found that the time for the system to reach balanced states depends critically on both the average path length and clustering coefficient of the network, which is different than other networked process like epidemic spreading. In particular, the system equilibrates most quickly when the underlying network demonstrates strong small-worldness, i.e., small average path lengths and large clustering coefficient. We also find that opinion clusters emerge in the process of the network approaching the global equilibrium, and a measure of global contrariety is proposed to quantify the balanced state of a social network.

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-range communication links are added on top of the random geometric graph, resulting in a "small-world"-like network and yielding a significantly reduced time to reach global agreement. We construct a finite-size scaling analysis for the agreement times in this case. When applying the model of Naming Game on empirical social networks, this stylized agent-based model captures essential features of agreement dynamics in a network of autonomous agents, corresponding to the development of shared classification schemes in a network of artificial agents or opinion spreading and social dynamics in social networks. Our study focuses on the impact that communities in the underlying social graphs have on the outcome of the agreement process. We find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the Naming Game in these networks maintains clusters of coexisting opinions indefinitely. Further, we investigate agent-based network strategies to facilitate convergence to global consensus.

Structures and Dynamics of Social Networks: Selection, Influence, and Self-Organization

ERIC Educational Resources Information Center

Go, Myong-Hyun

2010-01-01

This dissertation studies the social structures and dynamics of human networks: how peers at the micro level and physical environments at the macro level interact with the individual preferences and attributes and shape social dynamics. It is composed of three parts. The first essay, "Friendship Choices and Group Effects in Adolescent…

Random walks on activity-driven networks with attractiveness

NASA Astrophysics Data System (ADS)

Alessandretti, Laura; Sun, Kaiyuan; Baronchelli, Andrea; Perra, Nicola

2017-05-01

Virtually all real-world networks are dynamical entities. In social networks, the propensity of nodes to engage in social interactions (activity) and their chances to be selected by active nodes (attractiveness) are heterogeneously distributed. Here, we present a time-varying network model where each node and the dynamical formation of ties are characterized by these two features. We study how these properties affect random-walk processes unfolding on the network when the time scales describing the process and the network evolution are comparable. We derive analytical solutions for the stationary state and the mean first-passage time of the process, and we study cases informed by empirical observations of social networks. Our work shows that previously disregarded properties of real social systems, such as heterogeneous distributions of activity and attractiveness as well as the correlations between them, substantially affect the dynamical process unfolding on the network.

Active influence in dynamical models of structural balance in social networks

NASA Astrophysics Data System (ADS)

Summers, Tyler H.; Shames, Iman

2013-07-01

We consider a nonlinear dynamical system on a signed graph, which can be interpreted as a mathematical model of social networks in which the links can have both positive and negative connotations. In accordance with a concept from social psychology called structural balance, the negative links play a key role in both the structure and dynamics of the network. Recent research has shown that in a nonlinear dynamical system modeling the time evolution of “friendliness levels” in the network, two opposing factions emerge from almost any initial condition. Here we study active external influence in this dynamical model and show that any agent in the network can achieve any desired structurally balanced state from any initial condition by perturbing its own local friendliness levels. Based on this result, we also introduce a new network centrality measure for signed networks. The results are illustrated in an international-relations network using United Nations voting record data from 1946 to 2008 to estimate friendliness levels amongst various countries.

Complex social contagion makes networks more vulnerable to disease outbreaks.

PubMed

Campbell, Ellsworth; Salathé, Marcel

2013-01-01

Social network analysis is now widely used to investigate the dynamics of infectious disease spread. Vaccination dramatically disrupts disease transmission on a contact network, and indeed, high vaccination rates can potentially halt disease transmission altogether. Here, we build on mounting evidence that health behaviors - such as vaccination, and refusal thereof - can spread across social networks through a process of complex contagion that requires social reinforcement. Using network simulations that model health behavior and infectious disease spread, we find that under otherwise identical conditions, the process by which the health behavior spreads has a very strong effect on disease outbreak dynamics. This dynamic variability results from differences in the topology within susceptible communities that arise during the health behavior spreading process, which in turn depends on the topology of the overall social network. Our findings point to the importance of health behavior spread in predicting and controlling disease outbreaks.

Dynamic social networks promote cooperation in experiments with humans

PubMed Central

Rand, David G.; Arbesman, Samuel; Christakis, Nicholas A.

2011-01-01

Human populations are both highly cooperative and highly organized. Human interactions are not random but rather are structured in social networks. Importantly, ties in these networks often are dynamic, changing in response to the behavior of one's social partners. This dynamic structure permits an important form of conditional action that has been explored theoretically but has received little empirical attention: People can respond to the cooperation and defection of those around them by making or breaking network links. Here, we present experimental evidence of the power of using strategic link formation and dissolution, and the network modification it entails, to stabilize cooperation in sizable groups. Our experiments explore large-scale cooperation, where subjects’ cooperative actions are equally beneficial to all those with whom they interact. Consistent with previous research, we find that cooperation decays over time when social networks are shuffled randomly every round or are fixed across all rounds. We also find that, when networks are dynamic but are updated only infrequently, cooperation again fails. However, when subjects can update their network connections frequently, we see a qualitatively different outcome: Cooperation is maintained at a high level through network rewiring. Subjects preferentially break links with defectors and form new links with cooperators, creating an incentive to cooperate and leading to substantial changes in network structure. Our experiments confirm the predictions of a set of evolutionary game theoretic models and demonstrate the important role that dynamic social networks can play in supporting large-scale human cooperation. PMID:22084103

Structural and Dynamical Patterns on Online Social Networks: The Spanish May 15th Movement as a Case Study

PubMed Central

Borge-Holthoefer, Javier; Rivero, Alejandro; García, Iñigo; Cauhé, Elisa; Ferrer, Alfredo; Ferrer, Darío; Francos, David; Iñiguez, David; Pérez, María Pilar; Ruiz, Gonzalo; Sanz, Francisco; Serrano, Fermín; Viñas, Cristina; Tarancón, Alfonso; Moreno, Yamir

2011-01-01

The number of people using online social networks in their everyday life is continuously growing at a pace never saw before. This new kind of communication has an enormous impact on opinions, cultural trends, information spreading and even in the commercial success of new products. More importantly, social online networks have revealed as a fundamental organizing mechanism in recent country-wide social movements. In this paper, we provide a quantitative analysis of the structural and dynamical patterns emerging from the activity of an online social network around the ongoing May 15th (15M) movement in Spain. Our network is made up by users that exchanged tweets in a time period of one month, which includes the birth and stabilization of the 15M movement. We characterize in depth the growth of such dynamical network and find that it is scale-free with communities at the mesoscale. We also find that its dynamics exhibits typical features of critical systems such as robustness and power-law distributions for several quantities. Remarkably, we report that the patterns characterizing the spreading dynamics are asymmetric, giving rise to a clear distinction between information sources and sinks. Our study represents a first step towards the use of data from online social media to comprehend modern societal dynamics. PMID:21886834

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.

A Network Optimization Approach for Improving Organizational Design

DTIC Science & Technology

2004-01-01

functions, Dynamic Network Analysis, Social Network Analysis Abstract Organizations are frequently designed and redesigned, often in...links between sites on the web. Hence a change in any one of the four networks in which people are involved can potentially result in a cascade of...in terms of a set of networks that open the possibility of using all networks (both social and dynamic network measures) as indicators of potential

Similar but Different: Dynamic Social Network Analysis Highlights Fundamental Differences between the Fission-Fusion Societies of Two Equid Species, the Onager and Grevy’s Zebra

PubMed Central

Rubenstein, Daniel I.; Sundaresan, Siva R.; Fischhoff, Ilya R.; Tantipathananandh, Chayant; Berger-Wolf, Tanya Y.

2015-01-01

Understanding why animal societies take on the form that they do has benefited from insights gained by applying social network analysis to patterns of individual associations. Such analyses typically aggregate data over long time periods even though most selective forces that shape sociality have strong temporal elements. By explicitly incorporating the temporal signal in social interaction data we re-examine the network dynamics of the social systems of the evolutionarily closely-related Grevy’s zebras and wild asses that show broadly similar social organizations. By identifying dynamic communities, previously hidden differences emerge: Grevy’s zebras show more modularity than wild asses and in wild asses most communities consist of solitary individuals; and in Grevy’s zebras, lactating females show a greater propensity to switch communities than non-lactating females and males. Both patterns were missed by static network analyses and in general, adding a temporal dimension provides insights into differences associated with the size and persistence of communities as well as the frequency and synchrony of their formation. Dynamic network analysis provides insights into the functional significance of these social differences and highlights the way dynamic community analysis can be applied to other species. PMID:26488598

Dynamic vs. static social networks in models of parasite transmission: predicting Cryptosporidium spread in wild lemurs.

PubMed

Springer, Andrea; Kappeler, Peter M; Nunn, Charles L

2017-05-01

Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real-world populations. Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (Propithecus verreauxi), and simulated networks to investigate pathogen spread in dynamic vs. static social networks. First, we constructed a susceptible-exposed-infected-recovered model of Cryptosporidium spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks. We found that average outbreak size of Cryptosporidium infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery. Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between-group contact after an infectious agent has been introduced. © 2016 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Modeling cascading failures with the crisis of trust in social networks

NASA Astrophysics Data System (ADS)

Yi, Chengqi; Bao, Yuanyuan; Jiang, Jingchi; Xue, Yibo

2015-10-01

In social networks, some friends often post or disseminate malicious information, such as advertising messages, informal overseas purchasing messages, illegal messages, or rumors. Too much malicious information may cause a feeling of intense annoyance. When the feeling exceeds a certain threshold, it will lead social network users to distrust these friends, which we call the crisis of trust. The crisis of trust in social networks has already become a universal concern and an urgent unsolved problem. As a result of the crisis of trust, users will cut off their relationships with some of their untrustworthy friends. Once a few of these relationships are made unavailable, it is likely that other friends will decline trust, and a large portion of the social network will be influenced. The phenomenon in which the unavailability of a few relationships will trigger the failure of successive relationships is known as cascading failure dynamics. To our best knowledge, no one has formally proposed cascading failures dynamics with the crisis of trust in social networks. In this paper, we address this potential issue, quantify the trust between two users based on user similarity, and model the minimum tolerance with a nonlinear equation. Furthermore, we construct the processes of cascading failures dynamics by considering the unique features of social networks. Based on real social network datasets (Sina Weibo, Facebook and Twitter), we adopt two attack strategies (the highest trust attack (HT) and the lowest trust attack (LT)) to evaluate the proposed dynamics and to further analyze the changes of the topology, connectivity, cascading time and cascade effect under the above attacks. We numerically find that the sparse and inhomogeneous network structure in our cascading model can better improve the robustness of social networks than the dense and homogeneous structure. However, the network structure that seems like ripples is more vulnerable than the other two network structures. Our findings will be useful in further guiding the construction of social networks to effectively avoid the cascading propagation with the crisis of trust. Some research results can help social network service providers to avoid severe cascading failures.

Opinion formation in time-varying social networks: The case of the naming game

NASA Astrophysics Data System (ADS)

Maity, Suman Kalyan; Manoj, T. Venkat; Mukherjee, Animesh

2012-09-01

We study the dynamics of the naming game as an opinion formation model on time-varying social networks. This agent-based model captures the essential features of the agreement dynamics by means of a memory-based negotiation process. Our study focuses on the impact of time-varying properties of the social network of the agents on the naming game dynamics. In particular, we perform a computational exploration of this model using simulations on top of real networks. We investigate the outcomes of the dynamics on two different types of time-varying data: (1) the networks vary on a day-to-day basis and (2) the networks vary within very short intervals of time (20 sec). In the first case, we find that networks with strong community structure hinder the system from reaching global agreement; the evolution of the naming game in these networks maintains clusters of coexisting opinions indefinitely leading to metastability. In the second case, we investigate the evolution of the naming game in perfect synchronization with the time evolution of the underlying social network shedding new light on the traditional emergent properties of the game that differ largely from what has been reported in the existing literature.

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.

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

Evolutionary Games in Multi-Agent Systems of Weighted Social Networks

NASA Astrophysics Data System (ADS)

Du, Wen-Bo; Cao, Xian-Bin; Zheng, Hao-Ran; Zhou, Hong; Hu, Mao-Bin

Much empirical evidence has shown realistic networks are weighted. Compared with those on unweighted networks, the dynamics on weighted network often exhibit distinctly different phenomena. In this paper, we investigate the evolutionary game dynamics (prisoner's dilemma game and snowdrift game) on a weighted social network consisted of rational agents and focus on the evolution of cooperation in the system. Simulation results show that the cooperation level is strongly affected by the weighted nature of the network. Moreover, the variation of time series has also been investigated. Our work may be helpful in understanding the cooperative behavior in the social systems.

Dynamics of deceptive interactions in social networks.

PubMed

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

2015-11-06

In this paper, we examine the role of lies in human social relations by implementing some salient characteristics of deceptive interactions into an opinion formation model, so as to describe the dynamical behaviour of a social network more realistically. In this model, we take into account such basic properties of social networks as the dynamics of the intensity of interactions, the influence of public opinion and the fact that in every human interaction it might be convenient to deceive or withhold information depending on the instantaneous situation of each individual in the network. We find that lies shape the topology of social networks, especially the formation of tightly linked, small communities with loose connections between them. We also find that agents with a larger proportion of deceptive interactions are the ones that connect communities of different opinion, and, in this sense, they have substantial centrality in the network. We then discuss the consequences of these results for the social behaviour of humans and predict the changes that could arise due to a varying tolerance for lies in society. © 2015 The Author(s).

Static, Dynamic and Semantic Dimensions: Towards a Multidisciplinary Approach of Social Networks Analysis

NASA Astrophysics Data System (ADS)

Thovex, Christophe; Trichet, Francky

The objective of our work is to extend static and dynamic models of Social Networks Analysis (SNA), by taking conceptual aspects of enterprises and institutions social graph into account. The originality of our multidisciplinary work is to introduce abstract notions of electro-physic to define new measures in SNA, for new decision-making functions dedicated to Human Resource Management (HRM). This paper introduces a multidimensional system and new measures: (1) a tension measure for social network analysis, (2) an electrodynamic, predictive and semantic system for recommendations on social graphs evolutions and (3) a reactance measure used to evaluate the individual stress at work of the members of a social network.

Pattern Analysis in Social Networks with Dynamic Connections

NASA Astrophysics Data System (ADS)

Wu, Yu; Zhang, Yu

In this paper, we explore how decentralized local interactions of autonomous agents in a network relate to collective behaviors. Most existing work in this area models social network in which agent relations are fixed; instead, we focus on dynamic social networks where agents can rationally adjust their neighborhoods based on their individual interests. We propose a new connection evaluation rule called the Highest Weighted Reward (HWR) rule, with which agents dynamically choose their neighbors in order to maximize their own utilities based on the rewards from previous interactions. Our experiments show that in the 2-action pure coordination game, our system will stabilize to a clustering state where all relationships in the network are rewarded with the optimal payoff. Our experiments also reveal additional interesting patterns in the network.

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

PubMed Central

Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D.

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in “real-time”) and forecasting (predicting the future) ILI dynamics in the 2011 – 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets. PMID:29244814

Forecasting influenza-like illness dynamics for military populations using neural networks and social media.

PubMed

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; Corley, Courtney D

2017-01-01

This work is the first to take advantage of recurrent neural networks to predict influenza-like illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data and the state-of-the-art machine learning models, we build and evaluate the predictive power of neural network architectures based on Long Short Term Memory (LSTMs) units capable of nowcasting (predicting in "real-time") and forecasting (predicting the future) ILI dynamics in the 2011 - 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, embeddings, word ngrams, stylistic patterns, and communication behavior using hashtags and mentions. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks using a diverse set of evaluation metrics. Finally, we combine ILI and social media signals to build a joint neural network model for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance, specifically for military rather than general populations in 26 U.S. and six international locations., and analyze how model performance depends on the amount of social media data available per location. Our approach demonstrates several advantages: (a) Neural network architectures that rely on LSTM units trained on social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than stylistic and topic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns. (g) Model performance improves with more tweets available per geo-location e.g., the error gets lower and the Pearson score gets higher for locations with more tweets.

Exploring Classroom Interaction with Dynamic Social Network Analysis

ERIC Educational Resources Information Center

Bokhove, Christian

2018-01-01

This article reports on an exploratory project in which technology and dynamic social network analysis (SNA) are used for modelling classroom interaction. SNA focuses on the links between social actors, draws on graphic imagery to reveal and display the patterning of those links, and develops mathematical and computational models to describe and…

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE PAGES

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine; ...

2017-12-15

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Forecasting influenza-like illness dynamics for military populations using neural networks and social media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Volkova, Svitlana; Ayton, Ellyn; Porterfield, Katherine

This work is the first to take advantage of recurrent neural networks to predict influenza-like-illness (ILI) dynamics from various linguistic signals extracted from social media data. Unlike other approaches that rely on timeseries analysis of historical ILI data [1, 2] and the state-of-the-art machine learning models [3, 4], we build and evaluate the predictive power of Long Short Term Memory (LSTMs) architectures capable of nowcasting (predicting in \\real-time") and forecasting (predicting the future) ILI dynamics in the 2011 { 2014 influenza seasons. To build our models we integrate information people post in social media e.g., topics, stylistic and syntactic patterns,more » emotions and opinions, and communication behavior. We then quantitatively evaluate the predictive power of different social media signals and contrast the performance of the-state-of-the-art regression models with neural networks. Finally, we combine ILI and social media signals to build joint neural network models for ILI dynamics prediction. Unlike the majority of the existing work, we specifically focus on developing models for local rather than national ILI surveillance [1], specifically for military rather than general populations [3] in 26 U.S. and six international locations. Our approach demonstrates several advantages: (a) Neural network models learned from social media data yield the best performance compared to previously used regression models. (b) Previously under-explored language and communication behavior features are more predictive of ILI dynamics than syntactic and stylistic signals expressed in social media. (c) Neural network models learned exclusively from social media signals yield comparable or better performance to the models learned from ILI historical data, thus, signals from social media can be potentially used to accurately forecast ILI dynamics for the regions where ILI historical data is not available. (d) Neural network models learned from combined ILI and social media signals significantly outperform models that rely solely on ILI historical data, which adds to a great potential of alternative public sources for ILI dynamics prediction. (e) Location-specific models outperform previously used location-independent models e.g., U.S. only. (f) Prediction results significantly vary across geolocations depending on the amount of social media data available and ILI activity patterns.« less

Towards a dynamic social-network-based approach for service composition in the Internet of Things

NASA Astrophysics Data System (ADS)

Xu, Wen; Hu, Zheng; Gong, Tao; Zhao, Zhengzheng

2011-12-01

The User-Generated Service (UGS) concept allows end-users to create their own services as well as to share and manage the lifecycles of these services. The current development of the Internet-of-Things (IoT) has brought new challenges to the UGS area. Creating smart services in the IoT environment requires a dynamic social network that considers the relationship between people and things. In this paper, we consider the know-how required to best organize exchanges between users and things to enhance service composition. By surveying relevant aspects including service composition technology, social networks and a recommendation system, we present the first concept of our framework to provide recommendations for a dynamic social network-based means to organize UGSs in the IoT.

Establishing the reliability of rhesus macaque social network assessment from video observations

PubMed Central

Feczko, Eric; Mitchell, Thomas A. J.; Walum, Hasse; Brooks, Jenna M.; Heitz, Thomas R.; Young, Larry J.; Parr, Lisa A.

2015-01-01

Understanding the properties of a social environment is important for understanding the dynamics of social relationships. Understanding such dynamics is relevant for multiple fields, ranging from animal behaviour to social and cognitive neuroscience. To quantify social environment properties, recent studies have incorporated social network analysis. Social network analysis quantifies both the global and local properties of a social environment, such as social network efficiency and the roles played by specific individuals, respectively. Despite the plethora of studies incorporating social network analysis, methods to determine the amount of data necessary to derive reliable social networks are still being developed. Determining the amount of data necessary for a reliable network is critical for measuring changes in the social environment, for example following an experimental manipulation, and therefore may be critical for using social network analysis to statistically assess social behaviour. In this paper, we extend methods for measuring error in acquired data and for determining the amount of data necessary to generate reliable social networks. We derived social networks from a group of 10 male rhesus macaques, Macaca mulatta, for three behaviours: spatial proximity, grooming and mounting. Behaviours were coded using a video observation technique, where video cameras recorded the compound where the 10 macaques resided. We collected, coded and used 10 h of video data to construct these networks. Using the methods described here, we found in our data that 1 h of spatial proximity observations produced reliable social networks. However, this may not be true for other studies due to differences in data acquisition. Our results have broad implications for measuring and predicting the amount of error in any social network, regardless of species. PMID:26392632

A coevolving model based on preferential triadic closure for social media networks

PubMed Central

Li, Menghui; Zou, Hailin; Guan, Shuguang; Gong, Xiaofeng; Li, Kun; Di, Zengru; Lai, Choy-Heng

2013-01-01

The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations. PMID:23979061

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

PubMed Central

Almquist, Zack W.; Butts, Carter T.

2015-01-01

Change in group size and composition has long been an important area of research in the social sciences. Similarly, interest in interaction dynamics has a long history in sociology and social psychology. However, the effects of endogenous group change on interaction dynamics are a surprisingly understudied area. One way to explore these relationships is through social network models. Network dynamics may be viewed as a process of change in the edge structure of a network, in the vertex set on which edges are defined, or in both simultaneously. Although early studies of such processes were primarily descriptive, recent work on this topic has increasingly turned to formal statistical models. Although showing great promise, many of these modern dynamic models are computationally intensive and scale very poorly in the size of the network under study and/or the number of time points considered. Likewise, currently used models focus on edge dynamics, with little support for endogenously changing vertex sets. Here, the authors show how an existing approach based on logistic network regression can be extended to serve as a highly scalable framework for modeling large networks with dynamic vertex sets. The authors place this approach within a general dynamic exponential family (exponential-family random graph modeling) context, clarifying the assumptions underlying the framework (and providing a clear path for extensions), and they show how model assessment methods for cross-sectional networks can be extended to the dynamic case. Finally, the authors illustrate this approach on a classic data set involving interactions among windsurfers on a California beach. PMID:26120218

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

PubMed

Almquist, Zack W; Butts, Carter T

2014-08-01

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

Using big data to map the network organization of the brain.

PubMed

Swain, James E; Sripada, Chandra; Swain, John D

2014-02-01

The past few years have shown a major rise in network analysis of "big data" sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension - individuality versus sociality - might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization.

Using big data to map the network organization of the brain

PubMed Central

Swain, James E.; Sripada, Chandra; Swain, John D.

2015-01-01

The past few years have shown a major rise in network analysis of “big data” sets in the social sciences, revealing non-obvious patterns of organization and dynamic principles. We speculate that the dependency dimension – individuality versus sociality – might offer important insights into the dynamics of neurons and neuronal ensembles. Connectomic neural analyses, informed by social network theory, may be helpful in understanding underlying fundamental principles of brain organization. PMID:24572243

Modelling opinion formation driven communities in social networks

NASA Astrophysics Data System (ADS)

Iñiguez, Gerardo; Barrio, Rafael A.; Kertész, János; Kaski, Kimmo K.

2011-09-01

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

Strategic tradeoffs in competitor dynamics on adaptive networks.

PubMed

Hébert-Dufresne, Laurent; Allard, Antoine; Noël, Pierre-André; Young, Jean-Gabriel; Libby, Eric

2017-08-08

Recent empirical work highlights the heterogeneity of social competitions such as political campaigns: proponents of some ideologies seek debate and conversation, others create echo chambers. While symmetric and static network structure is typically used as a substrate to study such competitor dynamics, network structure can instead be interpreted as a signature of the competitor strategies, yielding competition dynamics on adaptive networks. Here we demonstrate that tradeoffs between aggressiveness and defensiveness (i.e., targeting adversaries vs. targeting like-minded individuals) creates paradoxical behaviour such as non-transitive dynamics. And while there is an optimal strategy in a two competitor system, three competitor systems have no such solution; the introduction of extreme strategies can easily affect the outcome of a competition, even if the extreme strategies have no chance of winning. Not only are these results reminiscent of classic paradoxical results from evolutionary game theory, but the structure of social networks created by our model can be mapped to particular forms of payoff matrices. Consequently, social structure can act as a measurable metric for social games which in turn allows us to provide a game theoretical perspective on online political debates.

Social network based dynamic transit service through the OMITS system.

DOT National Transportation Integrated Search

2014-02-01

The Open Mode Integrated Transportation System (OMITS) forms a sustainable information infrastructure for communication within and between the mobile/Internet network, the roadway : network, and the users social network. It manipulates the speed g...

Volunteerism: Social Network Dynamics and Education

PubMed Central

Ajrouch, Kristine J.; Antonucci, Toni C.; Webster, Noah J.

2016-01-01

Objectives . We examine how changes in social networks influence volunteerism through bridging (diversity) and bonding (spending time) mechanisms. We further investigate whether social network change substitutes or amplifies the effects of education on volunteerism. Methods . Data (n = 543) are drawn from a two-wave survey of Social Relations and Health over the Life Course (SRHLC). Zero-inflated negative binomial regressions were conducted to test competing hypotheses about how changes in social network characteristics alone and in conjunction with education level predict likelihood and frequency of volunteering. Results . Changes in social networks were associated with volunteerism: as the proportion of family members decreased and the average number of network members living within a one-hour drive increased over time, participants reported higher odds of volunteering. The substitution hypothesis was supported: social networks that exhibited more geographic proximity and greater contact frequency over-time compensated for lower levels of education to predict volunteering more hours. Discussion . The dynamic role of social networks and the ways in which they may work through bridging and bonding to influence both likelihood and frequency of volunteering are discussed. The potential benefits of volunteerism in light of longer life expectancies and smaller families are also considered. PMID:25512570

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

NASA Astrophysics Data System (ADS)

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-08-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure.

PubMed

Schleussner, Carl-Friedrich; Donges, Jonathan F; Engemann, Denis A; Levermann, Anders

2016-08-11

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking.

Modeling Social Capital as Dynamic Networks to Promote Access to Oral Healthcare

PubMed Central

Northridge, Mary E.; Kunzel, Carol; Zhang, Qiuyi; Kum, Susan S.; Gilbert, Jessica L.; Jin, Zhu; Metcalf, Sara S.

2016-01-01

Social capital, as comprised of human connections in social networks and their associated benefits, is closely related to the health of individuals, communities, and societies at large. For disadvantaged population groups such as older adults and racial/ethnic minorities, social capital may play a particularly critical role in mitigating the negative effects and reinforcing the positive effects on health. In this project, we model social capital as both cause and effect by simulating dynamic networks. Informed in part by a community-based health promotion program, an agent-based model is contextualized in a GIS environment to explore the complexity of social disparities in oral and general health as experienced at the individual, interpersonal, and community scales. This study provides the foundation for future work investigating how health and healthcare accessibility may be influenced by social networks. PMID:27668298

Modeling Social Capital as Dynamic Networks to Promote Access to Oral Healthcare.

PubMed

Wang, Hua; Northridge, Mary E; Kunzel, Carol; Zhang, Qiuyi; Kum, Susan S; Gilbert, Jessica L; Jin, Zhu; Metcalf, Sara S

2016-01-01

Social capital, as comprised of human connections in social networks and their associated benefits, is closely related to the health of individuals, communities, and societies at large. For disadvantaged population groups such as older adults and racial/ethnic minorities, social capital may play a particularly critical role in mitigating the negative effects and reinforcing the positive effects on health. In this project, we model social capital as both cause and effect by simulating dynamic networks. Informed in part by a community-based health promotion program, an agent-based model is contextualized in a GIS environment to explore the complexity of social disparities in oral and general health as experienced at the individual, interpersonal, and community scales. This study provides the foundation for future work investigating how health and healthcare accessibility may be influenced by social networks.

Social influence and bullying behavior: intervention-based network dynamics of the fairplayer.manual bullying prevention program.

PubMed

Wölfer, Ralf; Scheithauer, Herbert

2014-01-01

Bullying is a social phenomenon and although preventive interventions consequently address social mechanisms, evaluations hardly consider the complexity of peer processes. Therefore, the present study analyzes the efficacy of the fairplayer.manual bullying prevention program from a social network perspective. Within a pretest-posttest control group design, longitudinal data were available from 328 middle-school students (MAge  = 13.7 years; 51% girls), who provided information on bullying behavior and interaction patterns. The revealed network parameters were utilized to examine the network change (MANCOVA) and the network dynamics (SIENA). Across both forms of analyses, findings revealed the hypothesized intervention-based decrease of bullies' social influence. Hence the present bullying prevention program, as one example of programs that successfully addresses both individual skills and social mechanisms, demonstrates the desired effect of reducing contextual opportunities for the exhibition of bullying behavior. © 2014 Wiley Periodicals, Inc.

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.

From seconds to months: an overview of multi-scale dynamics of mobile telephone calls

NASA Astrophysics Data System (ADS)

Saramäki, Jari; Moro, Esteban

2015-06-01

Big Data on electronic records of social interactions allow approaching human behaviour and sociality from a quantitative point of view with unforeseen statistical power. Mobile telephone Call Detail Records (CDRs), automatically collected by telecom operators for billing purposes, have proven especially fruitful for understanding one-to-one communication patterns as well as the dynamics of social networks that are reflected in such patterns. We present an overview of empirical results on the multi-scale dynamics of social dynamics and networks inferred from mobile telephone calls. We begin with the shortest timescales and fastest dynamics, such as burstiness of call sequences between individuals, and "zoom out" towards longer temporal and larger structural scales, from temporal motifs formed by correlated calls between multiple individuals to long-term dynamics of social groups. We conclude this overview with a future outlook.

Prototype of a Mobile Social Network for Education Using Dynamic Web Service

ERIC Educational Resources Information Center

Hoentsch, Sandra Costa Pinto; Carvalho, Felipe Oliveira; Santos, Luiz Marcus Monteiro de Almeida; Ribeiro, Admilson de Ribamar Lima

2012-01-01

This article presents the proposal of a social network site SocialNetLab that belongs to the Department of Computing-Federal University of Sergipe and which aims to locate and notify users of a nearby friend independently of the location technology available in the equipment through dynamic Web Service; to serve as a laboratory for research in…

Exploring Peer Relationships, Friendships and Group Work Dynamics in Higher Education: Applying Social Network Analysis

ERIC Educational Resources Information Center

Mamas, Christoforos

2018-01-01

This study primarily applied social network analysis (SNA) to explore the relationship between friendships, peer social interactions and group work dynamics within a higher education undergraduate programme in England. A critical case study design was adopted so as to allow for an in-depth exploration of the students' voice. In doing so, the views…

MSEE: Stochastic Cognitive Linguistic Behavior Models for Semantic Sensing

DTIC Science & Technology

2013-09-01

recognition, a Gaussian Process Dynamic Model with Social Network Analysis (GPDM-SNA) for a small human group action recognition, an extended GPDM-SNA...44  3.2. Small Human Group Activity Modeling Based on Gaussian Process Dynamic Model and Social Network Analysis (SN-GPDM...51  Approved for public release; distribution unlimited. 3 3.2.3. Gaussian Process Dynamical Model and

Harvesting Ego-Network Data from Facebook: Using the CEMAP Facebook Profile in ORA

DTIC Science & Technology

2009-02-02

Keywords: Facebook , CEMAP, social network , ORA, dynamic network analysis Abstract...The Facebook social networking site (www.facebook.com) has become a popular phenomenon over the past five years. By its nature, Facebook has...tableset. The Facebook tableset is the CEMAP abstraction of the various levels of technology to harvest the social network data, via the Facebook developer

Smoking Behavior and Friendship Formation: The Importance of Time Heterogeneity in Studying Social Network Dynamics

DTIC Science & Technology

2010-01-01

Smoking Behavior and Friendship Formation: The Importance of Time Heterogeneity in Studying Social Network Dynamics Joshua A. Lospinoso Department of...djsatchell@gmail.com Abstract—This study illustrates the importance of assessing and accounting for time heterogeneity in longitudinal social net- work...analysis. We apply the time heterogeneity model selection procedure of [1] to a dataset collected on social tie formation for university freshman in the

Experimental resource pulses influence social-network dynamics and the potential for information flow in tool-using crows

PubMed Central

St Clair, James J. H.; Burns, Zackory T.; Bettaney, Elaine M.; Morrissey, Michael B.; Otis, Brian; Ryder, Thomas B.; Fleischer, Robert C.; James, Richard; Rutz, Christian

2015-01-01

Social-network dynamics have profound consequences for biological processes such as information flow, but are notoriously difficult to measure in the wild. We used novel transceiver technology to chart association patterns across 19 days in a wild population of the New Caledonian crow—a tool-using species that may socially learn, and culturally accumulate, tool-related information. To examine the causes and consequences of changing network topology, we manipulated the environmental availability of the crows' preferred tool-extracted prey, and simulated, in silico, the diffusion of information across field-recorded time-ordered networks. Here we show that network structure responds quickly to environmental change and that novel information can potentially spread rapidly within multi-family communities, especially when tool-use opportunities are plentiful. At the same time, we report surprisingly limited social contact between neighbouring crow communities. Such scale dependence in information-flow dynamics is likely to influence the evolution and maintenance of material cultures. PMID:26529116

Linking Individual and Collective Behavior in Adaptive Social Networks

NASA Astrophysics Data System (ADS)

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

2016-03-01

Adaptive social structures are known to promote the evolution of cooperation. However, up to now the characterization of the collective, population-wide dynamics resulting from the self-organization of individual strategies on a coevolving, adaptive network has remained unfeasible. Here we establish a (reversible) link between individual (micro)behavior and collective (macro)behavior for coevolutionary processes. We demonstrate that an adaptive network transforms a two-person social dilemma locally faced by individuals into a collective dynamics that resembles that associated with an N -person coordination game, whose characterization depends sensitively on the relative time scales between the entangled behavioral and network evolutions. In particular, we show that the faster the relative rate of adaptation of the network, the smaller the critical fraction of cooperators required for cooperation to prevail, thus establishing a direct link between network adaptation and the evolution of cooperation. The framework developed here is general and may be readily applied to other dynamical processes occurring on adaptive networks, notably, the spreading of contagious diseases or the diffusion of innovations.

Social network approaches to recruitment, HIV prevention, medical care, and medication adherence.

PubMed

Latkin, Carl A; Davey-Rothwell, Melissa A; Knowlton, Amy R; Alexander, Kamila A; Williams, Chyvette T; Boodram, Basmattee

2013-06-01

This article reviews the current issues and advancements in social network approaches to HIV prevention and care. Social network analysis can provide a method to understand health disparities in HIV rates, treatment access, and outcomes. Social network analysis is a valuable tool to link social structural factors to individual behaviors. Social networks provide an avenue for low-cost and sustainable HIV prevention interventions that can be adapted and translated into diverse populations. Social networks can be utilized as a viable approach to recruitment for HIV testing and counseling, HIV prevention interventions, optimizing HIV medical care, and medication adherence. Social network interventions may be face-to-face or through social media. Key issues in designing social network interventions are contamination due to social diffusion, network stability, density, and the choice and training of network members. There are also ethical issues involved in the development and implementation of social network interventions. Social network analyses can also be used to understand HIV transmission dynamics.

Social Balance on Networks: The Dynamics of Friendship and Hatred

NASA Astrophysics Data System (ADS)

Redner, Sidney

2006-03-01

We study the evolution of social networks that contain both friendly and unfriendly pairwise links between individual nodes. The network is endowed with dynamics in which the sense of a link in an imbalanced triad---a triangular loop with 1 or 3 unfriendly links---is reversed to make the triad balanced. Thus an imbalanced triad is analogous to a frustrated plaquette in a random magnet, while a balanced triad fulfills the adage: ``a friend of my friend is my friend; an enemy of my friend is my enemy; a friend of my enemy is my enemy; an enemy of my enemy is my friend.'' With this frustration-reducing dynamics, an infinite network undergoes a dynamic phase transition from a steady state to ``paradise''---all links are friendly---as the propensity for friendly links to be created in an update event passes through 1/2. On the other hand, a finite network always falls into a socially-balanced absorbing state where no imbalanced triads remain. A prominent example of the achievement of social balance is the evolution of pacts and treaties between various European countries during the late 1800's and early 1900's. Here social balance gave rise to the two major alliances that comprised the protagonists of World War I.

Adaptive Network Dynamics - Modeling and Control of Time-Dependent Social Contacts

PubMed Central

Schwartz, Ira B.; Shaw, Leah B.; Shkarayev, Maxim S.

2013-01-01

Real networks consisting of social contacts do not possess static connections. That is, social connections may be time dependent due to a variety of individual behavioral decisions based on current network connections. Examples of adaptive networks occur in epidemics, where information about infectious individuals may change the rewiring of healthy people, or in the recruitment of individuals to a cause or fad, where rewiring may optimize recruitment of susceptible individuals. In this paper, we will review some of the dynamical properties of adaptive networks, and show how they predict novel phenomena as well as yield insight into new controls. The applications will be control of epidemic outbreaks and terrorist recruitment modeling. PMID:25414913

Modeling Epidemics Spreading on Social Contact Networks.

PubMed

Zhang, Zhaoyang; Wang, Honggang; Wang, Chonggang; Fang, Hua

2015-09-01

Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion.

Modeling Epidemics Spreading on Social Contact Networks

PubMed Central

ZHANG, ZHAOYANG; WANG, HONGGANG; WANG, CHONGGANG; FANG, HUA

2016-01-01

Social contact networks and the way people interact with each other are the key factors that impact on epidemics spreading. However, it is challenging to model the behavior of epidemics based on social contact networks due to their high dynamics. Traditional models such as susceptible-infected-recovered (SIR) model ignore the crowding or protection effect and thus has some unrealistic assumption. In this paper, we consider the crowding or protection effect and develop a novel model called improved SIR model. Then, we use both deterministic and stochastic models to characterize the dynamics of epidemics on social contact networks. The results from both simulations and real data set conclude that the epidemics are more likely to outbreak on social contact networks with higher average degree. We also present some potential immunization strategies, such as random set immunization, dominating set immunization, and high degree set immunization to further prove the conclusion. PMID:27722037

Emergence of communities and diversity in social networks

PubMed Central

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

2017-01-01

Communities are common in complex networks and play a significant role in the functioning of social, biological, economic, and technological systems. Despite widespread interest in detecting community structures in complex networks and exploring the effect of communities on collective dynamics, a deep understanding of the emergence and prevalence of communities in social networks is still lacking. Addressing this fundamental problem is of paramount importance in understanding, predicting, and controlling a variety of collective behaviors in society. An elusive question is how communities with common internal properties arise in social networks with great individual diversity. Here, we answer this question using the ultimatum game, which has been a paradigm for characterizing altruism and fairness. We experimentally show that stable local communities with different internal agreements emerge spontaneously and induce social diversity into networks, which is in sharp contrast to populations with random interactions. Diverse communities and social norms come from the interaction between responders with inherent heterogeneous demands and rational proposers via local connections, where the former eventually become the community leaders. This result indicates that networks are significant in the emergence and stabilization of communities and social diversity. Our experimental results also provide valuable information about strategies for developing network models and theories of evolutionary games and social dynamics. PMID:28235785

Volunteerism: Social Network Dynamics and Education.

PubMed

Ajrouch, Kristine J; Antonucci, Toni C; Webster, Noah J

2016-03-01

. We examine how changes in social networks influence volunteerism through bridging (diversity) and bonding (spending time) mechanisms. We further investigate whether social network change substitutes or amplifies the effects of education on volunteerism. . Data (n = 543) are drawn from a two-wave survey of Social Relations and Health over the Life Course (SRHLC). Zero-inflated negative binomial regressions were conducted to test competing hypotheses about how changes in social network characteristics alone and in conjunction with education level predict likelihood and frequency of volunteering. . Changes in social networks were associated with volunteerism: as the proportion of family members decreased and the average number of network members living within a one-hour drive increased over time, participants reported higher odds of volunteering. The substitution hypothesis was supported: social networks that exhibited more geographic proximity and greater contact frequency over-time compensated for lower levels of education to predict volunteering more hours. . The dynamic role of social networks and the ways in which they may work through bridging and bonding to influence both likelihood and frequency of volunteering are discussed. The potential benefits of volunteerism in light of longer life expectancies and smaller families are also considered. © The Author 2014. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Emergence of communities and diversity in social networks.

PubMed

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

2017-03-14

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

Smart social adaptation prevents catastrophic ecological regime shifts in networks of myopic harvesters

NASA Astrophysics Data System (ADS)

Donges, Jonathan; Lucht, Wolfgang; Wiedermann, Marc; Heitzig, Jobst; Kurths, Jürgen

2015-04-01

In the anthropocene, the rise of global social and economic networks with ever increasing connectivity and speed of interactions, e.g., the internet or global financial markets, is a key challenge for sustainable development. The spread of opinions, values or technologies on these networks, in conjunction with the coevolution of the network structures themselves, underlies nexuses of current concern such as anthropogenic climate change, biodiversity loss or global land use change. To isolate and quantitatively study the effects and implications of network dynamics for sustainable development, we propose an agent-based model of information flow on adaptive networks between myopic harvesters that exploit private renewable resources. In this conceptual model of a network of socio-ecological systems, information on management practices flows between agents via boundedly rational imitation depending on the state of the resource stocks involved in an interaction. Agents can also adapt the structure of their social network locally by preferentially connecting to culturally similar agents with identical management practices and, at the same time, disconnecting from culturally dissimilar agents. Investigating in detail the statistical mechanics of this model, we find that an increasing rate of information flow through faster imitation dynamics or growing density of network connectivity leads to a marked increase in the likelihood of environmental resource collapse. However, we show that an optimal rate of social network adaptation can mitigate this negative effect without loss of social cohesion through network fragmentation. Our results highlight that seemingly immaterial network dynamics of spreading opinions or values can be of large relevance for the sustainable management of socio-ecological systems and suggest smartly conservative network adaptation as a strategy for mitigating environmental collapse. Hence, facing the great acceleration, these network dynamics should be more routinely incorporated in standard models of economic development or integrated assessment models used for evaluating anthropogenic climate change.

A stochastic agent-based model of pathogen propagation in dynamic multi-relational social networks

PubMed Central

Khan, Bilal; Dombrowski, Kirk; Saad, Mohamed

2015-01-01

We describe a general framework for modeling and stochastic simulation of epidemics in realistic dynamic social networks, which incorporates heterogeneity in the types of individuals, types of interconnecting risk-bearing relationships, and types of pathogens transmitted across them. Dynamism is supported through arrival and departure processes, continuous restructuring of risk relationships, and changes to pathogen infectiousness, as mandated by natural history; dynamism is regulated through constraints on the local agency of individual nodes and their risk behaviors, while simulation trajectories are validated using system-wide metrics. To illustrate its utility, we present a case study that applies the proposed framework towards a simulation of HIV in artificial networks of intravenous drug users (IDUs) modeled using data collected in the Social Factors for HIV Risk survey. PMID:25859056

Association, roost use and simulated disruption of Myotis septentrionalis maternity colonies

USGS Publications Warehouse

Silvis, Alexander; Ford, W. Mark; Britzke, Eric R.; Johnson, Joshua B.

2014-01-01

How wildlife social and resource networks are distributed on the landscape and how animals respond to resource loss are important aspects of behavioral ecology. For bats, understanding these responses may improve conservation efforts and provide insights into adaptations to environmental conditions. We tracked maternity colonies of northern bats (Myotis septentrionalis) at Fort Knox, Kentucky, USA to evaluate their social and resource networks and space use. Roost and social network structure differed between maternity colonies. Overall roost availability did not appear to be strongly related to network characteristics or space use. In simulations for our two largest networks, roost removal was related linearly to network fragmentation; despite this, networks were relatively robust, requiring removal of >20% of roosts to cause network fragmentation. Results from our analyses indicate that northern bat behavior and space use may differ among colonies and potentially across the maternity season. Simulation results suggest that colony social structure is robust to fragmentation caused by random loss of small numbers of roosts. Flexible social dynamics and tolerance of roost loss may be adaptive strategies for coping with ephemeral conditions in dynamic forest habitats.

Clustered marginalization of minorities during social transitions induced by co-evolution of behaviour and network structure

PubMed Central

Schleussner, Carl-Friedrich; Donges, Jonathan F.; Engemann, Denis A.; Levermann, Anders

2016-01-01

Large-scale transitions in societies are associated with both individual behavioural change and restructuring of the social network. These two factors have often been considered independently, yet recent advances in social network research challenge this view. Here we show that common features of societal marginalization and clustering emerge naturally during transitions in a co-evolutionary adaptive network model. This is achieved by explicitly considering the interplay between individual interaction and a dynamic network structure in behavioural selection. We exemplify this mechanism by simulating how smoking behaviour and the network structure get reconfigured by changing social norms. Our results are consistent with empirical findings: The prevalence of smoking was reduced, remaining smokers were preferentially connected among each other and formed increasingly marginalized clusters. We propose that self-amplifying feedbacks between individual behaviour and dynamic restructuring of the network are main drivers of the transition. This generative mechanism for co-evolution of individual behaviour and social network structure may apply to a wide range of examples beyond smoking. PMID:27510641

Simulating market dynamics: interactions between consumer psychology and social networks.

PubMed

Janssen, Marco A; Jager, Wander

2003-01-01

Markets can show different types of dynamics, from quiet markets dominated by one or a few products, to markets with continual penetration of new and reintroduced products. In a previous article we explored the dynamics of markets from a psychological perspective using a multi-agent simulation model. The main results indicated that the behavioral rules dominating the artificial consumer's decision making determine the resulting market dynamics, such as fashions, lock-in, and unstable renewal. Results also show the importance of psychological variables like social networks, preferences, and the need for identity to explain the dynamics of markets. In this article we extend this work in two directions. First, we will focus on a more systematic investigation of the effects of different network structures. The previous article was based on Watts and Strogatz's approach, which describes the small-world and clustering characteristics in networks. More recent research demonstrated that many large networks display a scale-free power-law distribution for node connectivity. In terms of market dynamics this may imply that a small proportion of consumers may have an exceptional influence on the consumptive behavior of others (hubs, or early adapters). We show that market dynamics is a self-organized property depending on the interaction between the agents' decision-making process (heuristics), the product characteristics (degree of satisfaction of unit of consumption, visibility), and the structure of interactions between agents (size of network and hubs in a social network).

Contagion of Cooperation in Static and Fluid Social Networks.

PubMed

Jordan, Jillian J; Rand, David G; Arbesman, Samuel; Fowler, James H; Christakis, Nicholas A

2013-01-01

Cooperation is essential for successful human societies. Thus, understanding how cooperative and selfish behaviors spread from person to person is a topic of theoretical and practical importance. Previous laboratory experiments provide clear evidence of social contagion in the domain of cooperation, both in fixed networks and in randomly shuffled networks, but leave open the possibility of asymmetries in the spread of cooperative and selfish behaviors. Additionally, many real human interaction structures are dynamic: we often have control over whom we interact with. Dynamic networks may differ importantly in the goals and strategic considerations they promote, and thus the question of how cooperative and selfish behaviors spread in dynamic networks remains open. Here, we address these questions with data from a social dilemma laboratory experiment. We measure the contagion of both cooperative and selfish behavior over time across three different network structures that vary in the extent to which they afford individuals control over their network ties. We find that in relatively fixed networks, both cooperative and selfish behaviors are contagious. In contrast, in more dynamic networks, selfish behavior is contagious, but cooperative behavior is not: subjects are fairly likely to switch to cooperation regardless of the behavior of their neighbors. We hypothesize that this insensitivity to the behavior of neighbors in dynamic networks is the result of subjects' desire to attract new cooperative partners: even if many of one's current neighbors are defectors, it may still make sense to switch to cooperation. We further hypothesize that selfishness remains contagious in dynamic networks because of the well-documented willingness of cooperators to retaliate against selfishness, even when doing so is costly. These results shed light on the contagion of cooperative behavior in fixed and fluid networks, and have implications for influence-based interventions aiming at increasing cooperative behavior.

Vulnerability of a killer whale social network to disease outbreaks

NASA Astrophysics Data System (ADS)

Guimarães, Paulo R., Jr.; de Menezes, Márcio Argollo; Baird, Robin W.; Lusseau, David; Guimarães, Paulo; Dos Reis, Sérgio F.

2007-10-01

Emerging infectious diseases are among the main threats to conservation of biological diversity. A crucial task facing epidemiologists is to predict the vulnerability of populations of endangered animals to disease outbreaks. In this context, the network structure of social interactions within animal populations may affect disease spreading. However, endangered animal populations are often small and to investigate the dynamics of small networks is a difficult task. Using network theory, we show that the social structure of an endangered population of mammal-eating killer whales is vulnerable to disease outbreaks. This feature was found to be a consequence of the combined effects of the topology and strength of social links among individuals. Our results uncover a serious challenge for conservation of the species and its ecosystem. In addition, this study shows that the network approach can be useful to study dynamical processes in very small networks.

Limitation of degree information for analyzing the interaction evolution in online social networks

NASA Astrophysics Data System (ADS)

Shang, Ke-Ke; Yan, Wei-Sheng; Xu, Xiao-Ke

2014-04-01

Previously many studies on online social networks simply analyze the static topology in which the friend relationship once established, then the links and nodes will not disappear, but this kind of static topology may not accurately reflect temporal interactions on online social services. In this study, we define four types of users and interactions in the interaction (dynamic) network. We found that active, disappeared, new and super nodes (users) have obviously different strength distribution properties and this result also can be revealed by the degree characteristics of the unweighted interaction and friendship (static) networks. However, the active, disappeared, new and super links (interactions) only can be reflected by the strength distribution in the weighted interaction network. This result indicates the limitation of the static topology data on analyzing social network evolutions. In addition, our study uncovers the approximately stable statistics for the dynamic social network in which there are a large variation for users and interaction intensity. Our findings not only verify the correctness of our definitions, but also helped to study the customer churn and evaluate the commercial value of valuable customers in online social networks.

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

PubMed

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

2016-11-29

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

The Reciprocal Relationship Between Social Connectedness and Mental Health Among Older European Adults: A SHARE-Based Analysis.

PubMed

Schwartz, Ella; Litwin, Howard

2017-11-04

The current study aimed to understand the reciprocal relationship between social networks and mental health in old age. It explored the dynamic aspects of that relationship and assessed the influence of social networks on mental health, as well as a concurrent influence of mental health on change in social connectedness. The data came from two measurement points in the Survey of Health, Aging and Retirement in Europe (SHARE). The analytic sample was composed of adults aged 65 years and above (N = 14,706). Analyses were conducted via latent change score models. Analyses showed a reciprocal association between social networks and mental health; baseline social connectedness led to mental health improvements and a better initial mental state led to richer social networks. The results further indicated that the relative effect of mental health on change in social network connectedness was greater than the corresponding effect of social network connectedness on change in mental health. No gender differences were found regarding the reciprocal associations. The results of this study demonstrate the dynamic inter-relationship of social networks and mental health. It highlights the need to take into account both directions of influence when studying the impact of social relationships on mental health. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Burstiness and tie activation strategies in time-varying social networks.

PubMed

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-13

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks' evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level.

PubMed

Coman, Alin; Momennejad, Ida; Drach, Rae D; Geana, Andra

2016-07-19

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members' memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals.

Social networks dynamics revealed by temporal analysis: An example in a non-human primate (Macaca sylvanus) in "La Forêt des Singes".

PubMed

Sosa, Sebastian; Zhang, Peng; Cabanes, Guénaël

2017-06-01

This study applied a temporal social network analysis model to describe three affiliative social networks (allogrooming, sleep in contact, and triadic interaction) in a non-human primate species, Macaca sylvanus. Three main social mechanisms were examined to determine interactional patterns among group members, namely preferential attachment (i.e., highly connected individuals are more likely to form new connections), triadic closure (new connections occur via previous close connections), and homophily (individuals interact preferably with others with similar attributes). Preferential attachment was only observed for triadic interaction network. Triadic closure was significant in allogrooming and triadic interaction networks. Finally, gender homophily was seasonal for allogrooming and sleep in contact networks, and observed in each period for triadic interaction network. These individual-based behaviors are based on individual reactions, and their analysis can shed light on the formation of the affiliative networks determining ultimate coalition networks, and how these networks may evolve over time. A focus on individual behaviors is necessary for a global interactional approach to understanding social behavior rules and strategies. When combined, these social processes could make animal social networks more resilient, thus enabling them to face drastic environmental changes. This is the first study to pinpoint some of the processes underlying the formation of a social structure in a non-human primate species, and identify common mechanisms with humans. The approach used in this study provides an ideal tool for further research seeking to answer long-standing questions about social network dynamics. © 2017 Wiley Periodicals, Inc.

a New Dynamic Community Model for Social Networks

NASA Astrophysics Data System (ADS)

Lu, Zhe-Ming; Wu, Zhen; Guo, Shi-Ze; Chen, Zhe; Song, Guang-Hua

2014-09-01

In this paper, based on the phenomenon that individuals join into and jump from the organizations in the society, we propose a dynamic community model to construct social networks. Two parameters are adopted in our model, one is the communication rate Pa that denotes the connection strength in the organization and the other is the turnover rate Pb, that stands for the frequency of jumping among the organizations. Based on simulations, we analyze not only the degree distribution, the clustering coefficient, the average distance and the network diameter but also the group distribution which is closely related to their community structure. Moreover, we discover that the networks generated by the proposed model possess the small-world property and can well reproduce the networks of social contacts.

Spreading in online social networks: the role of social reinforcement.

PubMed

Zheng, Muhua; Lü, Linyuan; Zhao, Ming

2013-07-01

Some epidemic spreading models are usually applied to analyze the propagation of opinions or news. However, the dynamics of epidemic spreading and information or behavior spreading are essentially different in many aspects. Centola's experiments [Science 329, 1194 (2010)] on behavior spreading in online social networks showed that the spreading is faster and broader in regular networks than in random networks. This result contradicts with the former understanding that random networks are preferable for spreading than regular networks. To describe the spreading in online social networks, a unknown-known-approved-exhausted four-status model was proposed, which emphasizes the effect of social reinforcement and assumes that the redundant signals can improve the probability of approval (i.e., the spreading rate). Performing the model on regular and random networks, it is found that our model can well explain the results of Centola's experiments on behavior spreading and some former studies on information spreading in different parameter space. The effects of average degree and network size on behavior spreading process are further analyzed. The results again show the importance of social reinforcement and are accordant with Centola's anticipation that increasing the network size or decreasing the average degree will enlarge the difference of the density of final approved nodes between regular and random networks. Our work complements the former studies on spreading dynamics, especially the spreading in online social networks where the information usually requires individuals' confirmations before being transmitted to others.

Influence of network dynamics on the spread of sexually transmitted diseases.

PubMed

Risau-Gusman, Sebastián

2012-06-07

Network epidemiology often assumes that the relationships defining the social network of a population are static. The dynamics of relationships is only taken indirectly into account by assuming that the relevant information to study epidemic spread is encoded in the network obtained, by considering numbers of partners accumulated over periods of time roughly proportional to the infectious period of the disease. On the other hand, models explicitly including social dynamics are often too schematic to provide a reasonable representation of a real population, or so detailed that no general conclusions can be drawn from them. Here, we present a model of social dynamics that is general enough so its parameters can be obtained by fitting data from surveys about sexual behaviour, but that can still be studied analytically, using mean-field techniques. This allows us to obtain some general results about epidemic spreading. We show that using accumulated network data to estimate the static epidemic threshold lead to a significant underestimation of that threshold. We also show that, for a dynamic network, the relative epidemic threshold is an increasing function of the infectious period of the disease, implying that the static value is a lower bound to the real threshold. A practical example is given of how to apply the model to the study of a real population.

Influence of network dynamics on the spread of sexually transmitted diseases

PubMed Central

Risau-Gusman, Sebastián

2012-01-01

Network epidemiology often assumes that the relationships defining the social network of a population are static. The dynamics of relationships is only taken indirectly into account by assuming that the relevant information to study epidemic spread is encoded in the network obtained, by considering numbers of partners accumulated over periods of time roughly proportional to the infectious period of the disease. On the other hand, models explicitly including social dynamics are often too schematic to provide a reasonable representation of a real population, or so detailed that no general conclusions can be drawn from them. Here, we present a model of social dynamics that is general enough so its parameters can be obtained by fitting data from surveys about sexual behaviour, but that can still be studied analytically, using mean-field techniques. This allows us to obtain some general results about epidemic spreading. We show that using accumulated network data to estimate the static epidemic threshold lead to a significant underestimation of that threshold. We also show that, for a dynamic network, the relative epidemic threshold is an increasing function of the infectious period of the disease, implying that the static value is a lower bound to the real threshold. A practical example is given of how to apply the model to the study of a real population. PMID:22112655

Impact of Trust on Security and Performance in Tactical Networks

DTIC Science & Technology

2013-06-01

and reliability . On the other hand, in organizational theory, trust management has viewed trust as a key factor to manage relationships that flourish...environments challenges, these dynamics can hinder accurate and reliable trust evaluation of entities in the network [10], [11]. • Information Network Domain...trustworthy entities. • Social/Cognitive Network Domain: Social scientists, physiologists, and neuroscientists have studied social trust, interpersonal

A Six-Year Follow-Up Study of Social Network Changes among African-American, Caribbean, and U.S.-Born Caucasian Urban Older Adults

ERIC Educational Resources Information Center

Conway, Francine; Magai, Carol; Jones, Samuel; Fiori, Katherine; Gillespie, Michael

2013-01-01

This study explores dynamic changes in network size and composition by examining patterns of older adults' social network change over time, that is: types of movements; the reason for the loss of network members; and the relation of movement and composition in concert. This study is a 6-year follow up of changes in the social networks of U.S.-Born…

The Role of Social Networks in the Adjustment and Academic Success of International Students: A Case Study of a University in the Southwest

ERIC Educational Resources Information Center

Kisang, Benjamin

2010-01-01

This study is a qualitative investigation of the role that social networks play in the adjustment and academic success of international students. With large numbers of international students enrolled on US campuses, it is important for practitioners to prepare, understand and address their dynamic needs. Based on social network, social capital and…

Dynamical influence processes on networks: general theory and applications to social contagion.

PubMed

Harris, Kameron Decker; Danforth, Christopher M; Dodds, Peter Sheridan

2013-08-01

We study binary state dynamics on a network where each node acts in response to the average state of its neighborhood. By allowing varying amounts of stochasticity in both the network and node responses, we find different outcomes in random and deterministic versions of the model. In the limit of a large, dense network, however, we show that these dynamics coincide. We construct a general mean-field theory for random networks and show this predicts that the dynamics on the network is a smoothed version of the average response function dynamics. Thus, the behavior of the system can range from steady state to chaotic depending on the response functions, network connectivity, and update synchronicity. As a specific example, we model the competing tendencies of imitation and nonconformity by incorporating an off-threshold into standard threshold models of social contagion. In this way, we attempt to capture important aspects of fashions and societal trends. We compare our theory to extensive simulations of this "limited imitation contagion" model on Poisson random graphs, finding agreement between the mean-field theory and stochastic simulations.

Communication Dynamics in Finite Capacity Social Networks

NASA Astrophysics Data System (ADS)

Haerter, Jan O.; Jamtveit, Bjørn; Mathiesen, Joachim

2012-10-01

In communication networks, structure and dynamics are tightly coupled. The structure controls the flow of information and is itself shaped by the dynamical process of information exchanged between nodes. In order to reconcile structure and dynamics, a generic model, based on the local interaction between nodes, is considered for the communication in large social networks. In agreement with data from a large human organization, we show that the flow is non-Markovian and controlled by the temporal limitations of individuals. We confirm the versatility of our model by predicting simultaneously the degree-dependent node activity, the balance between information input and output of nodes, and the degree distribution. Finally, we quantify the limitations to network analysis when it is based on data sampled over a finite period of time.

Recruitment dynamics in adaptive social networks

NASA Astrophysics Data System (ADS)

Shkarayev, Maxim; Shaw, Leah; Schwartz, Ira

2011-03-01

We model recruitment in social networks in the presence of birth and death processes. The recruitment is characterized by nodes changing their status to that of the recruiting class as a result of contact with recruiting nodes. The recruiting nodes may adapt their connections in order to improve recruitment capabilities, thus changing the network structure. We develop a mean-field theory describing the system dynamics. Using mean-field theory we characterize the dependence of the growth threshold of the recruiting class on the adaptation parameter. Furthermore, we investigate the effect of adaptation on the recruitment dynamics, as well as on network topology. The theoretical predictions are confirmed by the direct simulations of the full system.

Network cosmology.

PubMed

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology.

Network Cosmology

PubMed Central

Krioukov, Dmitri; Kitsak, Maksim; Sinkovits, Robert S.; Rideout, David; Meyer, David; Boguñá, Marián

2012-01-01

Prediction and control of the dynamics of complex networks is a central problem in network science. Structural and dynamical similarities of different real networks suggest that some universal laws might accurately describe the dynamics of these networks, albeit the nature and common origin of such laws remain elusive. Here we show that the causal network representing the large-scale structure of spacetime in our accelerating universe is a power-law graph with strong clustering, similar to many complex networks such as the Internet, social, or biological networks. We prove that this structural similarity is a consequence of the asymptotic equivalence between the large-scale growth dynamics of complex networks and causal networks. This equivalence suggests that unexpectedly similar laws govern the dynamics of complex networks and spacetime in the universe, with implications to network science and cosmology. PMID:23162688

The dynamics of information-driven coordination phenomena: A transfer entropy analysis

PubMed Central

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-01-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data. PMID:27051875

The dynamics of information-driven coordination phenomena: A transfer entropy analysis.

PubMed

Borge-Holthoefer, Javier; Perra, Nicola; Gonçalves, Bruno; González-Bailón, Sandra; Arenas, Alex; Moreno, Yamir; Vespignani, Alessandro

2016-04-01

Data from social media provide unprecedented opportunities to investigate the processes that govern the dynamics of collective social phenomena. We consider an information theoretical approach to define and measure the temporal and structural signatures typical of collective social events as they arise and gain prominence. We use the symbolic transfer entropy analysis of microblogging time series to extract directed networks of influence among geolocalized subunits in social systems. This methodology captures the emergence of system-level dynamics close to the onset of socially relevant collective phenomena. The framework is validated against a detailed empirical analysis of five case studies. In particular, we identify a change in the characteristic time scale of the information transfer that flags the onset of information-driven collective phenomena. Furthermore, our approach identifies an order-disorder transition in the directed network of influence between social subunits. In the absence of clear exogenous driving, social collective phenomena can be represented as endogenously driven structural transitions of the information transfer network. This study provides results that can help define models and predictive algorithms for the analysis of societal events based on open source data.

Common cold outbreaks: A network theory approach

NASA Astrophysics Data System (ADS)

Vishkaie, Faranak Rajabi; Bakouie, Fatemeh; Gharibzadeh, Shahriar

2014-11-01

In this study, at first we evaluated the network structure in social encounters by which respiratory diseases can spread. We considered common-cold and recorded a sample of human population and actual encounters between them. Our results show that the database structure presents a great value of clustering. In the second step, we evaluated dynamics of disease spread with SIR model by assigning a function to each node of the structural network. The rate of disease spread in networks was observed to be inversely correlated with characteristic path length. Therefore, the shortcuts have a significant role in increasing spread rate. We conclude that the dynamics of social encounters' network stands between the random and the lattice in network spectrum. Although in this study we considered the period of common-cold disease for network dynamics, it seems that similar approaches may be useful for other airborne diseases such as SARS.

Dynamical origins of the community structure of an online multi-layer society

NASA Astrophysics Data System (ADS)

Klimek, Peter; Diakonova, Marina; Eguíluz, Víctor M.; San Miguel, Maxi; Thurner, Stefan

2016-08-01

Social structures emerge as a result of individuals managing a variety of different social relationships. Societies can be represented as highly structured dynamic multiplex networks. Here we study the dynamical origins of the specific community structures of a large-scale social multiplex network of a human society that interacts in a virtual world of a massive multiplayer online game. There we find substantial differences in the community structures of different social actions, represented by the various layers in the multiplex network. Community sizes distributions are either fat-tailed or appear to be centered around a size of 50 individuals. To understand these observations we propose a voter model that is built around the principle of triadic closure. It explicitly models the co-evolution of node- and link-dynamics across different layers of the multiplex network. Depending on link and node fluctuation probabilities, the model exhibits an anomalous shattered fragmentation transition, where one layer fragments from one large component into many small components. The observed community size distributions are in good agreement with the predicted fragmentation in the model. This suggests that several detailed features of the fragmentation in societies can be traced back to the triadic closure processes.

Mnemonic convergence in social networks: The emergent properties of cognition at a collective level

PubMed Central

Coman, Alin; Momennejad, Ida; Drach, Rae D.; Geana, Andra

2016-01-01

The development of shared memories, beliefs, and norms is a fundamental characteristic of human communities. These emergent outcomes are thought to occur owing to a dynamic system of information sharing and memory updating, which fundamentally depends on communication. Here we report results on the formation of collective memories in laboratory-created communities. We manipulated conversational network structure in a series of real-time, computer-mediated interactions in fourteen 10-member communities. The results show that mnemonic convergence, measured as the degree of overlap among community members’ memories, is influenced by both individual-level information-processing phenomena and by the conversational social network structure created during conversational recall. By studying laboratory-created social networks, we show how large-scale social phenomena (i.e., collective memory) can emerge out of microlevel local dynamics (i.e., mnemonic reinforcement and suppression effects). The social-interactionist approach proposed herein points to optimal strategies for spreading information in social networks and provides a framework for measuring and forging collective memories in communities of individuals. PMID:27357678

Costs for switching partners reduce network dynamics but not cooperative behaviour

PubMed Central

Bednarik, Peter; Fehl, Katrin; Semmann, Dirk

2014-01-01

Social networks represent the structuring of interactions between group members. Above all, many interactions are profoundly cooperative in humans and other animals. In accordance with this natural observation, theoretical work demonstrates that certain network structures favour the evolution of cooperation. Yet, recent experimental evidence suggests that static networks do not enhance cooperative behaviour in humans. By contrast, dynamic networks do foster cooperation. However, costs associated with dynamism such as time or resource investments in finding and establishing new partnerships have been neglected so far. Here, we show that human participants are much less likely to break links when costs arise for building new links. Especially, when costs were high, the network was nearly static. Surprisingly, cooperation levels in Prisoner's Dilemma games were not affected by reduced dynamism in social networks. We conclude that the mere potential to quit collaborations is sufficient in humans to reach high levels of cooperative behaviour. Effects of self-structuring processes or assortment on the network played a minor role: participants simply adjusted their cooperative behaviour in response to the threats of losing a partner or of being expelled. PMID:25122233

Markets on Networks

NASA Astrophysics Data System (ADS)

Toroczkai, Zoltan; Anghel, Marian; Bassler, Kevin; Korniss, Gyorgy

2003-03-01

The dynamics of human, and most biological populations is characterized by competition for resources. By its own nature, this dynamics creates the group of "elites", formed by those agents who have strategies that are the most successful in the given situation, and therefore the rest of the agents will tend to follow, imitate, or interact with them, creating a social structure of leadership in the agent society. These inter-agent communications generate a complex social network with small-world character which itself forms the substrate for a second network, the action network. The latter is a highly dynamic, adaptive, directed network, defined by those inter-agent communication links on the substrate along which the passed information /prediction is acted upon by the other agents. By using the minority game for competition dynamics, here we show that when the substrate network is highly connected, the action network spontaneously develops hubs with a broad distribution of out-degrees, defining a robust leadership structure that is scale-free. Furthermore, in certain, realistic parameter ranges, facilitated by information passing on the action network, agents can spontaneously generate a high degree of cooperation making the collective almost maximally efficient.

Recovering time-varying networks of dependencies in social and biological studies.

PubMed

Ahmed, Amr; Xing, Eric P

2009-07-21

A plausible representation of the relational information among entities in dynamic systems such as a living cell or a social community is a stochastic network that is topologically rewiring and semantically evolving over time. Although there is a rich literature in modeling static or temporally invariant networks, little has been done toward recovering the network structure when the networks are not observable in a dynamic context. In this article, we present a machine learning method called TESLA, which builds on a temporally smoothed l(1)-regularized logistic regression formalism that can be cast as a standard convex-optimization problem and solved efficiently by using generic solvers scalable to large networks. We report promising results on recovering simulated time-varying networks and on reverse engineering the latent sequence of temporally rewiring political and academic social networks from longitudinal data, and the evolving gene networks over >4,000 genes during the life cycle of Drosophila melanogaster from a microarray time course at a resolution limited only by sample frequency.

Social and strategic imitation: the way to consensus.

PubMed

Vilone, Daniele; Ramasco, José J; Sánchez, Angel; Miguel, Maxi San

2012-01-01

Humans do not always make rational choices, a fact that experimental economics is putting on solid grounds. The social context plays an important role in determining our actions, and often we imitate friends or acquaintances without any strategic consideration. We explore here the interplay between strategic and social imitative behavior in a coordination problem on a social network. We observe that for interactions on 1D and 2D lattices any amount of social imitation prevents the freezing of the network in domains with different conventions, thus leading to global consensus. For interactions on complex networks, the interplay of social and strategic imitation also drives the system towards global consensus while neither dynamics alone does. We find an optimum value for the combination of imitative behaviors to reach consensus in a minimum time, and two different dynamical regimes to approach it: exponential when social imitation predominates, power-law when strategic considerations prevail.

Phase Transition in Opinion Diffusion in Social Networks

DTIC Science & Technology

2012-05-01

the opinions of social agents diffuse in a network under a so-called hard-interaction model, in which the agents inter- act more strongly with...gent behavior. Index Terms— opinion diffusion , opinion dynamics, social net- works, phase transition, herding. 1. INTRODUCTION The study of the

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,…

Burstiness and tie activation strategies in time-varying social networks

NASA Astrophysics Data System (ADS)

Ubaldi, Enrico; Vezzani, Alessandro; Karsai, Márton; Perra, Nicola; Burioni, Raffaella

2017-04-01

The recent developments in the field of social networks shifted the focus from static to dynamical representations, calling for new methods for their analysis and modelling. Observations in real social systems identified two main mechanisms that play a primary role in networks’ evolution and influence ongoing spreading processes: the strategies individuals adopt when selecting between new or old social ties, and the bursty nature of the social activity setting the pace of these choices. We introduce a time-varying network model accounting both for ties selection and burstiness and we analytically study its phase diagram. The interplay of the two effects is non trivial and, interestingly, the effects of burstiness might be suppressed in regimes where individuals exhibit a strong preference towards previously activated ties. The results are tested against numerical simulations and compared with two empirical datasets with very good agreement. Consequently, the framework provides a principled method to classify the temporal features of real networks, and thus yields new insights to elucidate the effects of social dynamics on spreading processes.

Capturing the Interplay of Dynamics and Networks through Parameterizations of Laplacian Operators

DTIC Science & Technology

2016-08-24

important vertices and communities in the network. Specifically, for each dynamical process in this framework, we define a centrality measure that...vertices as a potential cluster (or community ) with respect to this process. We show that the subset-quality function generalizes the traditional conductance...compare the different perspectives they create on network structure. Subjects Network Science and Online Social Networks Keywords Network, Community

The ‘Friendship Dynamics of Religion,’ or the ‘Religious Dynamics of Friendship’? A Social Network Analysis of Adolescents Who Attend Small Schools*

PubMed Central

Cheadle, Jacob E.; Schwadel, Philip

2012-01-01

Longitudinal social network data on adolescents in seven schools are analyzed to reach a new understanding about how the personal and interpersonal social dimensions of adolescent religion intertwine together in small school settings. We primarily address two issues relevant to the sociology of religion and sociology in general: (1) social selection as a source of religious homophily and (2) friend socialization of religion. Analysis results are consistent with Collins’ interaction ritual chain theory, which stresses the social dimensions of religion, since network-religion autocorrelations are relatively substantial in magnitude and both selection and socialization mechanisms play key roles in generating them. Results suggest that socialization plays a stronger role than social selection in four of six religious outcomes, and that more religious youth are more cliquish. Implications for our understanding of the social context of religion, religious homophily, and the ways we model religious influence, as well as limitations and considerations for future research, are discussed. PMID:23017927

Mapping dynamic social networks in real life using participants' own smartphones.

PubMed

Boonstra, Tjeerd W; E Larsen, Mark; Christensen, Helen

2015-11-01

Interpersonal relationships are vital for our daily functioning and wellbeing. Social networks may form the primary means by which environmental influences determine individual traits. Several studies have shown the influence of social networks on decision-making, behaviors and wellbeing. Smartphones have great potential for measuring social networks in a real world setting. Here we tested the feasibility of using people's own smartphones as a data collection platform for face-to-face interactions. We developed an application for iOS and Android to collect Bluetooth data and acquired one week of data from 14 participants in our organization. The Bluetooth scanning statistics were used to quantify the time-resolved connection strength between participants and define the weights of a dynamic social network. We used network metrics to quantify changes in network topology over time and non-negative matrix factorization to identify cliques or subgroups that reoccurred during the week. The scanning rate varied considerably between smartphones running Android and iOS and egocentric networks metrics were correlated with the scanning rate. The time courses of two identified subgroups matched with two meetings that took place that week. These findings demonstrate the feasibility of using participants' own smartphones to map social network, whilst identifying current limitations of using generic smartphones. The bias introduced by variations in scanning rate and missing data is an important limitation that needs to be addressed in future studies.

Dynamics of bounded confidence opinion in heterogeneous social networks: Concord against partial antagonism

NASA Astrophysics Data System (ADS)

Kurmyshev, Evguenii; Juárez, Héctor A.; González-Silva, Ricardo A.

2011-08-01

Bounded confidence models of opinion dynamics in social networks have been actively studied in recent years, in particular, opinion formation and extremism propagation along with other aspects of social dynamics. In this work, after an analysis of limitations of the Deffuant-Weisbuch (DW) bounded confidence, relative agreement model, we propose the mixed model that takes into account two psychological types of individuals. Concord agents (C-agents) are friendly people; they interact in a way that their opinions always get closer. Agents of the other psychological type show partial antagonism in their interaction (PA-agents). Opinion dynamics in heterogeneous social groups, consisting of agents of the two types, was studied on different social networks: Erdös-Rényi random graphs, small-world networks and complete graphs. Limit cases of the mixed model, pure C- and PA-societies, were also studied. We found that group opinion formation is, qualitatively, almost independent of the topology of networks used in this work. Opinion fragmentation, polarization and consensus are observed in the mixed model at different proportions of PA- and C-agents, depending on the value of initial opinion tolerance of agents. As for the opinion formation and arising of “dissidents”, the opinion dynamics of the C-agents society was found to be similar to that of the DW model, except for the rate of opinion convergence. Nevertheless, mixed societies showed dynamics and bifurcation patterns notably different to those of the DW model. The influence of biased initial conditions over opinion formation in heterogeneous social groups was also studied versus the initial value of opinion uncertainty, varying the proportion of the PA- to C-agents. Bifurcation diagrams showed an impressive evolution of collective opinion, in particular, radical changes of left to right consensus or vice versa at an opinion uncertainty value equal to 0.7 in the model with the PA/C mixture of population near 50/50.

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. Copyright © 2016. Published by Elsevier Inc.

Simulating drinking in social networks to inform alcohol prevention and treatment efforts.

PubMed

Hallgren, Kevin A; McCrady, Barbara S; Caudell, Thomas P; Witkiewitz, Katie; Tonigan, J Scott

2017-11-01

Adolescent drinking influences, and is influenced by, peer alcohol use. Several efficacious adolescent alcohol interventions include elements aimed at reducing susceptibility to peer influence. Modeling these interventions within dynamically changing social networks may improve our understanding of how such interventions work and for whom they work best. We used stochastic actor-based models to simulate longitudinal drinking and friendship formation within social networks using parameters obtained from a meta-analysis of real-world 10th grade adolescent social networks. Levels of social influence (i.e., friends affecting changes in one's drinking) and social selection (i.e., drinking affecting changes in one's friendships) were manipulated at several levels, which directly impacted the degree of clustering in friendships based on similarity in drinking behavior. Midway through each simulation, one randomly selected heavy-drinking actor from each network received an "intervention" that either (a) reduced their susceptibility to social influence, (b) reduced their susceptibility to social selection, (c) eliminated a friendship with a heavy drinker, or (d) initiated a friendship with a nondrinker. Only the intervention that eliminated targeted actors' susceptibility to social influence consistently reduced that actor's drinking. Moreover, this was only effective in networks with social influence and social selection that were at higher levels than what was found in the real-world reference study. Social influence and social selection are dynamic processes that can lead to complex systems that may moderate the effectiveness of network-based interventions. Interventions that reduce susceptibility to social influence may be most effective among adolescents with high susceptibility to social influence and heavier-drinking friends. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Fast Distributed Dynamics of Semantic Networks via Social Media.

PubMed

Carrillo, Facundo; Cecchi, Guillermo A; Sigman, Mariano; Slezak, Diego Fernández

2015-01-01

We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.

Fast Distributed Dynamics of Semantic Networks via Social Media

PubMed Central

Carrillo, Facundo; Cecchi, Guillermo A.; Sigman, Mariano; Fernández Slezak, Diego

2015-01-01

We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network. PMID:26074953

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.

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.

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.

Community Discovery in Dynamic, Rich-Context Social Networks

ERIC Educational Resources Information Center

Lin, Yu-Ru

2010-01-01

My research interest has been in understanding the human communities formed through interpersonal social activities. Participation in online communities on social network sites such as Twitter has been observed to influence people's behavior in diverse ways including financial decision-making and political choices, suggesting the rich potential…

Opinion evolution in different social acquaintance networks.

PubMed

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion p h and variation proportion p v are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve p v +2p h =2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Opinion evolution in different social acquaintance networks

NASA Astrophysics Data System (ADS)

Chen, Xi; Zhang, Xiao; Wu, Zhan; Wang, Hongwei; Wang, Guohua; Li, Wei

2017-11-01

Social acquaintance networks influenced by social culture and social policy have a great impact on public opinion evolution in daily life. Based on the differences between socio-culture and social policy, three different social acquaintance networks (kinship-priority acquaintance network, independence-priority acquaintance network, and hybrid acquaintance network) incorporating heredity proportion ph and variation proportion pv are proposed in this paper. Numerical experiments are conducted to investigate network topology and different phenomena during opinion evolution, using the Deffuant model. We found that in kinship-priority acquaintance networks, similar to the Chinese traditional acquaintance networks, opinions always achieve fragmentation, resulting in the formation of multiple large clusters and many small clusters due to the fact that individuals believe more in their relatives and live in a relatively closed environment. In independence-priority acquaintance networks, similar to Western acquaintance networks, the results are similar to those in the kinship-priority acquaintance network. In hybrid acquaintance networks, similar to the Chinese modern acquaintance networks, only a few clusters are formed indicating that in modern China, opinions are more likely to reach consensus on a large scale. These results are similar to the opinion evolution phenomena in modern society, proving the rationality and applicability of network models combined with social culture and policy. We also found a threshold curve pv+2 ph=2.05 in the results for the final opinion clusters and evolution time. Above the threshold curve, opinions could easily reach consensus. Based on the above experimental results, a culture-policy-driven mechanism for the opinion dynamic is worth promoting in this paper, that is, opinion dynamics can be driven by different social cultures and policies through the influence of heredity and variation in interpersonal relationship networks. This finding is of great significance for predicting opinion evolution under different acquaintance networks and formulating reasonable policies based on cultural characteristics to guide public opinion.

Incorporating social impact on new product adoption in choice modeing: A case study in green vehicles

DOE Office of Scientific and Technical Information (OSTI.GOV)

He, Lin; Wang, Mingxian; Chen, Wei

While discrete choice analysis is prevalent in capturing consumer preferences and describing their choice behaviors in product design, the traditional choice modeling approach assumes that each individual makes independent decisions, without considering the social impact. However, empirical studies show that choice is social - influenced by many factors beyond engineering performance of a product and consumer attributes. To alleviate this limitation, we propose a new choice modeling framework to capture the dynamic influence from social networks on consumer adoption of new products. By introducing social influence attributes into a choice utility function, social network simulation is integrated with the traditionalmore » discrete choice analysis in a three-stage process. Our study shows the need for considering social impact in forecasting new product adoption. Using hybrid electric vehicles as an example, our work illustrates the procedure of social network construction, social influence evaluation, and choice model estimation based on data from the National Household Travel Survey. Our study also demonstrates several interesting findings on the dynamic nature of new technology adoption and how social networks may influence hybrid electric vehicle adoption. (C) 2014 Elsevier Ltd. All rights reserved« less

Characterizing and modeling the dynamics of activity and popularity.

PubMed

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks.

Characterizing and Modeling the Dynamics of Activity and Popularity

PubMed Central

Zhang, Peng; Li, Menghui; Gao, Liang; Fan, Ying; Di, Zengru

2014-01-01

Social media, regarded as two-layer networks consisting of users and items, turn out to be the most important channels for access to massive information in the era of Web 2.0. The dynamics of human activity and item popularity is a crucial issue in social media networks. In this paper, by analyzing the growth of user activity and item popularity in four empirical social media networks, i.e., Amazon, Flickr, Delicious and Wikipedia, it is found that cross links between users and items are more likely to be created by active users and to be acquired by popular items, where user activity and item popularity are measured by the number of cross links associated with users and items. This indicates that users generally trace popular items, overall. However, it is found that the inactive users more severely trace popular items than the active users. Inspired by empirical analysis, we propose an evolving model for such networks, in which the evolution is driven only by two-step random walk. Numerical experiments verified that the model can qualitatively reproduce the distributions of user activity and item popularity observed in empirical networks. These results might shed light on the understandings of micro dynamics of activity and popularity in social media networks. PMID:24586586

Community evolution mining and analysis in social network

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Social influence and obesity.

PubMed

Hammond, Ross A

2010-10-01

To review a selection of research published in the last 12 months on the role of social influence in the obesity epidemic. Recent papers add evidence to previous work linking social network structures and obesity. Social norms, both eating norms and body image norms, are identified as one major source of social influence through networks. Social capital and social stress are additional types of social influence. There is increasing evidence that social influence and social network structures are significant factors in obesity. Deeper understanding of the mechanisms of action and dynamics of social influence, and its link with other factors involved in the obesity epidemic, is an important goal for further research.

CollaborationViz: Interactive Visual Exploration of Biomedical Research Collaboration Networks

PubMed Central

Bian, Jiang; Xie, Mengjun; Hudson, Teresa J.; Eswaran, Hari; Brochhausen, Mathias; Hanna, Josh; Hogan, William R.

2014-01-01

Social network analysis (SNA) helps us understand patterns of interaction between social entities. A number of SNA studies have shed light on the characteristics of research collaboration networks (RCNs). Especially, in the Clinical Translational Science Award (CTSA) community, SNA provides us a set of effective tools to quantitatively assess research collaborations and the impact of CTSA. However, descriptive network statistics are difficult for non-experts to understand. In this article, we present our experiences of building meaningful network visualizations to facilitate a series of visual analysis tasks. The basis of our design is multidimensional, visual aggregation of network dynamics. The resulting visualizations can help uncover hidden structures in the networks, elicit new observations of the network dynamics, compare different investigators and investigator groups, determine critical factors to the network evolution, and help direct further analyses. We applied our visualization techniques to explore the biomedical RCNs at the University of Arkansas for Medical Sciences – a CTSA institution. And, we created CollaborationViz, an open-source visual analytical tool to help network researchers and administration apprehend the network dynamics of research collaborations through interactive visualization. PMID:25405477

Popularity and Adolescent Friendship Networks: Selection and Influence Dynamics

ERIC Educational Resources Information Center

Dijkstra, Jan Kornelis; Cillessen, Antonius H. N.; Borch, Casey

2013-01-01

This study examined the dynamics of popularity in adolescent friendship networks across 3 years in middle school. Longitudinal social network modeling was used to identify selection and influence in the similarity of popularity among friends. It was argued that lower status adolescents strive to enhance their status through befriending higher…

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

PubMed

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

2018-03-01

The traits of the primary case of an infectious disease outbreak, and the circumstances for their aetiology, potentially influence the trajectory of transmission dynamics. However, these dynamics likely also depend on the traits of the individuals with whom the primary case interacts. We used the social spider Stegodyphus dumicola to test how the traits of the primary case, group phenotypic composition and group size interact to facilitate the transmission of a GFP-labelled cuticular bacterium. We also compared bacterial transmission across experimentally generated "daisy-chain" vs. "star" networks of social interactions. Finally, we compared social network structure across groups of different sizes. Groups of 10 spiders experienced more bacterial transmission events compared to groups of 30 spiders, regardless of groups' behavioural composition. Groups containing only one bold spider experienced the lowest levels of bacterial transmission regardless of group size. We found no evidence for the traits of the primary case influencing any transmission dynamics. In a second experiment, bacteria were transmitted to more individuals in experimentally induced star networks than in daisy-chains, on which transmission never exceeded three steps. In both experimental network types, transmission success depended jointly on the behavioural traits of the interacting individuals; however, the behavioural traits of the primary case were only important for transmission on star networks. Larger social groups exhibited lower interaction density (i.e. had a low ratio of observed to possible connections) and were more modular, i.e. they had more connections between nodes within a subgroup and fewer connections across subgroups. Thus, larger groups may restrict transmission by forming fewer interactions and by isolating subgroups that interacted with the primary case. These findings suggest that accounting for the traits of single exposed hosts has less power in predicting transmission dynamics compared to the larger scale factors of the social groups in which they reside. Factors like group size and phenotypic composition appear to alter social interaction patterns, which leads to differential transmission of microbes. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Communication Dynamics of Blog Networks

NASA Astrophysics Data System (ADS)

Goldberg, Mark; Kelley, Stephen; Magdon-Ismail, Malik; Mertsalov, Konstantin; Wallace, William (Al)

We study the communication dynamics of Blog networks, focusing on the Russian section of LiveJournal as a case study. Communication (blogger-to-blogger links) in such online communication networks is very dynamic: over 60% of the links in the network are new from one week to the next, though the set of bloggers remains approximately constant. Two fundamental questions are: (i) what models adequately describe such dynamic communication behavior; and (ii) how does one detect the phase transitions, i.e. the changes that go beyond the standard high-level dynamics? We approach these questions through the notion of stable statistics. We give strong experimental evidence to the fact that, despite the extreme amount of communication dynamics, several aggregate statistics are remarkably stable. We use stable statistics to test our models of communication dynamics postulating that any good model should produce values for these statistics which are both stable and close to the observed ones. Stable statistics can also be used to identify phase transitions, since any change in a normally stable statistic indicates a substantial change in the nature of the communication dynamics. We describe models of the communication dynamics in large social networks based on the principle of locality of communication: a node's communication energy is spent mostly within its own "social area," the locality of the node.

Modeling Human Dynamics of Face-to-Face Interaction Networks

NASA Astrophysics Data System (ADS)

Starnini, Michele; Baronchelli, Andrea; Pastor-Satorras, Romualdo

2013-04-01

Face-to-face interaction networks describe social interactions in human gatherings, and are the substrate for processes such as epidemic spreading and gossip propagation. The bursty nature of human behavior characterizes many aspects of empirical data, such as the distribution of conversation lengths, of conversations per person, or of interconversation times. Despite several recent attempts, a general theoretical understanding of the global picture emerging from data is still lacking. Here we present a simple model that reproduces quantitatively most of the relevant features of empirical face-to-face interaction networks. The model describes agents that perform a random walk in a two-dimensional space and are characterized by an attractiveness whose effect is to slow down the motion of people around them. The proposed framework sheds light on the dynamics of human interactions and can improve the modeling of dynamical processes taking place on the ensuing dynamical social networks.

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.

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

Dynamic social networks facilitate cooperation in the N-player Prisoner’s Dilemma

NASA Astrophysics Data System (ADS)

Rezaei, Golriz; Kirley, Michael

2012-12-01

Understanding how cooperative behaviour evolves in network communities, where the individual members interact via social dilemma games, is an on-going challenge. In this paper, we introduce a social network based model to investigate the evolution of cooperation in the N-player Prisoner’s Dilemma game. As such, this work complements previous studies focused on multi-player social dilemma games and endogenous networks. Agents in our model, employ different game-playing strategies reflecting varying cognitive capacities. When an agent plays cooperatively, a social link is formed with each of the other N-1 group members. Subsequent cooperative actions reinforce this link. However, when an agent defects, the links in the social network are broken. Computational simulations across a range of parameter settings are used to examine different scenarios: varying population and group sizes; the group formation process (or partner selection); and agent decision-making strategies under varying dilemma constraints (cost-to-benefit ratios), including a “discriminator” strategy where the action is based on a function of the weighted links within an agent’s social network. The simulation results show that the proposed social network model is able to evolve and maintain cooperation. As expected, as the value of N increases the equilibrium proportion of cooperators in the population decreases. In addition, this outcome is dependent on the dilemma constraint (cost-to-benefit ratio). However, in some circumstances the dynamic social network plays an increasingly important role in promoting and sustaining cooperation, especially when the agents adopt the discriminator strategy. The adjustment of social links results in the formation of communities of “like-minded” agents. Subsequently, this local optimal behaviour promotes the evolution of cooperative behaviour at the system level.

Effects of fundamentals acquisition and strategy switch on stock price dynamics

NASA Astrophysics Data System (ADS)

Wu, Songtao; He, Jianmin; Li, Shouwei

2018-02-01

An agent-based artificial stock market is developed to simulate trading behavior of investors. In the market, acquisition and employment of information about fundamentals and strategy switch are investigated to explain stock price dynamics. Investors could obtain the information from both market and neighbors resided on their social networks. Depending on information status and performances of different strategies, an informed investor may switch to the strategy of fundamentalist. This in turn affects the information acquisition process, since fundamentalists are more inclined to search and spread the information than chartists. Further investigation into price dynamics generated from three typical networks, i.e. regular lattice, small-world network and random graph, are conducted after general relation between network structures and price dynamics is revealed. In each network, integrated effects of different combinations of information efficiency and switch intensity are investigated. Results have shown that, along with increasing switch intensity, market and social information efficiency play different roles in the formation of price distortion, standard deviation and kurtosis of returns.

Social network and decision-making in primates: a report on Franco-Japanese research collaborations.

PubMed

Sueur, Cédric; Pelé, Marie

2016-07-01

Sociality is suggested to evolve as a strategy for animals to cope with challenges in their environment. Within a population, each individual can be seen as part of a network of social interactions that vary in strength, type and dynamics (Sueur et al. 2011a). The structure of this social network can strongly impact upon not only on the fitness of individuals and their decision-making, but also on the ecology of populations and the evolution of a species. Our Franco-Japanese collaboration allowed us to study social networks in several species (Japanese macaques, chimpanzees, colobines, etc.) and on different topics (social epidemiology, social evolution, information transmission). Individual attributes such as stress, rank or age can affect how individuals take decisions and the structure of the social network. This heterogeneity is linked to the assortativity of individuals and to the efficiency of the flow within a network. It is important, therefore, that this heterogeneity is integrated in the process or pattern under study in order to provide a better resolution of investigation and, ultimately, a better understanding of behavioural strategies, social dynamics and social evolution. How social information affects decision-making could be important to understand how social groups make collective decisions and how information may spread throughout the social group. In human beings, road-crossing behaviours in the presence of other individuals is a good way to study the influence of social information on individual behaviour and decision-making, for instance. Culture directly affects which information - personal vs social - individuals prefer to follow. Our collaboration contributed to the understanding of the relative influence of different factors, cultural and ecological, on primate, including human, sociality.

Dynamic Trust Models between Users over Social Networks

DTIC Science & Technology

2016-03-30

SUPPLEMENTARY NOTES 14. ABSTRACT In this project, by focusing on a number of word -of- mouth communication websites, we attempted to...analyzed evolution of trust networks in social media sites from a perspective of mediators. To this end, we proposed two stochastic models that...focusing on a number of word -of- mouth communication websites, we first attempt to construct dynamic trust models between users that enable to explain trust

Why we need more than just randomized controlled trials to establish the effectiveness of online social networks for health behavior change.

PubMed

Vandelanotte, Corneel; Maher, Carol A

2015-01-01

Despite their popularity and potential to promote health in large populations, the effectiveness of online social networks (e.g., Facebook) to improve health behaviors has been somewhat disappointing. Most of the research examining the effectiveness of such interventions has used randomized controlled trials (RCTs). It is asserted that the modest outcomes may be due to characteristics specific to both online social networks and RCTs. The highly controlled nature of RCTs stifles the dynamic nature of online social networks. Alternative and ecologically valid research designs that evaluate online social networks in real-life conditions are needed to advance the science in this area.

Dynamical Structure of a Traditional Amazonian Social Network

PubMed Central

Hooper, Paul L.; DeDeo, Simon; Caldwell Hooper, Ann E.; Gurven, Michael; Kaplan, Hillard S.

2014-01-01

Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha; Tsimane’: shocdye’) drinking events in a Tsimane’ village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days) is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone. PMID:25053880

Dynamical Structure of a Traditional Amazonian Social Network.

PubMed

Hooper, Paul L; DeDeo, Simon; Caldwell Hooper, Ann E; Gurven, Michael; Kaplan, Hillard S

2013-11-13

Reciprocity is a vital feature of social networks, but relatively little is known about its temporal structure or the mechanisms underlying its persistence in real world behavior. In pursuit of these two questions, we study the stationary and dynamical signals of reciprocity in a network of manioc beer (Spanish: chicha ; Tsimane': shocdye' ) drinking events in a Tsimane' village in lowland Bolivia. At the stationary level, our analysis reveals that social exchange within the community is heterogeneously patterned according to kinship and spatial proximity. A positive relationship between the frequencies at which two families host each other, controlling for kinship and proximity, provides evidence for stationary reciprocity. Our analysis of the dynamical structure of this network presents a novel method for the study of conditional, or non-stationary, reciprocity effects. We find evidence that short-timescale reciprocity (within three days) is present among non- and distant-kin pairs; conversely, we find that levels of cooperation among close kin can be accounted for on the stationary hypothesis alone.

The evolutionary and ecological consequences of animal social networks: emerging issues.

PubMed

Kurvers, Ralf H J M; Krause, Jens; Croft, Darren P; Wilson, Alexander D M; Wolf, Max

2014-06-01

The first generation of research on animal social networks was primarily aimed at introducing the concept of social networks to the fields of animal behaviour and behavioural ecology. More recently, a diverse body of evidence has shown that social fine structure matters on a broader scale than initially expected, affecting many key ecological and evolutionary processes. Here, we review this development. We discuss the effects of social network structure on evolutionary dynamics (genetic drift, fixation probabilities, and frequency-dependent selection) and social evolution (cooperation and between-individual behavioural differences). We discuss how social network structure can affect important coevolutionary processes (host-pathogen interactions and mutualisms) and population stability. We also discuss the potentially important, but poorly studied, role of social network structure on dispersal and invasion. Throughout, we highlight important areas for future research. Copyright © 2014 Elsevier Ltd. All rights reserved.

Estimating User Influence in Online Social Networks Subject to Information Overload

NASA Astrophysics Data System (ADS)

Li, Pei; Sun, Yunchuan; Chen, Yingwen; Tian, Zhi

2014-11-01

Online social networks have attracted remarkable attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. Due to the existence of information overload, the research on diffusion dynamics in epidemiology cannot be adopted directly to that in online social networks. In this paper, we consider diffusion dynamics in online social networks subject to information overload, and model the information-processing process of a user by a queue with a batch arrival and a finite buffer. We use the average number of times a message is processed after it is generated by a given user to characterize the user influence, which is then estimated through theoretical analysis for a given network. We validate the accuracy of our estimation by simulations, and apply the results to study the impacts of different factors on the user influence. Among the observations, we find that the impact of network size on the user influence is marginal while the user influence decreases with assortativity due to information overload, which is particularly interesting.

The 'friendship dynamics of religion,' or the 'religious dynamics of friendship'? A social network analysis of adolescents who attend small schools.

PubMed

Cheadle, Jacob E; Schwadel, Philip

2012-09-01

Longitudinal social network data on adolescents in seven schools are analyzed to reach a new understanding about how the personal and interpersonal social dimensions of adolescent religion intertwine together in small school settings. We primarily address two issues relevant to the sociology of religion and sociology in general: (1) social selection as a source of religious homophily and (2) friend socialization of religion. Analysis results are consistent with Collins' interaction ritual chain theory, which stresses the social dimensions of religion, since network-religion autocorrelations are relatively substantial in magnitude and both selection and socialization mechanisms play key roles in generating them. Results suggest that socialization plays a stronger role than social selection in four of six religious outcomes, and that more religious youth are more cliquish. Implications for our understanding of the social context of religion, religious homophily, and the ways we model religious influence, as well as limitations and considerations for future research, are discussed. Copyright © 2012 Elsevier Inc. All rights reserved.

An opinion-driven behavioral dynamics model for addictive behaviors

NASA Astrophysics Data System (ADS)

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; Ambrose, Bridget K.; Brodsky, Nancy S.; Brown, Theresa J.; Husten, Corinne; Glass, Robert J.

2015-04-01

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual's behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters provide targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. This has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.

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. Copyright © 2015 Elsevier B.V. All rights reserved.

User Access Management Based on Network Pricing for Social Network Applications

PubMed Central

Ma, Xingmin; Gu, Qing

2018-01-01

Social applications play a very important role in people’s lives, as users communicate with each other through social networks on a daily basis. This presents a challenge: How does one receive high-quality service from social networks at a low cost? Users can access different kinds of wireless networks from various locations. This paper proposes a user access management strategy based on network pricing such that networks can increase its income and improve service quality. Firstly, network price is treated as an optimizing access parameter, and an unascertained membership algorithm is used to make pricing decisions. Secondly, network price is adjusted dynamically in real time according to network load. Finally, selecting a network is managed and controlled in terms of the market economy. Simulation results show that the proposed scheme can effectively balance network load, reduce network congestion, improve the user's quality of service (QoS) requirements, and increase the network’s income. PMID:29495252

Predicting and controlling infectious disease epidemics using temporal networks

PubMed Central

Holme, Petter

2013-01-01

Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments. PMID:23513178

Predicting and controlling infectious disease epidemics using temporal networks.

PubMed

Masuda, Naoki; Holme, Petter

2013-01-01

Infectious diseases can be considered to spread over social networks of people or animals. Mainly owing to the development of data recording and analysis techniques, an increasing amount of social contact data with time stamps has been collected in the last decade. Such temporal data capture the dynamics of social networks on a timescale relevant to epidemic spreading and can potentially lead to better ways to analyze, forecast, and prevent epidemics. However, they also call for extended analysis tools for network epidemiology, which has, to date, mostly viewed networks as static entities. We review recent results of network epidemiology for such temporal network data and discuss future developments.

A cooperative game framework for detecting overlapping communities in social networks

NASA Astrophysics Data System (ADS)

Jonnalagadda, Annapurna; Kuppusamy, Lakshmanan

2018-02-01

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

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

NASA Astrophysics Data System (ADS)

Laurent, Guillaume; Saramäki, Jari; Karsai, Márton

2015-11-01

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

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks.

PubMed

Onaga, Tomokatsu; Gleeson, James P; Masuda, Naoki

2017-09-08

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

Concurrency-Induced Transitions in Epidemic Dynamics on Temporal Networks

NASA Astrophysics Data System (ADS)

Onaga, Tomokatsu; Gleeson, James P.; Masuda, Naoki

2017-09-01

Social contact networks underlying epidemic processes in humans and animals are highly dynamic. The spreading of infections on such temporal networks can differ dramatically from spreading on static networks. We theoretically investigate the effects of concurrency, the number of neighbors that a node has at a given time point, on the epidemic threshold in the stochastic susceptible-infected-susceptible dynamics on temporal network models. We show that network dynamics can suppress epidemics (i.e., yield a higher epidemic threshold) when the node's concurrency is low, but can also enhance epidemics when the concurrency is high. We analytically determine different phases of this concurrency-induced transition, and confirm our results with numerical simulations.

A Dynamic Game on Network Topology for Counterinsurgency Applications

DTIC Science & Technology

2015-03-26

scenario. This study creates a dynamic game on network topology to provide insight into the effec- tiveness of offensive targeting strategies determined by...focused upon the diffusion of thoughts and innovations throughout complex social networks. Coleman et al. (1966) and Ryan & Gross (1950) investigated...free networks make them extremely resilient against errors but very vulnerable to attack. Most interest- ingly, a determined attacker can remove well

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.

Threshold behaviors of social dynamics and financial outcomes of Ponzi scheme diffusion in complex networks

NASA Astrophysics Data System (ADS)

Fu, Peihua; Zhu, Anding; Ni, He; Zhao, Xin; Li, Xiulin

2018-01-01

Ponzi schemes always lead to mass disasters after collapse. It is important to study the critical behaviors of both social dynamics and financial outcomes for Ponzi scheme diffusion in complex networks. We develop the potential-investor-divestor-investor (PIDI) model by considering the individual behavior of direct reinvestment. We find that only the spreading rate relates to the epidemic outbreak while the reinvestment rate relates to the zero and non-zero final states for social dynamics of both homo- and inhomogeneous networks. Financially, we find that there is a critical spreading threshold, above which the scheme needs not to use its own initial capital for taking off, i.e. the starting cost is covered by the rapidly inflowing funds. However, the higher the cost per recruit, the larger the critical spreading threshold and the worse the financial outcomes. Theoretical and simulation results also reveal that schemes are easier to take off in inhomogeneous networks. The reinvestment rate does not affect the starting. However, it improves the financial outcome in the early stages and postpones the outbreak of financial collapse. Some policy suggestions for the regulator from the perspective of social physics are proposed in the end of the paper.

Social climber attachment in forming networks produces a phase transition in a measure of connectivity

NASA Astrophysics Data System (ADS)

Taylor, Dane; Larremore, Daniel B.

2012-09-01

The formation and fragmentation of networks are typically studied using percolation theory, but most previous research has been restricted to studying a phase transition in cluster size, examining the emergence of a giant component. This approach does not study the effects of evolving network structure on dynamics that occur at the nodes, such as the synchronization of oscillators and the spread of information, epidemics, and neuronal excitations. We introduce and analyze an alternative link-formation rule, called social climber (SC) attachment, that may be combined with arbitrary percolation models to produce a phase transition using the largest eigenvalue of the network adjacency matrix as the order parameter. This eigenvalue is significant in the analyses of many network-coupled dynamical systems in which it measures the quality of global coupling and is hence a natural measure of connectivity. We highlight the important self-organized properties of SC attachment and discuss implications for controlling dynamics on networks.

Social imitation versus strategic choice, or consensus versus cooperation, in the networked Prisoner's Dilemma

NASA Astrophysics Data System (ADS)

Vilone, Daniele; Ramasco, José J.; Sánchez, Angel; Miguel, Maxi San

2014-08-01

The interplay of social and strategic motivations in human interactions is a largely unexplored topic in collective social phenomena. Whether individuals' decisions are taken in a purely strategic basis or due to social pressure without a rational background crucially influences the model outcome. Here we study a networked Prisoner's Dilemma in which decisions are made either based on the replication of the most successful neighbor's strategy (unconditional imitation) or by pure social imitation following an update rule inspired by the voter model. The main effects of the voter dynamics are an enhancement of the final consensus, i.e., asymptotic states are generally uniform, and a promotion of cooperation in certain regions of the parameter space as compared to the outcome of purely strategic updates. Thus, voter dynamics acts as an interface noise and has a similar effect as a pure random noise; furthermore, its influence is mostly independent of the network heterogeneity. When strategic decisions are made following other update rules such as the replicator or Moran processes, the dynamic mixed state found under unconditional imitation for some parameters disappears, but an increase of cooperation in certain parameter regions is still observed. Comparing our results with recent experiments on the Prisoner's Dilemma, we conclude that such a mixed dynamics may explain moody conditional cooperation among the agents.

Social imitation versus strategic choice, or consensus versus cooperation, in the networked Prisoner's Dilemma.

PubMed

Vilone, Daniele; Ramasco, José J; Sánchez, Angel; San Miguel, Maxi

2014-08-01

The interplay of social and strategic motivations in human interactions is a largely unexplored topic in collective social phenomena. Whether individuals' decisions are taken in a purely strategic basis or due to social pressure without a rational background crucially influences the model outcome. Here we study a networked Prisoner's Dilemma in which decisions are made either based on the replication of the most successful neighbor's strategy (unconditional imitation) or by pure social imitation following an update rule inspired by the voter model. The main effects of the voter dynamics are an enhancement of the final consensus, i.e., asymptotic states are generally uniform, and a promotion of cooperation in certain regions of the parameter space as compared to the outcome of purely strategic updates. Thus, voter dynamics acts as an interface noise and has a similar effect as a pure random noise; furthermore, its influence is mostly independent of the network heterogeneity. When strategic decisions are made following other update rules such as the replicator or Moran processes, the dynamic mixed state found under unconditional imitation for some parameters disappears, but an increase of cooperation in certain parameter regions is still observed. Comparing our results with recent experiments on the Prisoner's Dilemma, we conclude that such a mixed dynamics may explain moody conditional cooperation among the agents.

Personality traits and ego-network dynamics

PubMed Central

Centellegher, Simone; López, Eduardo; Saramäki, Jari; Lepri, Bruno

2017-01-01

Strong and supportive social relationships are fundamental to our well-being. However, there are costs to their maintenance, resulting in a trade-off between quality and quantity, a typical strategy being to put a lot of effort on a few high-intensity relationships while maintaining larger numbers of less close relationships. It has also been shown that there are persistent individual differences in this pattern; some individuals allocate their efforts more uniformly across their networks, while others strongly focus on their closest relationships. Furthermore, some individuals maintain more stable networks than others. Here, we focus on how personality traits of individuals affect this picture, using mobile phone calls records and survey data from the Mobile Territorial Lab (MTL) study. In particular, we look at the relationship between personality traits and the (i) persistence of social signatures, namely the similarity of the social signature shape of an individual measured in different time intervals; (ii) the turnover in egocentric networks, that is, differences in the set of alters present at two consecutive temporal intervals; and (iii) the rank dynamics defined as the variation of alter rankings in egocentric networks in consecutive intervals. We observe that some traits have effects on the stability of the social signatures as well as network turnover and rank dynamics. As an example, individuals who score highly in the Openness to Experience trait tend to have higher levels of network turnover and larger alter rank variations. On broader terms, our study shows that personality traits clearly affect the ways in which individuals maintain their personal networks. PMID:28253333

Mental health and social networks in early adolescence: a dynamic study of objectively-measured social interaction behaviors.

PubMed

Pachucki, Mark C; Ozer, Emily J; Barrat, Alain; Cattuto, Ciro

2015-01-01

How are social interaction dynamics associated with mental health during early stages of adolescence? The goal of this study is to objectively measure social interactions and evaluate the roles that multiple aspects of the social environment--such as physical activity and food choice--may jointly play in shaping the structure of children's relationships and their mental health. The data in this study are drawn from a longitudinal network-behavior study conducted in 2012 at a private K-8 school in an urban setting in California. We recruited a highly complete network sample of sixth-graders (n = 40, 91% of grade, mean age = 12.3), and examined how two measures of distressed mental health (self-esteem and depressive symptoms) are positionally distributed in an early adolescent interaction network. We ascertained how distressed mental health shapes the structure of relationships over a three-month period, adjusting for relevant dimensions of the social environment. Cross-sectional analyses of interaction networks revealed that self-esteem and depressive symptoms are differentially stratified by gender. Specifically, girls with more depressive symptoms have interactions consistent with social inhibition, while boys' interactions suggest robustness to depressive symptoms. Girls higher in self-esteem tended towards greater sociability. Longitudinal network behavior models indicate that gender similarity and perceived popularity are influential in the formation of social ties. Greater school connectedness predicts the development of self-esteem, though social ties contribute to more self-esteem improvement among students who identify as European-American. Cross-sectional evidence shows associations between distressed mental health and students' network peers. However, there is no evidence that connected students' mental health status becomes more similar in their over time because of their network interactions. These findings suggest that mental health during early adolescence may be less subject to mechanisms of social influence than network research in even slightly older adolescents currently indicates. Copyright © 2014. Published by Elsevier Ltd.

Linking social and pathogen transmission networks using microbial genetics in giraffe (Giraffa camelopardalis).

PubMed

VanderWaal, Kimberly L; Atwill, Edward R; Isbell, Lynne A; McCowan, Brenda

2014-03-01

Although network analysis has drawn considerable attention as a promising tool for disease ecology, empirical research has been hindered by limitations in detecting the occurrence of pathogen transmission (who transmitted to whom) within social networks. Using a novel approach, we utilize the genetics of a diverse microbe, Escherichia coli, to infer where direct or indirect transmission has occurred and use these data to construct transmission networks for a wild giraffe population (Giraffe camelopardalis). Individuals were considered to be a part of the same transmission chain and were interlinked in the transmission network if they shared genetic subtypes of E. coli. By using microbial genetics to quantify who transmits to whom independently from the behavioural data on who is in contact with whom, we were able to directly investigate how the structure of contact networks influences the structure of the transmission network. To distinguish between the effects of social and environmental contact on transmission dynamics, the transmission network was compared with two separate contact networks defined from the behavioural data: a social network based on association patterns, and a spatial network based on patterns of home-range overlap among individuals. We found that links in the transmission network were more likely to occur between individuals that were strongly linked in the social network. Furthermore, individuals that had more numerous connections or that occupied 'bottleneck' positions in the social network tended to occupy similar positions in the transmission network. No similar correlations were observed between the spatial and transmission networks. This indicates that an individual's social network position is predictive of transmission network position, which has implications for identifying individuals that function as super-spreaders or transmission bottlenecks in the population. These results emphasize the importance of association patterns in understanding transmission dynamics, even for environmentally transmitted microbes like E. coli. This study is the first to use microbial genetics to construct and analyse transmission networks in a wildlife population and highlights the potential utility of an approach integrating microbial genetics with network analysis. © 2013 The Authors. Journal of Animal Ecology © 2013 British Ecological Society.

Competitive diffusion in online social networks with heterogeneous users

NASA Astrophysics Data System (ADS)

Li, Pei; He, Su; Wang, Hui; Zhang, Xin

2014-06-01

Online social networks have attracted increasing attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. However, most research on diffusion dynamics in epidemiology cannot be applied directly to characterize online social networks, where users are heterogeneous and may act differently according to their standpoints. In this paper, we propose models to characterize the competitive diffusion in online social networks with heterogeneous users. We classify messages into two types (i.e., positive and negative) and users into three types (i.e., positive, negative and neutral). We estimate the positive (negative) influence for a user generating a given type message, which is the number of times that positive (negative) messages are processed (i.e., read) incurred by this action. We then consider the diffusion threshold, above which the corresponding influence will approach infinity, and the effect threshold, above which the unexpected influence of generating a message will exceed the expected one. We verify all these results by simulations, which show the analysis results are perfectly consistent with the simulation results. These results are of importance in understanding the diffusion dynamics in online social networks, and also critical for advertisers in viral marketing where there are fans, haters and neutrals.

Measures of node centrality in mobile social networks

NASA Astrophysics Data System (ADS)

Gao, Zhenxiang; Shi, Yan; Chen, Shanzhi

2015-02-01

Mobile social networks exploit human mobility and consequent device-to-device contact to opportunistically create data paths over time. While links in mobile social networks are time-varied and strongly impacted by human mobility, discovering influential nodes is one of the important issues for efficient information propagation in mobile social networks. Although traditional centrality definitions give metrics to identify the nodes with central positions in static binary networks, they cannot effectively identify the influential nodes for information propagation in mobile social networks. In this paper, we address the problems of discovering the influential nodes in mobile social networks. We first use the temporal evolution graph model which can more accurately capture the topology dynamics of the mobile social network over time. Based on the model, we explore human social relations and mobility patterns to redefine three common centrality metrics: degree centrality, closeness centrality and betweenness centrality. We then employ empirical traces to evaluate the benefits of the proposed centrality metrics, and discuss the predictability of nodes' global centrality ranking by nodes' local centrality ranking. Results demonstrate the efficiency of the proposed centrality metrics.

The dynamics of social networks among female Asian elephants

PubMed Central

2011-01-01

Background Patterns in the association of individuals can shed light on the underlying conditions and processes that shape societies. Here we characterize patterns of association in a population of wild Asian Elephants at Uda Walawe National Park in Sri Lanka. We observed 286 individually-identified adult female elephants over 20 months and examined their social dynamics at three levels of organization: pairs of individuals (dyads), small sets of direct companions (ego-networks), and the population level (complete networks). Results Corroborating previous studies of this and other Asian elephant populations, we find that the sizes of elephant groups observed in the field on any particular day are typically small and that rates of association are low. In contrast to earlier studies, our longitudinal observations reveal that individuals form larger social units that can be remarkably stable across years while associations among such units change across seasons. Association rates tend to peak in dry seasons as opposed to wet seasons, with some cyclicity at the level of dyads. In addition, we find that individuals vary substantially in their fidelity to companions. At the ego-network level, we find that despite these fluctuations, individuals associate with a pool of long-term companions. At the population level, social networks do not exhibit any clear seasonal structure or hierarchical stratification. Conclusions This detailed longitudinal study reveals different social dynamics at different levels of organization. Taken together, these results demonstrate that low association rates, seemingly small group sizes, and fission-fusion grouping behavior mask hidden stability in the extensive and fluid social affiliations in this population of Asian elephants. PMID:21794147

How to estimate the signs' configuration in the directed signed social networks?

NASA Astrophysics Data System (ADS)

Guo, Long; Gao, Fujuan; Jiang, Jian

2017-02-01

Inspired by the ensemble theory in statistical mechanics, we introduce a reshuffling approach to empirical analyze signs' configuration in the directed signed social networks of Epinions and Slashdots. In our reshuffling approach, each negative link has the reshuffling probability prs to exchange its sign with another positive link chosen randomly. Many reshuffled networks with different signs' configuration are built under different prss. For each reshuffled network, the entropies of the self social status are calculated and the opinion formation of the majority-rule model is analyzed. We find that Souts reach their own minimum values and the order parameter |m* | reaches its maximum value in the networks of Epinions and Slashdots without the reshuffling operation. Namely, individuals share the homogeneous properties of self social status and dynamic status in the real directed signed social networks. Our present work provides some interesting tools and perspective to understand the signs' configuration in signed social networks, especially in the online affiliation networks.

Flexible Web services integration: a novel personalised social approach

NASA Astrophysics Data System (ADS)

Metrouh, Abdelmalek; Mokhati, Farid

2018-05-01

Dynamic composition or integration remains one of the key objectives of Web services technology. This paper aims to propose an innovative approach of dynamic Web services composition based on functional and non-functional attributes and individual preferences. In this approach, social networks of Web services are used to maintain interactions between Web services in order to select and compose Web services that are more tightly related to user's preferences. We use the concept of Web services community in a social network of Web services to reduce considerably their search space. These communities are created by the direct involvement of Web services providers.

The dynamic evolution of social ties and user-generated content: a case study on a Douban group

NASA Astrophysics Data System (ADS)

Shan, Siqing; Ren, Jie; Li, Cangyan

2017-11-01

As platforms based on user-generated content (UGC), social media platforms emphasise the social ties between users and user participation, which promote the communication and propagation of ideas and help to build and maintain relationships. However, many researchers have studied only predefined social networks, such as academic social networks. We believe that there are certain characteristics associated with the network's UGC worth evaluating. We conducted research in communities in which content attracts discussion and new members and examined the evolution patterns of social and content networks in a topic-oriented Douban group. Datasets of user and content information in communities of interest were collected through web crawler software. Networks based on social and content ties were constructed and analysed. We chose scale, density, centrality, average path length and cluster coefficient as measures for exploring the evolution and correlation of both types of networks. These findings are valuable for social media marketing and helpful in directing and controlling public opinion.

Taking sociality seriously: the structure of multi-dimensional social networks as a source of information for individuals

PubMed Central

Barrett, Louise; Henzi, S. Peter; Lusseau, David

2012-01-01

Understanding human cognitive evolution, and that of the other primates, means taking sociality very seriously. For humans, this requires the recognition of the sociocultural and historical means by which human minds and selves are constructed, and how this gives rise to the reflexivity and ability to respond to novelty that characterize our species. For other, non-linguistic, primates we can answer some interesting questions by viewing social life as a feedback process, drawing on cybernetics and systems approaches and using social network neo-theory to test these ideas. Specifically, we show how social networks can be formalized as multi-dimensional objects, and use entropy measures to assess how networks respond to perturbation. We use simulations and natural ‘knock-outs’ in a free-ranging baboon troop to demonstrate that changes in interactions after social perturbations lead to a more certain social network, in which the outcomes of interactions are easier for members to predict. This new formalization of social networks provides a framework within which to predict network dynamics and evolution, helps us highlight how human and non-human social networks differ and has implications for theories of cognitive evolution. PMID:22734054

Analyzing the Dynamics of Communication in Online Social Networks

NASA Astrophysics Data System (ADS)

de Choudhury, Munmun; Sundaram, Hari; John, Ajita; Seligmann, Doree Duncan

This chapter deals with the analysis of interpersonal communication dynamics in online social networks and social media. Communication is central to the evolution of social systems. Today, the different online social sites feature variegated interactional affordances, ranging from blogging, micro-blogging, sharing media elements (i.e., image, video) as well as a rich set of social actions such as tagging, voting, commenting and so on. Consequently, these communication tools have begun to redefine the ways in which we exchange information or concepts, and how the media channels impact our online interactional behavior. Our central hypothesis is that such communication dynamics between individuals manifest themselves via two key aspects: the information or concept that is the content of communication, and the channel i.e., the media via which communication takes place. We present computational models and discuss large-scale quantitative observational studies for both these organizing ideas. First, we develop a computational framework to determine the "interestingness" property of conversations cented around rich media. Second, we present user models of diffusion of social actions and study the impact of homophily on the diffusion process. The outcome of this research is twofold. First, extensive empirical studies on datasets from YouTube have indicated that on rich media sites, the conversations that are deemed "interesting" appear to have consequential impact on the properties of the social network they are associated with: in terms of degree of participation of the individuals in future conversations, thematic diffusion as well as emergent cohesiveness in activity among the concerned participants in the network. Second, observational and computational studies on large social media datasets such as Twitter have indicated that diffusion of social actions in a network can be indicative of future information cascades. Besides, given a topic, these cascades are often a function of attribute homophily existent among the participants. We believe that this chapter can make significant contribution into a better understanding of how we communicate online and how it is redefining our collective sociological behavior.

Predicting Node Degree Centrality with the Node Prominence Profile

PubMed Central

Yang, Yang; Dong, Yuxiao; Chawla, Nitesh V.

2014-01-01

Centrality of a node measures its relative importance within a network. There are a number of applications of centrality, including inferring the influence or success of an individual in a social network, and the resulting social network dynamics. While we can compute the centrality of any node in a given network snapshot, a number of applications are also interested in knowing the potential importance of an individual in the future. However, current centrality is not necessarily an effective predictor of future centrality. While there are different measures of centrality, we focus on degree centrality in this paper. We develop a method that reconciles preferential attachment and triadic closure to capture a node's prominence profile. We show that the proposed node prominence profile method is an effective predictor of degree centrality. Notably, our analysis reveals that individuals in the early stage of evolution display a distinctive and robust signature in degree centrality trend, adequately predicted by their prominence profile. We evaluate our work across four real-world social networks. Our findings have important implications for the applications that require prediction of a node's future degree centrality, as well as the study of social network dynamics. PMID:25429797

Dynamic Socialized Gaussian Process Models for Human Behavior Prediction in a Health Social Network

PubMed Central

Shen, Yelong; Phan, NhatHai; Xiao, Xiao; Jin, Ruoming; Sun, Junfeng; Piniewski, Brigitte; Kil, David; Dou, Dejing

2016-01-01

Modeling and predicting human behaviors, such as the level and intensity of physical activity, is a key to preventing the cascade of obesity and helping spread healthy behaviors in a social network. In our conference paper, we have developed a social influence model, named Socialized Gaussian Process (SGP), for socialized human behavior modeling. Instead of explicitly modeling social influence as individuals' behaviors influenced by their friends' previous behaviors, SGP models the dynamic social correlation as the result of social influence. The SGP model naturally incorporates personal behavior factor and social correlation factor (i.e., the homophily principle: Friends tend to perform similar behaviors) into a unified model. And it models the social influence factor (i.e., an individual's behavior can be affected by his/her friends) implicitly in dynamic social correlation schemes. The detailed experimental evaluation has shown the SGP model achieves better prediction accuracy compared with most of baseline methods. However, a Socialized Random Forest model may perform better at the beginning compared with the SGP model. One of the main reasons is the dynamic social correlation function is purely based on the users' sequential behaviors without considering other physical activity-related features. To address this issue, we further propose a novel “multi-feature SGP model” (mfSGP) which improves the SGP model by using multiple physical activity-related features in the dynamic social correlation learning. Extensive experimental results illustrate that the mfSGP model clearly outperforms all other models in terms of prediction accuracy and running time. PMID:27746515

Social opinion dynamics is not chaotic

NASA Astrophysics Data System (ADS)

Lim, Chjan; Zhang, Weituo

2016-08-01

Motivated by the research on social opinion dynamics over large and dense networks, a general framework for verifying the monotonicity property of multi-agent dynamics is introduced. This allows a derivation of sociologically meaningful sufficient conditions for monotonicity that are tailor-made for social opinion dynamics, which typically have high nonlinearity. A direct consequence of monotonicity is that social opinion dynamics is nonchaotic. A key part of this framework is the definition of a partial order relation that is suitable for a large class of social opinion dynamics such as the generalized naming games. Comparisons are made to previous techniques to verify monotonicity. Using the results obtained, we extend many of the consequences of monotonicity to this class of social dynamics, including several corollaries on their asymptotic behavior, such as global convergence to consensus and tipping points of a minority fraction of zealots or leaders.

Cooperation dynamics of generalized reciprocity in state-based social dilemmas

NASA Astrophysics Data System (ADS)

Stojkoski, Viktor; Utkovski, Zoran; Basnarkov, Lasko; Kocarev, Ljupco

2018-05-01

We introduce a framework for studying social dilemmas in networked societies where individuals follow a simple state-based behavioral mechanism based on generalized reciprocity, which is rooted in the principle "help anyone if helped by someone." Within this general framework, which applies to a wide range of social dilemmas including, among others, public goods, donation, and snowdrift games, we study the cooperation dynamics on a variety of complex network examples. By interpreting the studied model through the lenses of nonlinear dynamical systems, we show that cooperation through generalized reciprocity always emerges as the unique attractor in which the overall level of cooperation is maximized, while simultaneously exploitation of the participating individuals is prevented. The analysis elucidates the role of the network structure, here captured by a local centrality measure which uniquely quantifies the propensity of the network structure to cooperation by dictating the degree of cooperation displayed both at the microscopic and macroscopic level. We demonstrate the applicability of the analysis on a practical example by considering an interaction structure that couples a donation process with a public goods game.

Dynamic properties of epidemic spreading on finite size complex networks

NASA Astrophysics Data System (ADS)

Li, Ying; Liu, Yang; Shan, Xiu-Ming; Ren, Yong; Jiao, Jian; Qiu, Ben

2005-11-01

The Internet presents a complex topological structure, on which computer viruses can easily spread. By using theoretical analysis and computer simulation methods, the dynamic process of disease spreading on finite size networks with complex topological structure is investigated. On the finite size networks, the spreading process of SIS (susceptible-infected-susceptible) model is a finite Markov chain with an absorbing state. Two parameters, the survival probability and the conditional infecting probability, are introduced to describe the dynamic properties of disease spreading on finite size networks. Our results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks. Also, knowledge about the dynamic character of virus spreading is helpful for adopting immunity policy.

Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study

PubMed Central

Edge, Rhiannon; Heath, Joseph; Rowlingson, Barry; Keegan, Thomas J.; Isba, Rachel

2015-01-01

Introduction The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics. Methods We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students’ beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network. Results Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics. Conclusions This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student’s perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers. PMID:26452223

Seasonal Influenza Vaccination amongst Medical Students: A Social Network Analysis Based on a Cross-Sectional Study.

PubMed

Edge, Rhiannon; Heath, Joseph; Rowlingson, Barry; Keegan, Thomas J; Isba, Rachel

2015-01-01

The Chief Medical Officer for England recommends that healthcare workers have a seasonal influenza vaccination in an attempt to protect both patients and NHS staff. Despite this, many healthcare workers do not have a seasonal influenza vaccination. Social network analysis is a well-established research approach that looks at individuals in the context of their social connections. We examine the effects of social networks on influenza vaccination decision and disease dynamics. We used a social network analysis approach to look at vaccination distribution within the network of the Lancaster Medical School students and combined these data with the students' beliefs about vaccination behaviours. We then developed a model which simulated influenza outbreaks to study the effects of preferentially vaccinating individuals within this network. Of the 253 eligible students, 217 (86%) provided relational data, and 65% of responders had received a seasonal influenza vaccination. Students who were vaccinated were more likely to think other medical students were vaccinated. However, there was no clustering of vaccinated individuals within the medical student social network. The influenza simulation model demonstrated that vaccination of well-connected individuals may have a disproportional effect on disease dynamics. This medical student population exhibited vaccination coverage levels similar to those seen in other healthcare groups but below recommendations. However, in this population, a lack of vaccination clustering might provide natural protection from influenza outbreaks. An individual student's perception of the vaccination coverage amongst their peers appears to correlate with their own decision to vaccinate, but the directionality of this relationship is not clear. When looking at the spread of disease within a population it is important to include social structures alongside vaccination data. Social networks influence disease epidemiology and vaccination campaigns designed with information from social networks could be a future target for policy makers.

Modeling socio-cultural processes in network-centric environments

NASA Astrophysics Data System (ADS)

Santos, Eunice E.; Santos, Eugene, Jr.; Korah, John; George, Riya; Gu, Qi; Kim, Keumjoo; Li, Deqing; Russell, Jacob; Subramanian, Suresh

2012-05-01

The major focus in the field of modeling & simulation for network centric environments has been on the physical layer while making simplifications for the human-in-the-loop. However, the human element has a big impact on the capabilities of network centric systems. Taking into account the socio-behavioral aspects of processes such as team building, group decision-making, etc. are critical to realistically modeling and analyzing system performance. Modeling socio-cultural processes is a challenge because of the complexity of the networks, dynamism in the physical and social layers, feedback loops and uncertainty in the modeling data. We propose an overarching framework to represent, model and analyze various socio-cultural processes within network centric environments. The key innovation in our methodology is to simultaneously model the dynamism in both the physical and social layers while providing functional mappings between them. We represent socio-cultural information such as friendships, professional relationships and temperament by leveraging the Culturally Infused Social Network (CISN) framework. The notion of intent is used to relate the underlying socio-cultural factors to observed behavior. We will model intent using Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network, which can represent incomplete and uncertain socio-cultural information. We will leverage previous work on a network performance modeling framework called Network-Centric Operations Performance and Prediction (N-COPP) to incorporate dynamism in various aspects of the physical layer such as node mobility, transmission parameters, etc. We validate our framework by simulating a suitable scenario, incorporating relevant factors and providing analyses of the results.

Chronic, long-term social stress can cause decreased microtubule protein network activity and dynamics in cerebral cortex of male Wistar rats.

PubMed

Eskandari Sedighi, Ghazaleh; Riazi, Gholam Hossein; Vaez Mahdavi, Mohammad Reza; Cheraghi, Tayebe; Atarod, Deyhim; Rafiei, Shahrbanoo

2015-03-01

Social stress is viewed as a factor in the etiology of a variety of psychopathologies such as depression and anxiety. Animal models of social stress are well developed and widely used in studying clinical and physiological effects of stress. Stress is known to significantly affect learning and memory, and this effect strongly depends on the type of stress, its intensity, and duration. It has been demonstrated that chronic and acute stress conditions can change neuronal plasticity, characterized by retraction of apical dendrites, reduction in axonogenesis, and decreased neurogenesis. Various behavioral studies have also confirmed a decrease in learning and memory upon exposure of animals to long-term chronic stress. On the other hand, the close relationship between microtubule (MT) protein network and neuroplasticity controlling system suggests the possibility of MT protein alterations in high stressful conditions. In this work, we have studied the kinetics, activity, and dynamicity changes of MT proteins in the cerebral cortex of male Wistar rats that were subjected to social instability for 35 and 100 days. Our results indicate that MT protein network dynamicity and polymerization ability is decreased under long-term (100 days) social stress conditions.

Reinforced communication and social navigation: Remember your friends and remember yourself

NASA Astrophysics Data System (ADS)

Mirshahvalad, A.; Rosvall, M.

2011-09-01

In social systems, people communicate with each other and form groups based on their interests. The pattern of interactions, the network, and the ideas that flow on the network naturally evolve together. Researchers use simple models to capture the feedback between changing network patterns and ideas on the network, but little is understood about the role of past events in the feedback process. Here, we introduce a simple agent-based model to study the coupling between peoples’ ideas and social networks, and better understand the role of history in dynamic social networks. We measure how information about ideas can be recovered from information about network structure and, the other way around, how information about network structure can be recovered from information about ideas. We find that it is, in general, easier to recover ideas from the network structure than vice versa.

Emergent inequality and self-organized social classes in a network of power and frustration

DOE PAGES

Mahault, Benoit; Saxena, Avadh; Nisoli, Cristiano

2017-02-17

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution ofmore » wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes.« less

Emergent inequality and self-organized social classes in a network of power and frustration

DOE Office of Scientific and Technical Information (OSTI.GOV)

Mahault, Benoit; Saxena, Avadh; Nisoli, Cristiano

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution ofmore » wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes.« less

Emergent inequality and self-organized social classes in a network of power and frustration

PubMed Central

Mahault, Benoit; Saxena, Avadh

2017-01-01

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution of wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes. PMID:28212440

Emergent inequality and self-organized social classes in a network of power and frustration.

PubMed

Mahault, Benoit; Saxena, Avadh; Nisoli, Cristiano

2017-01-01

We propose a simple agent-based model on a network to conceptualize the allocation of limited wealth among more abundant expectations at the interplay of power, frustration, and initiative. Concepts imported from the statistical physics of frustrated systems in and out of equilibrium allow us to compare subjective measures of frustration and satisfaction to collective measures of fairness in wealth distribution, such as the Lorenz curve and the Gini index. We find that a completely libertarian, law-of-the-jungle setting, where every agent can acquire wealth from or lose wealth to anybody else invariably leads to a complete polarization of the distribution of wealth vs. opportunity. This picture is however dramatically ameliorated when hard constraints are imposed over agents in the form of a limiting network of transactions. There, an out of equilibrium dynamics of the networks, based on a competition between power and frustration in the decision-making of agents, leads to network coevolution. The ratio of power and frustration controls different dynamical regimes separated by kinetic transitions and characterized by drastically different values of equality. It also leads, for proper values of social initiative, to the emergence of three self-organized social classes, lower, middle, and upper class. Their dynamics, which appears mostly controlled by the middle class, drives a cyclical regime of dramatic social changes.

Network Stability Is a Balancing Act of Personality, Power, and Conflict Dynamics in Rhesus Macaque Societies

PubMed Central

McCowan, Brenda; Beisner, Brianne A.; Capitanio, John P.; Jackson, Megan E.; Cameron, Ashley N.; Seil, Shannon; Atwill, Edward R.; Fushing, Hsieh

2011-01-01

Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex) and external factors (e.g., rank dynamics, sex ratio) were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups. PMID:21857922

Network stability is a balancing act of personality, power, and conflict dynamics in rhesus macaque societies.

PubMed

McCowan, Brenda; Beisner, Brianne A; Capitanio, John P; Jackson, Megan E; Cameron, Ashley N; Seil, Shannon; Atwill, Edward R; Fushing, Hsieh

2011-01-01

Stability in biological systems requires evolved mechanisms that promote robustness. Cohesive primate social groups represent one example of a stable biological system, which persist in spite of frequent conflict. Multiple sources of stability likely exist for any biological system and such robustness, or lack thereof, should be reflected and thus detectable in the group's network structure, and likely at multiple levels. Here we show how network structure and group stability are linked to the fundamental characteristics of the individual agents in groups and to the environmental and social contexts in which these individuals interact. Both internal factors (e.g., personality, sex) and external factors (e.g., rank dynamics, sex ratio) were considered from the level of the individual to that of the group to examine the effects of network structure on group stability in a nonhuman primate species. The results yielded three main findings. First, successful third-party intervention behavior is a mechanism of group stability in rhesus macaques in that successful interventions resulted in less wounding in social groups. Second, personality is the primary factor that determines which individuals perform the role of key intervener, via its effect on social power and dominance discrepancy. Finally, individuals with high social power are not only key interveners but also key players in grooming networks and receive reconciliations from a higher diversity of individuals. The results from this study provide sound evidence that individual and group characteristics such as personality and sex ratio influence network structures such as patterns of reconciliation, grooming and conflict intervention that are indicators of network robustness and consequent health and well-being in rhesus macaque societies. Utilizing this network approach has provided greater insight into how behavioral and social processes influence social stability in nonhuman primate groups.

Human communication dynamics in digital footsteps: a study of the agreement between self-reported ties and email networks.

PubMed

Wuchty, Stefan; Uzzi, Brian

2011-01-01

Digital communication data has created opportunities to advance the knowledge of human dynamics in many areas, including national security, behavioral health, and consumerism. While digital data uniquely captures the totality of a person's communication, past research consistently shows that a subset of contacts makes up a person's "social network" of unique resource providers. To address this gap, we analyzed the correspondence between self-reported social network data and email communication data with the objective of identifying the dynamics in e-communication that correlate with a person's perception of a significant network tie. First, we examined the predictive utility of three popular methods to derive social network data from email data based on volume and reciprocity of bilateral email exchanges. Second, we observed differences in the response dynamics along self-reported ties, allowing us to introduce and test a new method that incorporates time-resolved exchange data. Using a range of robustness checks for measurement and misreporting errors in self-report and email data, we find that the methods have similar predictive utility. Although e-communication has lowered communication costs with large numbers of persons, and potentially extended our number of, and reach to contacts, our case results suggest that underlying behavioral patterns indicative of friendship or professional contacts continue to operate in a classical fashion in email interactions.

Learning Networks: Iran and the Effects of Sanctions

DTIC Science & Technology

2013-03-27

synthesizes and summarizes our research efforts. 15. SUBJECT TERMS Network Science, Social Network Analysis, Dynamic Networks 16. SECURITY...www.ecssr.com/ECSSR/appmanager/portal/ecssr?_nfpb=true. Klebnikov, Paul. " Millionaire Mullahs." Forbes. Forbes Magazine, 21 July 2003. Web. http

An opinion-driven behavioral dynamics model for addictive behaviors

DOE PAGES

Moore, Thomas W.; Finley, Patrick D.; Apelberg, Benjamin J.; ...

2015-04-08

We present a model of behavioral dynamics that combines a social network-based opinion dynamics model with behavioral mapping. The behavioral component is discrete and history-dependent to represent situations in which an individual’s behavior is initially driven by opinion and later constrained by physiological or psychological conditions that serve to maintain the behavior. Additionally, individuals are modeled as nodes in a social network connected by directed edges. Parameter sweeps illustrate model behavior and the effects of individual parameters and parameter interactions on model results. Mapping a continuous opinion variable into a discrete behavioral space induces clustering on directed networks. Clusters providemore » targets of opportunity for influencing the network state; however, the smaller the network the greater the stochasticity and potential variability in outcomes. Furthermore, this has implications both for behaviors that are influenced by close relationships verses those influenced by societal norms and for the effectiveness of strategies for influencing those behaviors.« less

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks.

PubMed

Perisic, Ana; Bauch, Chris T

2009-05-28

Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network. We simulate transmission of a vaccine-preventable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection. We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective. For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled.

A simulation analysis to characterize the dynamics of vaccinating behaviour on contact networks

PubMed Central

2009-01-01

Background Human behavior influences infectious disease transmission, and numerous "prevalence-behavior" models have analyzed this interplay. These previous analyses assumed homogeneously mixing populations without spatial or social structure. However, spatial and social heterogeneity are known to significantly impact transmission dynamics and are particularly relevant for certain diseases. Previous work has demonstrated that social contact structure can change the individual incentive to vaccinate, thus enabling eradication of a disease under a voluntary vaccination policy when the corresponding homogeneous mixing model predicts that eradication is impossible due to free rider effects. Here, we extend this work and characterize the range of possible behavior-prevalence dynamics on a network. Methods We simulate transmission of a vaccine-prevetable infection through a random, static contact network. Individuals choose whether or not to vaccinate on any given day according to perceived risks of vaccination and infection. Results We find three possible outcomes for behavior-prevalence dynamics on this type of network: small final number vaccinated and final epidemic size (due to rapid control through voluntary ring vaccination); large final number vaccinated and significant final epidemic size (due to imperfect voluntary ring vaccination), and little or no vaccination and large final epidemic size (corresponding to little or no voluntary ring vaccination). We also show that the social contact structure enables eradication under a broad range of assumptions, except when vaccine risk is sufficiently high, the disease risk is sufficiently low, or individuals vaccinate too late for the vaccine to be effective. Conclusion For populations where infection can spread only through social contact network, relatively small differences in parameter values relating to perceived risk or vaccination behavior at the individual level can translate into large differences in population-level outcomes such as final size and final number vaccinated. The qualitative outcome of rational, self interested behaviour under a voluntary vaccination policy can vary substantially depending on interactions between social contact structure, perceived vaccine and disease risks, and the way that individual vaccination decision-making is modelled. PMID:19476616

Dynamic model of time-dependent complex networks.

PubMed

Hill, Scott A; Braha, Dan

2010-10-01

The characterization of the "most connected" nodes in static or slowly evolving complex networks has helped in understanding and predicting the behavior of social, biological, and technological networked systems, including their robustness against failures, vulnerability to deliberate attacks, and diffusion properties. However, recent empirical research of large dynamic networks (characterized by irregular connections that evolve rapidly) has demonstrated that there is little continuity in degree centrality of nodes over time, even when their degree distributions follow a power law. This unexpected dynamic centrality suggests that the connections in these systems are not driven by preferential attachment or other known mechanisms. We present an approach to explain real-world dynamic networks and qualitatively reproduce these dynamic centrality phenomena. This approach is based on a dynamic preferential attachment mechanism, which exhibits a sharp transition from a base pure random walk scheme.

Transformation of social networks in the late pre-Hispanic US Southwest.

PubMed

Mills, Barbara J; Clark, Jeffery J; Peeples, Matthew A; Haas, W R; Roberts, John M; Hill, J Brett; Huntley, Deborah L; Borck, Lewis; Breiger, Ronald L; Clauset, Aaron; Shackley, M Steven

2013-04-09

The late pre-Hispanic period in the US Southwest (A.D. 1200-1450) was characterized by large-scale demographic changes, including long-distance migration and population aggregation. To reconstruct how these processes reshaped social networks, we compiled a comprehensive artifact database from major sites dating to this interval in the western Southwest. We combine social network analysis with geographic information systems approaches to reconstruct network dynamics over 250 y. We show how social networks were transformed across the region at previously undocumented spatial, temporal, and social scales. Using well-dated decorated ceramics, we track changes in network topology at 50-y intervals to show a dramatic shift in network density and settlement centrality from the northern to the southern Southwest after A.D. 1300. Both obsidian sourcing and ceramic data demonstrate that long-distance network relationships also shifted from north to south after migration. Surprisingly, social distance does not always correlate with spatial distance because of the presence of network relationships spanning long geographic distances. Our research shows how a large network in the southern Southwest grew and then collapsed, whereas networks became more fragmented in the northern Southwest but persisted. The study also illustrates how formal social network analysis may be applied to large-scale databases of material culture to illustrate multigenerational changes in network structure.

Transformation of social networks in the late pre-Hispanic US Southwest

PubMed Central

Mills, Barbara J.; Clark, Jeffery J.; Peeples, Matthew A.; Haas, W. R.; Roberts, John M.; Hill, J. Brett; Huntley, Deborah L.; Borck, Lewis; Breiger, Ronald L.; Clauset, Aaron; Shackley, M. Steven

2013-01-01

The late pre-Hispanic period in the US Southwest (A.D. 1200–1450) was characterized by large-scale demographic changes, including long-distance migration and population aggregation. To reconstruct how these processes reshaped social networks, we compiled a comprehensive artifact database from major sites dating to this interval in the western Southwest. We combine social network analysis with geographic information systems approaches to reconstruct network dynamics over 250 y. We show how social networks were transformed across the region at previously undocumented spatial, temporal, and social scales. Using well-dated decorated ceramics, we track changes in network topology at 50-y intervals to show a dramatic shift in network density and settlement centrality from the northern to the southern Southwest after A.D. 1300. Both obsidian sourcing and ceramic data demonstrate that long-distance network relationships also shifted from north to south after migration. Surprisingly, social distance does not always correlate with spatial distance because of the presence of network relationships spanning long geographic distances. Our research shows how a large network in the southern Southwest grew and then collapsed, whereas networks became more fragmented in the northern Southwest but persisted. The study also illustrates how formal social network analysis may be applied to large-scale databases of material culture to illustrate multigenerational changes in network structure. PMID:23530201

From sparse to dense and from assortative to disassortative in online social networks

PubMed Central

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-01-01

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. PMID:24798703

From sparse to dense and from assortative to disassortative in online social networks.

PubMed

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-05-06

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

Opinion dynamics in a group-based society

NASA Astrophysics Data System (ADS)

Gargiulo, F.; Huet, S.

2010-09-01

Many models have been proposed to analyze the evolution of opinion structure due to the interaction of individuals in their social environment. Such models analyze the spreading of ideas both in completely interacting backgrounds and on social networks, where each person has a finite set of interlocutors. In this paper we analyze the reciprocal feedback between the opinions of the individuals and the structure of the interpersonal relationships at the level of community structures. For this purpose we define a group-based random network and we study how this structure co-evolves with opinion dynamics processes. We observe that the adaptive network structure affects the opinion dynamics process helping the consensus formation. The results also show interesting behaviors in regards to the size distribution of the groups and their correlation with opinion structure.

An economic model of friendship and enmity for measuring social balance in networks

NASA Astrophysics Data System (ADS)

Lee, Kyu-Min; Shin, Euncheol; You, Seungil

2017-12-01

We propose a dynamic economic model of networks where agents can be friends or enemies with one another. This is a decentralized relationship model in that agents decide whether to change their relationships so as to minimize their imbalanced triads. In this model, there is a single parameter, which we call social temperature, that captures the degree to which agents care about social balance in their relationships. We show that the global structure of relationship configuration converges to a unique stationary distribution. Using this stationary distribution, we characterize the maximum likelihood estimator of the social temperature parameter. Since the estimator is computationally challenging to calculate from real social network datasets, we provide a simple simulation algorithm and verify its performance with real social network datasets.

The importance of including dynamic social networks when modeling epidemics of airborne infections: does increasing complexity increase accuracy?

PubMed

Blower, Sally; Go, Myong-Hyun

2011-07-19

Mathematical models are useful tools for understanding and predicting epidemics. A recent innovative modeling study by Stehle and colleagues addressed the issue of how complex models need to be to ensure accuracy. The authors collected data on face-to-face contacts during a two-day conference. They then constructed a series of dynamic social contact networks, each of which was used to model an epidemic generated by a fast-spreading airborne pathogen. Intriguingly, Stehle and colleagues found that increasing model complexity did not always increase accuracy. Specifically, the most detailed contact network and a simplified version of this network generated very similar results. These results are extremely interesting and require further exploration to determine their generalizability.

Emergence of hysteresis loop in social contagions on complex networks.

PubMed

Su, Zhen; Wang, Wei; Li, Lixiang; Xiao, Jinghua; Stanley, H Eugene

2017-07-21

Understanding the spreading mechanisms of social contagions in complex network systems has attracted much attention in the physics community. Here we propose a generalized threshold model to describe social contagions. Using extensive numerical simulations and theoretical analyses, we find that a hysteresis loop emerges in the system. Specifically, the steady state of the system is sensitive to the initial conditions of the dynamics of the system. In the steady state, the adoption size increases discontinuously with the transmission probability of information about social contagions, and trial size exhibits a non-monotonic pattern, i.e., it first increases discontinuously then decreases continuously. Finally we study social contagions on heterogeneous networks and find that network topology does not qualitatively affect our results.

Cultural propagation on social networks

NASA Astrophysics Data System (ADS)

Kuperman, M. N.

2006-04-01

In this work we present a model for the propagation of culture on networks of different topology and by considering different underlying dynamics. We extend a previous model proposed by Axelrod by letting a majority govern the dynamics of changes. This in turn allows us to define a Lyapunov functional for the system.

Optimal community structure for social contagions

NASA Astrophysics Data System (ADS)

Su, Zhen; Wang, Wei; Li, Lixiang; Stanley, H. Eugene; Braunstein, Lidia A.

2018-05-01

Community structure is an important factor in the behavior of real-world networks because it strongly affects the stability and thus the phase transition order of the spreading dynamics. We here propose a reversible social contagion model of community networks that includes the factor of social reinforcement. In our model an individual adopts a social contagion when the number of received units of information exceeds its adoption threshold. We use mean-field approximation to describe our proposed model, and the results agree with numerical simulations. The numerical simulations and theoretical analyses both indicate that there is a first-order phase transition in the spreading dynamics, and that a hysteresis loop emerges in the system when there is a variety of initially adopted seeds. We find an optimal community structure that maximizes spreading dynamics. We also find a rich phase diagram with a triple point that separates the no-diffusion phase from the two diffusion phases.

A prospective examination of online social network dynamics and smoking cessation

PubMed Central

Zhao, Kang; Papandonatos, George D.; Erar, Bahar; Wang, Xi; Amato, Michael S.; Cha, Sarah; Cohn, Amy M.; Pearson, Jennifer L.

2017-01-01

Introduction Use of online social networks for smoking cessation has been associated with abstinence. Little is known about the mechanisms through which the formation of social ties in an online network may influence smoking behavior. Using dynamic social network analysis, we investigated how temporal changes of an individual’s number of social network ties are prospectively related to abstinence in an online social network for cessation. In a network where quitting is normative and is the focus of communications among members, we predicted that an increasing number of ties would be positively associated with abstinence. Method Participants were N = 2,657 adult smokers recruited to a randomized cessation treatment trial following enrollment on BecomeAnEX.org, a longstanding Internet cessation program with a large and mature online social network. At 3-months post-randomization, 30-day point prevalence abstinence was assessed and website engagement metrics were extracted. The social network was constructed with clickstream data to capture the flow of information among members. Two network centrality metrics were calculated at weekly intervals over 3 months: 1) in-degree, defined as the number of members whose posts a participant read; and 2) out-degree-aware, defined as the number of members who read a participant’s post and commented, which was subsequently viewed by the participant. Three groups of users were identified based on social network engagement patterns: non-users (N = 1,362), passive users (N = 812), and active users (N = 483). Logistic regression modeled 3-month abstinence by group as a function of baseline variables, website utilization, and network centrality metrics. Results Abstinence rates varied by group (non-users = 7.7%, passive users = 10.7%, active users = 20.7%). Significant baseline predictors of abstinence were age, nicotine dependence, confidence to quit, and smoking temptations in social situations among passive users (ps < .05); age and confidence to quit among active users. Among centrality metrics, positive associations with abstinence were observed for in-degree increases from Week 2 to Week 12 among passive and active users, and for out-degree-aware increases from Week 2 to Week 12 among active users (ps < .05). Conclusions This study is the first to demonstrate that increased tie formation among members of an online social network for smoking cessation is prospectively associated with abstinence. It also highlights the value of using individuals’ activities in online social networks to predict their offline health behaviors. PMID:28832621

Leveraging Social Networking Technologies: An Analysis of the Knowledge Flows Facilitated by Social Media and the Potential Improvements in Situational Awareness, Readiness, and Productivity

DTIC Science & Technology

2010-09-01

articulating perception, interpretation and actionable prediction in an operational environment . BCKS’ success with digital storytelling has far...podcasts; wikis and other collaborative spaces; social networks such as Facebook and LinkedIn; other user generated content; virtual social environments ...study of Xerox’s knowledge management systems noting that 80% of its IT was focused on adapting to the social dynamics of its workplace environment

Nonlinear Dynamics on Interconnected Networks

NASA Astrophysics Data System (ADS)

Arenas, Alex; De Domenico, Manlio

2016-06-01

Networks of dynamical interacting units can represent many complex systems, from the human brain to transportation systems and societies. The study of these complex networks, when accounting for different types of interactions has become a subject of interest in the last few years, especially because its representational power in the description of users' interactions in diverse online social platforms (Facebook, Twitter, Instagram, etc.) [1], or in representing different transportation modes in urban networks [2,3]. The general name coined for these networks is multilayer networks, where each layer accounts for a type of interaction (see Fig. 1).

Epidemics in Adaptive Social Networks with Temporary Link Deactivation

NASA Astrophysics Data System (ADS)

Tunc, Ilker; Shkarayev, Maxim S.; Shaw, Leah B.

2013-04-01

Disease spread in a society depends on the topology of the network of social contacts. Moreover, individuals may respond to the epidemic by adapting their contacts to reduce the risk of infection, thus changing the network structure and affecting future disease spread. We propose an adaptation mechanism where healthy individuals may choose to temporarily deactivate their contacts with sick individuals, allowing reactivation once both individuals are healthy. We develop a mean-field description of this system and find two distinct regimes: slow network dynamics, where the adaptation mechanism simply reduces the effective number of contacts per individual, and fast network dynamics, where more efficient adaptation reduces the spread of disease by targeting dangerous connections. Analysis of the bifurcation structure is supported by numerical simulations of disease spread on an adaptive network. The system displays a single parameter-dependent stable steady state and non-monotonic dependence of connectivity on link deactivation rate.

Evolution of cooperation under social pressure in multiplex networks

NASA Astrophysics Data System (ADS)

Pereda, María

2016-09-01

In this work, we aim to contribute to the understanding of human prosocial behavior by studying the influence that a particular form of social pressure, "being watched," has on the evolution of cooperative behavior. We study how cooperation emerges in multiplex complex topologies by analyzing a particular bidirectionally coupled dynamics on top of a two-layer multiplex network (duplex). The coupled dynamics appears between the prisoner's dilemma game in a network and a threshold cascade model in the other. The threshold model is intended to abstract the behavior of a network of vigilant nodes that impose the pressure of being observed altering hence the temptation to defect of the dilemma. Cooperation or defection in the game also affects the state of a node of being vigilant. We analyze these processes on different duplex networks structures and assess the influence of the topology, average degree and correlated multiplexity, on the outcome of cooperation. Interestingly, we find that the social pressure of vigilance may impact cooperation positively or negatively, depending on the duplex structure, specifically the degree correlations between layers is determinant. Our results give further quantitative insights in the promotion of cooperation under social pressure.

Evolution of cooperation under social pressure in multiplex networks.

PubMed

Pereda, María

2016-09-01

In this work, we aim to contribute to the understanding of human prosocial behavior by studying the influence that a particular form of social pressure, "being watched," has on the evolution of cooperative behavior. We study how cooperation emerges in multiplex complex topologies by analyzing a particular bidirectionally coupled dynamics on top of a two-layer multiplex network (duplex). The coupled dynamics appears between the prisoner's dilemma game in a network and a threshold cascade model in the other. The threshold model is intended to abstract the behavior of a network of vigilant nodes that impose the pressure of being observed altering hence the temptation to defect of the dilemma. Cooperation or defection in the game also affects the state of a node of being vigilant. We analyze these processes on different duplex networks structures and assess the influence of the topology, average degree and correlated multiplexity, on the outcome of cooperation. Interestingly, we find that the social pressure of vigilance may impact cooperation positively or negatively, depending on the duplex structure, specifically the degree correlations between layers is determinant. Our results give further quantitative insights in the promotion of cooperation under social pressure.

First Selection, Then Influence: Developmental Differences in Friendship Dynamics Regarding Academic Achievement

ERIC Educational Resources Information Center

Gremmen, Mariola Claudia; Dijkstra, Jan Kornelis; Steglich, Christian; Veenstra, René

2017-01-01

This study concerns peer selection and influence dynamics in early adolescents' friendships regarding academic achievement. Using longitudinal social network analysis (RSiena), both selection and influence processes were investigated for students' average grades and their cluster-specific grades (i.e., language, exact, and social cluster). Data…

Dynamic phenomena and human activity in an artificial society

NASA Astrophysics Data System (ADS)

Grabowski, A.; Kruszewska, N.; Kosiński, R. A.

2008-12-01

We study dynamic phenomena in a large social network of nearly 3×104 individuals who interact in the large virtual world of a massive multiplayer online role playing game. On the basis of a database received from the online game server, we examine the structure of the friendship network and human dynamics. To investigate the relation between networks of acquaintances in virtual and real worlds, we carried out a survey among the players. We show that, even though the virtual network did not develop as a growing graph of an underlying network of social acquaintances in the real world, it influences it. Furthermore we find very interesting scaling laws concerning human dynamics. Our research shows how long people are interested in a single task and how much time they devote to it. Surprisingly, exponent values in both cases are close to -1 . We calculate the activity of individuals, i.e., the relative time daily devoted to interactions with others in the artificial society. Our research shows that the distribution of activity is not uniform and is highly correlated with the degree of the node, and that such human activity has a significant influence on dynamic phenomena, e.g., epidemic spreading and rumor propagation, in complex networks. We find that spreading is accelerated (an epidemic) or decelerated (a rumor) as a result of superspreaders’ various behavior.

Conflict and convention in dynamic networks.

PubMed

Foley, Michael; Forber, Patrick; Smead, Rory; Riedl, Christoph

2018-03-01

An important way to resolve games of conflict (snowdrift, hawk-dove, chicken) involves adopting a convention: a correlated equilibrium that avoids any conflict between aggressive strategies. Dynamic networks allow individuals to resolve conflict via their network connections rather than changing their strategy. Exploring how behavioural strategies coevolve with social networks reveals new dynamics that can help explain the origins and robustness of conventions. Here, we model the emergence of conventions as correlated equilibria in dynamic networks. Our results show that networks have the tendency to break the symmetry between the two conventional solutions in a strongly biased way. Rather than the correlated equilibrium associated with ownership norms (play aggressive at home, not away), we usually see the opposite host-guest norm (play aggressive away, not at home) evolve on dynamic networks, a phenomenon common to human interaction. We also show that learning to avoid conflict can produce realistic network structures in a way different than preferential attachment models. © 2017 The Author(s).

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…

A Learning Content Authoring Approach Based on Semantic Technologies and Social Networking: An Empirical Study

ERIC Educational Resources Information Center

Nesic, Sasa; Gasevic, Dragan; Jazayeri, Mehdi; Landoni, Monica

2011-01-01

Semantic web technologies have been applied to many aspects of learning content authoring including semantic annotation, semantic search, dynamic assembly, and personalization of learning content. At the same time, social networking services have started to play an important role in the authoring process by supporting authors' collaborative…

Modeling the dynamics of evaluation: a multilevel neural network implementation of the iterative reprocessing model.

PubMed

Ehret, Phillip J; Monroe, Brian M; Read, Stephen J

2015-05-01

We present a neural network implementation of central components of the iterative reprocessing (IR) model. The IR model argues that the evaluation of social stimuli (attitudes, stereotypes) is the result of the IR of stimuli in a hierarchy of neural systems: The evaluation of social stimuli develops and changes over processing. The network has a multilevel, bidirectional feedback evaluation system that integrates initial perceptual processing and later developing semantic processing. The network processes stimuli (e.g., an individual's appearance) over repeated iterations, with increasingly higher levels of semantic processing over time. As a result, the network's evaluations of stimuli evolve. We discuss the implications of the network for a number of different issues involved in attitudes and social evaluation. The success of the network supports the IR model framework and provides new insights into attitude theory. © 2014 by the Society for Personality and Social Psychology, Inc.

An information spreading model based on online social networks

NASA Astrophysics Data System (ADS)

Wang, Tao; He, Juanjuan; Wang, Xiaoxia

2018-01-01

Online social platforms are very popular in recent years. In addition to spreading information, users could review or collect information on online social platforms. According to the information spreading rules of online social network, a new information spreading model, namely IRCSS model, is proposed in this paper. It includes sharing mechanism, reviewing mechanism, collecting mechanism and stifling mechanism. Mean-field equations are derived to describe the dynamics of the IRCSS model. Moreover, the steady states of reviewers, collectors and stiflers and the effects of parameters on the peak values of reviewers, collectors and sharers are analyzed. Finally, numerical simulations are performed on different networks. Results show that collecting mechanism and reviewing mechanism, as well as the connectivity of the network, make information travel wider and faster, and compared to WS network and ER network, the speed of reviewing, sharing and collecting information is fastest on BA network.

Spreading paths in partially observed social networks

NASA Astrophysics Data System (ADS)

Onnela, Jukka-Pekka; Christakis, Nicholas A.

2012-03-01

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

Spreading paths in partially observed social networks.

PubMed

Onnela, Jukka-Pekka; Christakis, Nicholas A

2012-03-01

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

Collective Dynamics of Belief Evolution under Cognitive Coherence and Social Conformity.

PubMed

Rodriguez, Nathaniel; Bollen, Johan; Ahn, Yong-Yeol

2016-01-01

Human history has been marked by social instability and conflict, often driven by the irreconcilability of opposing sets of beliefs, ideologies, and religious dogmas. The dynamics of belief systems has been studied mainly from two distinct perspectives, namely how cognitive biases lead to individual belief rigidity and how social influence leads to social conformity. Here we propose a unifying framework that connects cognitive and social forces together in order to study the dynamics of societal belief evolution. Each individual is endowed with a network of interacting beliefs that evolves through interaction with other individuals in a social network. The adoption of beliefs is affected by both internal coherence and social conformity. Our framework may offer explanations for how social transitions can arise in otherwise homogeneous populations, how small numbers of zealots with highly coherent beliefs can overturn societal consensus, and how belief rigidity protects fringe groups and cults against invasion from mainstream beliefs, allowing them to persist and even thrive in larger societies. Our results suggest that strong consensus may be insufficient to guarantee social stability, that the cognitive coherence of belief-systems is vital in determining their ability to spread, and that coherent belief-systems may pose a serious problem for resolving social polarization, due to their ability to prevent consensus even under high levels of social exposure. We argue that the inclusion of cognitive factors into a social model could provide a more complete picture of collective human dynamics.

Talking the talk, walking the walk: social network norms, communication patterns, and condom use among the male partners of female sex workers in La Romana, Dominican Republic.

PubMed

Barrington, Clare; Latkin, Carl; Sweat, Michael D; Moreno, Luis; Ellen, Jonathan; Kerrigan, Deanna

2009-06-01

Male partners of female sex workers are rarely targeted by HIV prevention interventions in the commercial sex industry, despite recognition of their central role and power in condom use negotiation. Social networks offer a naturally existing social structure to increase male participation in preventing HIV. The purpose of this study was to explore the relationship between social network norms and condom use among male partners of female sex workers in La Romana, Dominican Republic. Male partners (N =318) were recruited from 36 sex establishments to participate in a personal network survey. Measures of social network norms included 1) perceived condom use by male social network members and 2) encouragement to use condoms from social network members. Other social network characteristics included composition, density, social support, and communication. The primary behavioral outcome was consistent condom use by male partners with their most recent female sex worker partner during the last 3 months. In general, men reported small, dense networks with high levels of communication about condoms and consistent condom use. Multivariate logistic regression revealed consistent condom use was significantly more likely among male partners who perceived that some or all of their male social network members used condoms consistently. Perceived condom use was, in turn, significantly associated with dense networks, expressing dislike for condoms, and encouragement to use condoms from social network members. Findings suggest that the tight social networks of male partners may help to explain the high level of condom use and could provide an entry point for HIV prevention efforts with men. Such efforts should tap into existing social dynamics and patterns of communication to promote pro-condom norms and reduce HIV-related vulnerability among men and their sexual partners.

New approaches to model and study social networks

NASA Astrophysics Data System (ADS)

Lind, P. G.; Herrmann, H. J.

2007-07-01

We describe and develop three recent novelties in network research which are particularly useful for studying social systems. The first one concerns the discovery of some basic dynamical laws that enable the emergence of the fundamental features observed in social networks, namely the nontrivial clustering properties, the existence of positive degree correlations and the subdivision into communities. To reproduce all these features, we describe a simple model of mobile colliding agents, whose collisions define the connections between the agents which are the nodes in the underlying network, and develop some analytical considerations. The second point addresses the particular feature of clustering and its relationship with global network measures, namely with the distribution of the size of cycles in the network. Since in social bipartite networks it is not possible to measure the clustering from standard procedures, we propose an alternative clustering coefficient that can be used to extract an improved normalized cycle distribution in any network. Finally, the third point addresses dynamical processes occurring on networks, namely when studying the propagation of information in them. In particular, we focus on the particular features of gossip propagation which impose some restrictions in the propagation rules. To this end we introduce a quantity, the spread factor, which measures the average maximal fraction of nearest neighbours which get in contact with the gossip, and find the striking result that there is an optimal non-trivial number of friends for which the spread factor is minimized, decreasing the danger of being gossiped about.

Structure-based control of complex networks with nonlinear dynamics.

PubMed

Zañudo, Jorge Gomez Tejeda; Yang, Gang; Albert, Réka

2017-07-11

What can we learn about controlling a system solely from its underlying network structure? Here we adapt a recently developed framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system toward any of its natural long-term dynamic behaviors, regardless of the specific functional forms and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of structural controllability in control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case but not in specific model instances.

Dynamical networks of influence in small group discussions.

PubMed

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

2018-01-01

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

Friendship Network and Dental Brushing Behavior among Middle School Students: An Agent Based Modeling Approach.

PubMed

Sadeghipour, Maryam; Khoshnevisan, Mohammad Hossein; Jafari, Afshin; Shariatpanahi, Seyed Peyman

2017-01-01

By using a standard questionnaire, the level of dental brushing frequency was assessed among 201 adolescent female middle school students in Tehran. The initial assessment was repeated after 5 months, in order to observe the dynamics in dental health behavior level. Logistic Regression model was used to evaluate the correlation among individuals' dental health behavior in their social network. A significant correlation on dental brushing habits was detected among groups of friends. This correlation was further spread over the network within the 5 months period. Moreover, it was identified that the average brushing level was improved within the 5 months period. Given that there was a significant correlation between social network's nodes' in-degree value, and brushing level, it was suggested that the observed improvement was partially due to more popularity of individuals with better tooth brushing habit. Agent Based Modeling (ABM) was used to demonstrate the dynamics of dental brushing frequency within a sample of friendship network. Two models with static and dynamic assumptions for the network structure were proposed. The model with dynamic network structure successfully described the dynamics of dental health behavior. Based on this model, on average, every 43 weeks a student changes her brushing habit due to learning from her friends. Finally, three training scenarios were tested by these models in order to evaluate their effectiveness. When training more popular students, considerable improvement in total students' brushing frequency was demonstrated by simulation results.

Axelrod's Metanorm Games on Networks

PubMed Central

Galán, José M.; Łatek, Maciej M.; Rizi, Seyed M. Mussavi

2011-01-01

Metanorms is a mechanism proposed to promote cooperation in social dilemmas. Recent experimental results show that network structures that underlie social interactions influence the emergence of norms that promote cooperation. We generalize Axelrod's analysis of metanorms dynamics to interactions unfolding on networks through simulation and mathematical modeling. Network topology strongly influences the effectiveness of the metanorms mechanism in establishing cooperation. In particular, we find that average degree, clustering coefficient and the average number of triplets per node play key roles in sustaining or collapsing cooperation. PMID:21655211

Social Networks as a Critical Pathway for Public Education in IYA2009

NASA Astrophysics Data System (ADS)

Plait, P.; Gay, P. L.

2008-11-01

Social networks are websites (or software that distributes media over the Internet) where users can share 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 teens, and by tapping into these networks IYA can excite and inspire a younger audience. IYA already has a small but growing presence on several of the larger social networks, and more are planned.

Impaired social brain network for processing dynamic facial expressions in autism spectrum disorders.

PubMed

Sato, Wataru; Toichi, Motomi; Uono, Shota; Kochiyama, Takanori

2012-08-13

Impairment of social interaction via facial expressions represents a core clinical feature of autism spectrum disorders (ASD). However, the neural correlates of this dysfunction remain unidentified. Because this dysfunction is manifested in real-life situations, we hypothesized that the observation of dynamic, compared with static, facial expressions would reveal abnormal brain functioning in individuals with ASD.We presented dynamic and static facial expressions of fear and happiness to individuals with high-functioning ASD and to age- and sex-matched typically developing controls and recorded their brain activities using functional magnetic resonance imaging (fMRI). Regional analysis revealed reduced activation of several brain regions in the ASD group compared with controls in response to dynamic versus static facial expressions, including the middle temporal gyrus (MTG), fusiform gyrus, amygdala, medial prefrontal cortex, and inferior frontal gyrus (IFG). Dynamic causal modeling analyses revealed that bi-directional effective connectivity involving the primary visual cortex-MTG-IFG circuit was enhanced in response to dynamic as compared with static facial expressions in the control group. Group comparisons revealed that all these modulatory effects were weaker in the ASD group than in the control group. These results suggest that weak activity and connectivity of the social brain network underlie the impairment in social interaction involving dynamic facial expressions in individuals with ASD.

Evolution properties of the community members for dynamic networks

NASA Astrophysics Data System (ADS)

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

2017-03-01

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

Cognitive Network Modeling as a Basis for Characterizing Human Communication Dynamics and Belief Contagion in Technology Adoption

NASA Technical Reports Server (NTRS)

Hutto, Clayton; Briscoe, Erica; Trewhitt, Ethan

2012-01-01

Societal level macro models of social behavior do not sufficiently capture nuances needed to adequately represent the dynamics of person-to-person interactions. Likewise, individual agent level micro models have limited scalability - even minute parameter changes can drastically affect a model's response characteristics. This work presents an approach that uses agent-based modeling to represent detailed intra- and inter-personal interactions, as well as a system dynamics model to integrate societal-level influences via reciprocating functions. A Cognitive Network Model (CNM) is proposed as a method of quantitatively characterizing cognitive mechanisms at the intra-individual level. To capture the rich dynamics of interpersonal communication for the propagation of beliefs and attitudes, a Socio-Cognitive Network Model (SCNM) is presented. The SCNM uses socio-cognitive tie strength to regulate how agents influence--and are influenced by--one another's beliefs during social interactions. We then present experimental results which support the use of this network analytical approach, and we discuss its applicability towards characterizing and understanding human information processing.

Emergence of bursts and communities in evolving weighted networks.

PubMed

Jo, Hang-Hyun; Pan, Raj Kumar; Kaski, Kimmo

2011-01-01

Understanding the patterns of human dynamics and social interaction and the way they lead to the formation of an organized and functional society are important issues especially for techno-social development. Addressing these issues of social networks has recently become possible through large scale data analysis of mobile phone call records, which has revealed the existence of modular or community structure with many links between nodes of the same community and relatively few links between nodes of different communities. The weights of links, e.g., the number of calls between two users, and the network topology are found correlated such that intra-community links are stronger compared to the weak inter-community links. This feature is known as Granovetter's "The strength of weak ties" hypothesis. In addition to this inhomogeneous community structure, the temporal patterns of human dynamics turn out to be inhomogeneous or bursty, characterized by the heavy tailed distribution of time interval between two consecutive events, i.e., inter-event time. In this paper, we study how the community structure and the bursty dynamics emerge together in a simple evolving weighted network model. The principal mechanisms behind these patterns are social interaction by cyclic closure, i.e., links to friends of friends and the focal closure, links to individuals sharing similar attributes or interests, and human dynamics by task handling process. These three mechanisms have been implemented as a network model with local attachment, global attachment, and priority-based queuing processes. By comprehensive numerical simulations we show that the interplay of these mechanisms leads to the emergence of heavy tailed inter-event time distribution and the evolution of Granovetter-type community structure. Moreover, the numerical results are found to be in qualitative agreement with empirical analysis results from mobile phone call dataset.

Dynamics of social behaviour at parturition in a gregarious ungulate.

PubMed

Pérez-Barbería, F J; Walker, D M

2018-05-01

Group living is the behavioural response that results when individuals assess the costs vs benefits of sociality, and these trade-offs vary across an animal's life. Here we quantitatively assess how periparturient condition (mother/non-mother) and births affect the dynamics of social interactions of a gregarious ungulate, and how such can help to explain evolutionary hypotheses of the mother-offspring bond. To achieve this we used data of the individual movement of a group of Scottish blackface sheep (Ovis aries) marked with GPS collars and properties of mathematical graphs (networks). Euclidean pair-wise distance between sheep were threshold at different percentiles to determine network links, and these thresholds have a profound effect on the connectivity of the resulting network. Births increased the average pair-wise distance between mothers, and between mothers and non-mothers, with less effect on the distance between non-mothers. Mothers occupied peripheral positions within the flock, more evident following births. Associations between individuals (i.e. network community change) were highly dynamic, though mothers were less likely to change community than non-mothers, especially after births. Births hampered individual communication within the flock (assessed via network closeness centrality), especially in mothers. Overall leadership (lead positioning relative to flock movement) was not associated to reproductive condition, and individual leadership rank was not affected by births. A ten minute GPS acquisition time was adequate to capture complex social dynamics in sheep movement. The results on mother's isolation behaviour support the hypotheses of selection for maternal imprint facilitation, reducing risks to nursing alien offspring, and group/multilevel selection on group formation. Copyright © 2018 Elsevier B.V. All rights reserved.

Effects of active links on epidemic transmission over social networks

NASA Astrophysics Data System (ADS)

Zhu, Guanghu; Chen, Guanrong; Fu, Xinchu

2017-02-01

A new epidemic model with two infection periods is developed to account for the human behavior in social network, where newly infected individuals gradually restrict most of future contacts or are quarantined, causing infectivity change from a degree-dependent form to a constant. The corresponding dynamics are formulated by a set of ordinary differential equations (ODEs) via mean-field approximation. The effects of diverse infectivity on the epidemic dynamics ​are examined, with a behavioral interpretation of the basic reproduction number. Results show that such simple adaptive reactions largely determine the impact of network structure on epidemics. Particularly, a theorem proposed by Lajmanovich and Yorke in 1976 is generalized, so that it can be applied for the analysis of the epidemic models with multi-compartments especially network-coupled ODE systems.

The dynamics of social innovation

PubMed Central

Young, H. Peyton

2011-01-01

Social norms and institutions are mechanisms that facilitate coordination between individuals. A social innovation is a novel mechanism that increases the welfare of the individuals who adopt it compared with the status quo. We model the dynamics of social innovation as a coordination game played on a network. Individuals experiment with a novel strategy that would increase their payoffs provided that it is also adopted by their neighbors. The rate at which a social innovation spreads depends on three factors: the topology of the network and in particular the extent to which agents interact in small local clusters, the payoff gain of the innovation relative to the status quo, and the amount of noise in the best response process. The analysis shows that local clustering greatly enhances the speed with which social innovations spread. It also suggests that the welfare gains from innovation are more likely to occur in large jumps than in a series of small incremental improvements. PMID:22198762

The dynamics of social innovation.

PubMed

Young, H Peyton

2011-12-27

Social norms and institutions are mechanisms that facilitate coordination between individuals. A social innovation is a novel mechanism that increases the welfare of the individuals who adopt it compared with the status quo. We model the dynamics of social innovation as a coordination game played on a network. Individuals experiment with a novel strategy that would increase their payoffs provided that it is also adopted by their neighbors. The rate at which a social innovation spreads depends on three factors: the topology of the network and in particular the extent to which agents interact in small local clusters, the payoff gain of the innovation relative to the status quo, and the amount of noise in the best response process. The analysis shows that local clustering greatly enhances the speed with which social innovations spread. It also suggests that the welfare gains from innovation are more likely to occur in large jumps than in a series of small incremental improvements.

A Markov chain model for image ranking system in social networks

NASA Astrophysics Data System (ADS)

Zin, Thi Thi; Tin, Pyke; Toriu, Takashi; Hama, Hiromitsu

2014-03-01

In today world, different kinds of networks such as social, technological, business and etc. exist. All of the networks are similar in terms of distributions, continuously growing and expanding in large scale. Among them, many social networks such as Facebook, Twitter, Flickr and many others provides a powerful abstraction of the structure and dynamics of diverse kinds of inter personal connection and interaction. Generally, the social network contents are created and consumed by the influences of all different social navigation paths that lead to the contents. Therefore, identifying important and user relevant refined structures such as visual information or communities become major factors in modern decision making world. Moreover, the traditional method of information ranking systems cannot be successful due to their lack of taking into account the properties of navigation paths driven by social connections. In this paper, we propose a novel image ranking system in social networks by using the social data relational graphs from social media platform jointly with visual data to improve the relevance between returned images and user intentions (i.e., social relevance). Specifically, we propose a Markov chain based Social-Visual Ranking algorithm by taking social relevance into account. By using some extensive experiments, we demonstrated the significant and effectiveness of the proposed social-visual ranking method.

An Inside Look at Homeless Youths’ Social Networks: Perceptions of Substance Use Norms1

PubMed Central

Melander, Lisa A.; Tyler, Kimberly A.; Schmitz, Rachel M.

2016-01-01

Substance use among homeless young people is a pervasive problem, and there have been many efforts to understand more about the dynamics of this health compromising behavior. The current study examined perceived substance use norms within homeless youths’ social networks utilizing in-depth interviews. The sample included 19 homeless individuals aged 16 to 21. Four elements of substance use within networks emerged: substance use choices, drug use safety issues, encouragement and/or discouragement, and appropriate situations in which substance use is condoned. These findings provide unique insight into the norms associated with drug and alcohol use within homeless youths’ social networks. PMID:26989340

Navigability of multiplex temporal network

NASA Astrophysics Data System (ADS)

Wang, Yan; Song, Qiao-Zhen

2017-01-01

Real world complex systems have multiple levels of relationships and in many cases, they need to be modeled as multiplex networks where the same nodes can interact with each other in different layers, such as social networks. However, social relationships only appear at prescribed times so the temporal structures of edge activations can also affect the dynamical processes located above them. To consider both factors are simultaneously, we introduce multiplex temporal networks and propose three different walk strategies to investigate the concurrent dynamics of random walks and the temporal structure of multiplex networks. Thus, we derive analytical results for the multiplex centrality and coverage function in multiplex temporal networks. By comparing them with the numerical results, we show how the underlying topology of the layers and the walk strategy affect the efficiency when exploring the networks. In particular, the most interesting result is the emergence of a super-diffusion process, where the time scale of the multiplex is faster than that of both layers acting separately.

From social integration to health: Durkheim in the new millennium.

PubMed

Berkman, L F; Glass, T; Brissette, I; Seeman, T E

2000-09-01

It is widely recognized that social relationships and affiliation have powerful effects on physical and mental health. When investigators write about the impact of social relationships on health, many terms are used loosely and interchangeably including social networks, social ties and social integration. The aim of this paper is to clarify these terms using a single framework. We discuss: (1) theoretical orientations from diverse disciplines which we believe are fundamental to advancing research in this area; (2) a set of definitions accompanied by major assessment tools; and (3) an overarching model which integrates multilevel phenomena. Theoretical orientations that we draw upon were developed by Durkheim whose work on social integration and suicide are seminal and John Bowlby, a psychiatrist who developed attachment theory in relation to child development and contemporary social network theorists. We present a conceptual model of how social networks impact health. We envision a cascading causal process beginning with the macro-social to psychobiological processes that are dynamically linked together to form the processes by which social integration effects health. We start by embedding social networks in a larger social and cultural context in which upstream forces are seen to condition network structure. Serious consideration of the larger macro-social context in which networks form and are sustained has been lacking in all but a small number of studies and is almost completely absent in studies of social network influences on health. We then move downstream to understand the influences network structure and function have on social and interpersonal behavior. We argue that networks operate at the behavioral level through four primary pathways: (1) provision of social support; (2) social influence; (3) on social engagement and attachment; and (4) access to resources and material goods.

Large Scale Data Analysis and Knowledge Extraction in Communication Data

DTIC Science & Technology

2017-03-31

this purpose, we developed a novel method the " Correlation Density Ran!C’ which finds probability density distribution of related frequent event on all...which is called " Correlation Density Rank", is developed to derive the community tree from the network. As in the real world, where a network is...Community Structure in Dynamic Social Networks using the Correlation Density Rank," 2014 ASE BigData/SocialCom/Cybersecurity Conference, Stanford

Dynamic node immunization for restraint of harmful information diffusion in social networks

NASA Astrophysics Data System (ADS)

Yang, Dingda; Liao, Xiangwen; Shen, Huawei; Cheng, Xueqi; Chen, Guolong

2018-08-01

To restrain the spread of harmful information is crucial for the healthy and sustainable development of social networks. We address the problem of restraining the spread of harmful information by immunizing nodes in the networks. Previous works have developed methods based on the network topology or studied how to immunize nodes in the presence of initial infected nodes. These static methods, in which nodes are immunized at once, may have poor performance in the certain situation due to the dynamics of diffusion. To tackle this problem, we introduce a new dynamic immunization problem of immunizing nodes during the process of the diffusion in this paper. We formulate the problem and propose a novel heuristic algorithm by dealing with two sub-problems: (1) how to select a node to achieve the best immunization effect at the present time? (2) whether the selected node should be immunized right now? Finally, we demonstrate the effectiveness of our algorithm through extensive experiments on various real datasets.

Blackmail propagation on small-world networks

NASA Astrophysics Data System (ADS)

Shao, Zhi-Gang; Jian-Ping Sang; Zou, Xian-Wu; Tan, Zhi-Jie; Jin, Zhun-Zhi

2005-06-01

The dynamics of the blackmail propagation model based on small-world networks is investigated. It is found that for a given transmitting probability λ the dynamical behavior of blackmail propagation transits from linear growth type to logistical growth one with the network randomness p increases. The transition takes place at the critical network randomness pc=1/N, where N is the total number of nodes in the network. For a given network randomness p the dynamical behavior of blackmail propagation transits from exponential decrease type to logistical growth one with the transmitting probability λ increases. The transition occurs at the critical transmitting probability λc=1/, where is the average number of the nearest neighbors. The present work will be useful for understanding computer virus epidemics and other spreading phenomena on communication and social networks.

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

PubMed Central

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

2014-01-01

Community structure is one of the most important properties in social networks. In dynamic networks, there are two conflicting criteria that need to be considered. One is the snapshot quality, which evaluates the quality of the community partitions at the current time step. The other is the temporal cost, which evaluates the difference between communities at different time steps. In this paper, we propose a decomposition-based multiobjective community detection algorithm to simultaneously optimize these two objectives to reveal community structure and its evolution in dynamic networks. It employs the framework of multiobjective evolutionary algorithm based on decomposition to simultaneously optimize the modularity and normalized mutual information, which quantitatively measure the quality of the community partitions and temporal cost, respectively. A local search strategy dealing with the problem-specific knowledge is incorporated to improve the effectiveness of the new algorithm. Experiments on computer-generated and real-world networks demonstrate that the proposed algorithm can not only find community structure and capture community evolution more accurately, but also be steadier than the two compared algorithms. PMID:24723806

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

PubMed

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

2014-01-01

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

Dynamic Communicability Predicts Infectiousness

NASA Astrophysics Data System (ADS)

Mantzaris, Alexander V.; Higham, Desmond J.

Using real, time-dependent social interaction data, we look at correlations between some recently proposed dynamic centrality measures and summaries from large-scale epidemic simulations. The evolving network arises from email exchanges. The centrality measures, which are relatively inexpensive to compute, assign rankings to individual nodes based on their ability to broadcast information over the dynamic topology. We compare these with node rankings based on infectiousness that arise when a full stochastic SI simulation is performed over the dynamic network. More precisely, we look at the proportion of the network that a node is able to infect over a fixed time period, and the length of time that it takes for a node to infect half the network. We find that the dynamic centrality measures are an excellent, and inexpensive, proxy for the full simulation-based measures.

Detection of social group instability among captive rhesus macaques using joint network modeling

PubMed Central

Beisner, Brianne A.; Jin, Jian; Fushing, Hsieh; Mccowan, Brenda

2015-01-01

Social stability in group-living animals is an emergent property which arises from the interaction amongst multiple behavioral networks. However, pinpointing when a social group is at risk of collapse is difficult. We used a joint network modeling approach to examine the interdependencies between two behavioral networks, aggression and status signaling, from four stable and three unstable groups of rhesus macaques in order to identify characteristic patterns of network interdependence in stable groups that are readily distinguishable from unstable groups. Our results showed that the most prominent source of aggression-status network interdependence in stable social groups came from more frequent dyads than expected with opposite direction status-aggression (i.e. A threatens B and B signals acceptance of subordinate status). In contrast, unstable groups showed a decrease in opposite direction aggression-status dyads (but remained higher than expected) as well as more frequent than expected dyads with bidirectional aggression. These results demonstrate that not only was the stable joint relationship between aggression and status networks readily distinguishable from unstable time points, social instability manifested in at least two different ways. In sum, our joint modeling approach may prove useful in quantifying and monitoring the complex social dynamics of any wild or captive social system, as all social systems are composed of multiple interconnected networks PMID:26052339

Parental Networks, Ethnicity, and Social and Cultural Capital: The Societal Dynamics of Educational Resilience in Turkey

ERIC Educational Resources Information Center

Çelik, Çetin

2017-01-01

Resilience research has increasingly gained ground in the field of education research, due to its potential for ameliorating inequalities. This article deals with the emergence of educational resilience, with particular attention to parental network structure, by employing a Bourdieusian social and cultural capital approach. While much of the…

Modeling Dynamic Evolution of Online Friendship Network

NASA Astrophysics Data System (ADS)

Wu, Lian-Ren; Yan, Qiang

2012-10-01

In this paper, we study the dynamic evolution of friendship network in SNS (Social Networking Site). Our analysis suggests that an individual joining a community depends not only on the number of friends he or she has within the community, but also on the friendship network generated by those friends. In addition, we propose a model which is based on two processes: first, connecting nearest neighbors; second, strength driven attachment mechanism. The model reflects two facts: first, in the social network it is a universal phenomenon that two nodes are connected when they have at least one common neighbor; second, new nodes connect more likely to nodes which have larger weights and interactions, a phenomenon called strength driven attachment (also called weight driven attachment). From the simulation results, we find that degree distribution P(k), strength distribution P(s), and degree-strength correlation are all consistent with empirical data.

The Dynamics of Protest Recruitment through an Online Network

NASA Astrophysics Data System (ADS)

González-Bailón, Sandra; Borge-Holthoefer, Javier; Rivero, Alejandro; Moreno, Yamir

2011-12-01

The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action.

The dynamics of protest recruitment through an online network.

PubMed

González-Bailón, Sandra; Borge-Holthoefer, Javier; Rivero, Alejandro; Moreno, Yamir

2011-01-01

The recent wave of mobilizations in the Arab world and across Western countries has generated much discussion on how digital media is connected to the diffusion of protests. We examine that connection using data from the surge of mobilizations that took place in Spain in May 2011. We study recruitment patterns in the Twitter network and find evidence of social influence and complex contagion. We identify the network position of early participants (i.e. the leaders of the recruitment process) and of the users who acted as seeds of message cascades (i.e. the spreaders of information). We find that early participants cannot be characterized by a typical topological position but spreaders tend to be more central in the network. These findings shed light on the connection between online networks, social contagion, and collective dynamics, and offer an empirical test to the recruitment mechanisms theorized in formal models of collective action.

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.

Human Communication Dynamics in Digital Footsteps: A Study of the Agreement between Self-Reported Ties and Email Networks

PubMed Central

Wuchty, Stefan; Uzzi, Brian

2011-01-01

Digital communication data has created opportunities to advance the knowledge of human dynamics in many areas, including national security, behavioral health, and consumerism. While digital data uniquely captures the totality of a person's communication, past research consistently shows that a subset of contacts makes up a person's “social network” of unique resource providers. To address this gap, we analyzed the correspondence between self-reported social network data and email communication data with the objective of identifying the dynamics in e-communication that correlate with a person's perception of a significant network tie. First, we examined the predictive utility of three popular methods to derive social network data from email data based on volume and reciprocity of bilateral email exchanges. Second, we observed differences in the response dynamics along self-reported ties, allowing us to introduce and test a new method that incorporates time-resolved exchange data. Using a range of robustness checks for measurement and misreporting errors in self-report and email data, we find that the methods have similar predictive utility. Although e-communication has lowered communication costs with large numbers of persons, and potentially extended our number of, and reach to contacts, our case results suggest that underlying behavioral patterns indicative of friendship or professional contacts continue to operate in a classical fashion in email interactions. PMID:22114665

Detecting the Influence of Spreading in Social Networks with Excitable Sensor Networks

PubMed Central

Pei, Sen; Tang, Shaoting; Zheng, Zhiming

2015-01-01

Detecting spreading outbreaks in social networks with sensors is of great significance in applications. Inspired by the formation mechanism of humans’ physical sensations to external stimuli, we propose a new method to detect the influence of spreading by constructing excitable sensor networks. Exploiting the amplifying effect of excitable sensor networks, our method can better detect small-scale spreading processes. At the same time, it can also distinguish large-scale diffusion instances due to the self-inhibition effect of excitable elements. Through simulations of diverse spreading dynamics on typical real-world social networks (Facebook, coauthor, and email social networks), we find that the excitable sensor networks are capable of detecting and ranking spreading processes in a much wider range of influence than other commonly used sensor placement methods, such as random, targeted, acquaintance and distance strategies. In addition, we validate the efficacy of our method with diffusion data from a real-world online social system, Twitter. We find that our method can detect more spreading topics in practice. Our approach provides a new direction in spreading detection and should be useful for designing effective detection methods. PMID:25950181

Ponzi scheme diffusion in complex networks

NASA Astrophysics Data System (ADS)

Zhu, Anding; Fu, Peihua; Zhang, Qinghe; Chen, Zhenyue

2017-08-01

Ponzi schemes taking the form of Internet-based financial schemes have been negatively affecting China's economy for the last two years. Because there is currently a lack of modeling research on Ponzi scheme diffusion within social networks yet, we develop a potential-investor-divestor (PID) model to investigate the diffusion dynamics of Ponzi scheme in both homogeneous and inhomogeneous networks. Our simulation study of artificial and real Facebook social networks shows that the structure of investor networks does indeed affect the characteristics of dynamics. Both the average degree of distribution and the power-law degree of distribution will reduce the spreading critical threshold and will speed up the rate of diffusion. A high speed of diffusion is the key to alleviating the interest burden and improving the financial outcomes for the Ponzi scheme operator. The zero-crossing point of fund flux function we introduce proves to be a feasible index for reflecting the fast-worsening situation of fiscal instability and predicting the forthcoming collapse. The faster the scheme diffuses, the higher a peak it will reach and the sooner it will collapse. We should keep a vigilant eye on the harm of Ponzi scheme diffusion through modern social networks.

High-risk sexual activity in the House and Ball community: influence of social networks.

PubMed

Schrager, Sheree M; Latkin, Carl A; Weiss, George; Kubicek, Katrina; Kipke, Michele D

2014-02-01

We investigated the roles of House membership and the influence of social and sexual network members on the sexual risk behavior of men in the Los Angeles House and Ball community. From February 2009 to January 2010, male participants (n = 233) completed interviewer-assisted surveys during a House meeting or Ball event. We used logistic regression to model the effects of sexual network size, influence of sexual network members, House membership status, and their interactions on high-risk sex. Significant predictors of high-risk sex included number of sexual partners in the nominated social network, multiethnicity, and previous diagnosis of sexually transmitted infection. House membership was protective against high-risk sex. Additionally, a 3-way interaction emerged between number of sexual partners in the network, influence, and network members' House membership. Future research should assess network members' attitudes and behavior in detail to provide a greater understanding of the dynamics of social influence and to identify additional avenues for intervention.

Variability in personality expression across contexts: a social network approach.

PubMed

Clifton, Allan

2014-04-01

The current research investigated how the contextual expression of personality differs across interpersonal relationships. Two related studies were conducted with college samples (Study 1: N = 52, 38 female; Study 2: N = 111, 72 female). Participants in each study completed a five-factor measure of personality and constructed a social network detailing their 30 most important relationships. Participants used a brief Five-Factor Model scale to rate their personality as they experience it when with each person in their social network. Multiple informants selected from each social network then rated the target participant's personality (Study 1: N = 227, Study 2: N = 777). Contextual personality ratings demonstrated incremental validity beyond standard global self-report in predicting specific informants' perceptions. Variability in these contextualized personality ratings was predicted by the position of the other individuals within the social network. Across both studies, participants reported being more extraverted and neurotic, and less conscientious, with more central members of their social networks. Dyadic social network-based assessments of personality provide incremental validity in understanding personality, revealing dynamic patterns of personality variability unobservable with standard assessment techniques. © 2013 Wiley Periodicals, Inc.

Analysing Health Professionals' Learning Interactions in an Online Social Network: A Longitudinal Study.

PubMed

Li, Xin; Verspoor, Karin; Gray, Kathleen; Barnett, Stephen

2016-01-01

This paper summarises a longitudinal analysis of learning interactions occurring over three years among health professionals in an online social network. The study employs the techniques of Social Network Analysis (SNA) and statistical modeling to identify the changes in patterns of interaction over time and test associated structural network effects. SNA results indicate overall low participation in the network, although some participants became active over time and even led discussions. In particular, the analysis has shown that a change of lead contributor results in a change in learning interaction and network structure. The analysis of structural network effects demonstrates that the interaction dynamics slow down over time, indicating that interactions in the network are more stable. The health professionals may be reluctant to share knowledge and collaborate in groups but were interested in building personal learning networks or simply seeking information.

Expected Number of Fixed Points in Boolean Networks with Arbitrary Topology.

PubMed

Mori, Fumito; Mochizuki, Atsushi

2017-07-14

Boolean network models describe genetic, neural, and social dynamics in complex networks, where the dynamics depend generally on network topology. Fixed points in a genetic regulatory network are typically considered to correspond to cell types in an organism. We prove that the expected number of fixed points in a Boolean network, with Boolean functions drawn from probability distributions that are not required to be uniform or identical, is one, and is independent of network topology if only a feedback arc set satisfies a stochastic neutrality condition. We also demonstrate that the expected number is increased by the predominance of positive feedback in a cycle.

Stylized facts in social networks: Community-based static modeling

NASA Astrophysics Data System (ADS)

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

2018-06-01

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

Early alterations of social brain networks in young children with autism

PubMed Central

Kojovic, Nada; Rihs, Tonia Anahi; Jan, Reem Kais; Franchini, Martina; Plomp, Gijs; Vulliemoz, Serge; Eliez, Stephan; Michel, Christoph Martin; Schaer, Marie

2018-01-01

Social impairments are a hallmark of Autism Spectrum Disorders (ASD), but empirical evidence for early brain network alterations in response to social stimuli is scant in ASD. We recorded the gaze patterns and brain activity of toddlers with ASD and their typically developing peers while they explored dynamic social scenes. Directed functional connectivity analyses based on electrical source imaging revealed frequency specific network atypicalities in the theta and alpha frequency bands, manifesting as alterations in both the driving and the connections from key nodes of the social brain associated with autism. Analyses of brain-behavioural relationships within the ASD group suggested that compensatory mechanisms from dorsomedial frontal, inferior temporal and insular cortical regions were associated with less atypical gaze patterns and lower clinical impairment. Our results provide strong evidence that directed functional connectivity alterations of social brain networks is a core component of atypical brain development at early stages of ASD. PMID:29482718

Integrating Entropy and Closed Frequent Pattern Mining for Social Network Modelling and Analysis

NASA Astrophysics Data System (ADS)

Adnan, Muhaimenul; Alhajj, Reda; Rokne, Jon

The recent increase in the explicitly available social networks has attracted the attention of the research community to investigate how it would be possible to benefit from such a powerful model in producing effective solutions for problems in other domains where the social network is implicit; we argue that social networks do exist around us but the key issue is how to realize and analyze them. This chapter presents a novel approach for constructing a social network model by an integrated framework that first preparing the data to be analyzed and then applies entropy and frequent closed patterns mining for network construction. For a given problem, we first prepare the data by identifying items and transactions, which arc the basic ingredients for frequent closed patterns mining. Items arc main objects in the problem and a transaction is a set of items that could exist together at one time (e.g., items purchased in one visit to the supermarket). Transactions could be analyzed to discover frequent closed patterns using any of the well-known techniques. Frequent closed patterns have the advantage that they successfully grab the inherent information content of the dataset and is applicable to a broader set of domains. Entropies of the frequent closed patterns arc used to keep the dimensionality of the feature vectors to a reasonable size; it is a kind of feature reduction process. Finally, we analyze the dynamic behavior of the constructed social network. Experiments were conducted on a synthetic dataset and on the Enron corpus email dataset. The results presented in the chapter show that social networks extracted from a feature set as frequent closed patterns successfully carry the community structure information. Moreover, for the Enron email dataset, we present an analysis to dynamically indicate the deviations from each user's individual and community profile. These indications of deviations can be very useful to identify unusual events.

HSC Foundation

MedlinePlus

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Social Sensor Analytics: Making Sense of Network Models in Social Media

DOE Office of Scientific and Technical Information (OSTI.GOV)

Dowling, Chase P.; Harrison, Joshua J.; Sathanur, Arun V.

Social networks can be thought of as noisy sensor networks mapping real world information to the web. Owing to the extensive body of literature in sensor network analysis, this work sought to apply several novel and traditional methods in sensor network analysis for the purposes of efficiently interrogating social media data streams from raw data. We carefully revisit our definition of a social media signal from previous work both in terms of time-varying features within the data and the networked nature of the medium. Further, we detail our analysis of global patterns in Twitter over the months of November 2013more » and June 2014, detect and categorize events, and illustrate how these analyses can be used to inform graph-based models of Twitter, namely using a recent network influence model called PhySense: similar to PageRank but tuned to behavioral analysis by leveraging a sociologically inspired probabilistic model. We ultimately identify forms of information dissemination via analysis of time series and dynamic graph spectra and corroborate these findings through manual investigation of the data as a requisite step in modeling the diffusion process with PhySense. We hope to sufficiently characterize global behavior in a medium such as Twitter as a means of learning global model parameters one may use to predict or simulate behavior on a large scale. We have made our time series and dynamic graph analytical code available via a GitHub repository https://github.com/cpatdowling/salsa and our data are available upon request.« less

Conceptualizing the Dynamics between Bicultural Identification and Personal Social Networks

PubMed Central

Repke, Lydia; Benet-Martínez, Verónica

2017-01-01

An adequate understanding of the acculturation processes affecting immigrants and their descendants involves ascertaining the dynamic interplay between the way these individuals manage their multiple (and sometimes conflictual) cultural value systems and identifications and possible changes in their social networks. To fill this gap, the present research examines how key acculturation variables (e.g., strength of ethnic/host cultural identifications, bicultural identity integration or BII) relate to the composition and structure of bicultural individuals’ personal social networks. In Study 1, we relied on a generationally and culturally diverse community sample of 123 Latinos residing in the US. Participants nominated eight individuals (i.e., alters) from their habitual social networks and across two relational domains: friendships and colleagues. Results indicated that the interconnection of same ethnicity alters across different relationship domains is linked to cultural identifications, while the amount of coethnic and host individuals in the network is not. In particular, higher interconnection between Latino friends and colleagues was linked to lower levels of U.S. identification. Conversely, the interconnection of non-Latino friends and colleagues was associated with lower levels of Latino identification. This pattern of results suggests that the relational context for each type of cultural identification works in a subtractive and inverse manner. Further, time spent in the US was linked to both Latino and U.S. cultural identifications, but this relationship was moderated by the level of BII. Specifically, the association between time in the US and strength of both cultural identities was stronger for individuals reporting low levels of BII. Taking the findings from Study 1 as departure point, Study 2 used an agent-based model data simulation approach to explore the dynamic ways in which the content and the structure of an immigrant’s social network might matter over time in predicting three possible identity patterns: coexisting cultural identifications, conflicting cultural identifications, and a mixture of the two. These simulations allowed us to detect network constellations, which lead to identification or disidentification with both cultures. We showed that distinct patterns of social relations do not lead to identity outcomes in a deterministic fashion, but that often many different outcomes are probable. PMID:28408892

Long-Term Evolution of Email Networks: Statistical Regularities, Predictability and Stability of Social Behaviors.

PubMed

Godoy-Lorite, Antonia; Guimerà, Roger; Sales-Pardo, Marta

2016-01-01

In social networks, individuals constantly drop ties and replace them by new ones in a highly unpredictable fashion. This highly dynamical nature of social ties has important implications for processes such as the spread of information or of epidemics. Several studies have demonstrated the influence of a number of factors on the intricate microscopic process of tie replacement, but the macroscopic long-term effects of such changes remain largely unexplored. Here we investigate whether, despite the inherent randomness at the microscopic level, there are macroscopic statistical regularities in the long-term evolution of social networks. In particular, we analyze the email network of a large organization with over 1,000 individuals throughout four consecutive years. We find that, although the evolution of individual ties is highly unpredictable, the macro-evolution of social communication networks follows well-defined statistical patterns, characterized by exponentially decaying log-variations of the weight of social ties and of individuals' social strength. At the same time, we find that individuals have social signatures and communication strategies that are remarkably stable over the scale of several years.

A Multilayer Naïve Bayes Model for Analyzing User's Retweeting Sentiment Tendency.

PubMed

Wang, Mengmeng; Zuo, Wanli; Wang, Ying

2015-01-01

Today microblogging has increasingly become a means of information diffusion via user's retweeting behavior. Since retweeting content, as context information of microblogging, is an understanding of microblogging, hence, user's retweeting sentiment tendency analysis has gradually become a hot research topic. Targeted at online microblogging, a dynamic social network, we investigate how to exploit dynamic retweeting sentiment features in retweeting sentiment tendency analysis. On the basis of time series of user's network structure information and published text information, we first model dynamic retweeting sentiment features. Then we build Naïve Bayes models from profile-, relationship-, and emotion-based dimensions, respectively. Finally, we build a multilayer Naïve Bayes model based on multidimensional Naïve Bayes models to analyze user's retweeting sentiment tendency towards a microblog. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of dynamic retweeting sentiment features and temporal information in retweeting sentiment tendency analysis. What is more, we provide a new train of thought for retweeting sentiment tendency analysis in dynamic social networks.

Challenges in network science: Applications to infrastructures, climate, social systems and economics

NASA Astrophysics Data System (ADS)

Havlin, S.; Kenett, D. Y.; Ben-Jacob, E.; Bunde, A.; Cohen, R.; Hermann, H.; Kantelhardt, J. W.; Kertész, J.; Kirkpatrick, S.; Kurths, J.; Portugali, J.; Solomon, S.

2012-11-01

Network theory has become one of the most visible theoretical frameworks that can be applied to the description, analysis, understanding, design and repair of multi-level complex systems. Complex networks occur everywhere, in man-made and human social systems, in organic and inorganic matter, from nano to macro scales, and in natural and anthropogenic structures. New applications are developed at an ever-increasing rate and the promise for future growth is high, since increasingly we interact with one another within these vital and complex environments. Despite all the great successes of this field, crucial aspects of multi-level complex systems have been largely ignored. Important challenges of network science are to take into account many of these missing realistic features such as strong coupling between networks (networks are not isolated), the dynamics of networks (networks are not static), interrelationships between structure, dynamics and function of networks, interdependencies in given networks (and other classes of links, including different signs of interactions), and spatial properties (including geographical aspects) of networks. This aim of this paper is to introduce and discuss the challenges that future network science needs to address, and how different disciplines will be accordingly affected.

Dynamical networks of influence in small group discussions

PubMed Central

Noriega Campero, Alejandro; Almaatouq, Abdullah

2018-01-01

In many domains of life, business and management, numerous problems are addressed by small groups of individuals engaged in face-to-face discussions. While research in social psychology has a long history of studying the determinants of small group performances, the internal dynamics that govern a group discussion are not yet well understood. Here, we rely on computational methods based on network analyses and opinion dynamics to describe how individuals influence each other during a group discussion. We consider the situation in which a small group of three individuals engages in a discussion to solve an estimation task. We propose a model describing how group members gradually influence each other and revise their judgments over the course of the discussion. The main component of the model is an influence network—a weighted, directed graph that determines the extent to which individuals influence each other during the discussion. In simulations, we first study the optimal structure of the influence network that yields the best group performances. Then, we implement a social learning process by which individuals adapt to the past performance of their peers, thereby affecting the structure of the influence network in the long run. We explore the mechanisms underlying the emergence of efficient or maladaptive networks and show that the influence network can converge towards the optimal one, but only when individuals exhibit a social discounting bias by downgrading the relative performances of their peers. Finally, we find a late-speaker effect, whereby individuals who speak later in the discussion are perceived more positively in the long run and are thus more influential. The numerous predictions of the model can serve as a basis for future experiments, and this work opens research on small group discussion to computational social sciences. PMID:29338013

Gray matter volume of the anterior insular cortex and social networking.

PubMed

Spagna, Alfredo; Dufford, Alexander J; Wu, Qiong; Wu, Tingting; Zheng, Weihao; Coons, Edgar E; Hof, Patrick R; Hu, Bin; Wu, Yanhong; Fan, Jin

2018-05-01

In human life, social context requires the engagement in complex interactions among individuals as the dynamics of social networks. The evolution of the brain as the neurological basis of the mind must be crucial in supporting social networking. Although the relationship between social networking and the amygdala, a small but core region for emotion processing, has been reported, other structures supporting sophisticated social interactions must be involved and need to be identified. In this study, we examined the relationship between morphology of the anterior insular cortex (AIC), a structure involved in basic and high-level cognition, and social networking. Two independent cohorts of individuals (New York group n = 50, Beijing group n = 100) were recruited. Structural magnetic resonance images were acquired and the social network index (SNI), a composite measure summarizing an individual's network diversity, size, and complexity, was measured. The association between morphological features of the AIC, in addition to amygdala, and the SNI was examined. Positive correlations between the measures of the volume as well as sulcal depth of the AIC and the SNI were found in both groups, while a significant positive correlation between the volume of the amygdala and the SNI was only found in the New York group. The converging results from the two groups suggest that the AIC supports network-level social interactions. © 2018 Wiley Periodicals, Inc.

Networks of Learning

NASA Astrophysics Data System (ADS)

Bettencourt, Luis; Kaiser, David

2004-03-01

Based on an a historically documented example of scientific discovery - Feynman diagrams as the main calculational tool of theoretical high energy Physics - we map the time evolution of the social network of early adopters through in the US, UK, Japan and the USSR. The spread of the technique for total number of users in each region is then modelled in terms of epidemic models, highlighting parallel and divergent aspects of this analogy. We also show that transient social arrangements develop as the idea is introduced and learned, which later disappear as the technique becomes common knowledge. Such early transient is characterized by abnormally low connectivity distribution powers and by high clustering. This interesting early non-equilibrium stage of network evolution is captured by a new dynamical model for network evolution, which coincides in its long time limit with familiar preferential aggregation dynamics.

Synergistic effects in threshold models on networks.

PubMed

Juul, Jonas S; Porter, Mason A

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can-depending on a parameter-either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

Synergistic effects in threshold models on networks

NASA Astrophysics Data System (ADS)

Juul, Jonas S.; Porter, Mason A.

2018-01-01

Network structure can have a significant impact on the propagation of diseases, memes, and information on social networks. Different types of spreading processes (and other dynamical processes) are affected by network architecture in different ways, and it is important to develop tractable models of spreading processes on networks to explore such issues. In this paper, we incorporate the idea of synergy into a two-state ("active" or "passive") threshold model of social influence on networks. Our model's update rule is deterministic, and the influence of each meme-carrying (i.e., active) neighbor can—depending on a parameter—either be enhanced or inhibited by an amount that depends on the number of active neighbors of a node. Such a synergistic system models social behavior in which the willingness to adopt either accelerates or saturates in a way that depends on the number of neighbors who have adopted that behavior. We illustrate that our model's synergy parameter has a crucial effect on system dynamics, as it determines whether degree-k nodes are possible or impossible to activate. We simulate synergistic meme spreading on both random-graph models and networks constructed from empirical data. Using a heterogeneous mean-field approximation, which we derive under the assumption that a network is locally tree-like, we are able to determine which synergy-parameter values allow degree-k nodes to be activated for many networks and for a broad family of synergistic models.

An incremental community detection method for social tagging systems using locality-sensitive hashing.

PubMed

Wu, Zhenyu; Zou, Ming

2014-10-01

An increasing number of users interact, collaborate, and share information through social networks. Unprecedented growth in social networks is generating a significant amount of unstructured social data. From such data, distilling communities where users have common interests and tracking variations of users' interests over time are important research tracks in fields such as opinion mining, trend prediction, and personalized services. However, these tasks are extremely difficult considering the highly dynamic characteristics of the data. Existing community detection methods are time consuming, making it difficult to process data in real time. In this paper, dynamic unstructured data is modeled as a stream. Tag assignments stream clustering (TASC), an incremental scalable community detection method, is proposed based on locality-sensitive hashing. Both tags and latent interactions among users are incorporated in the method. In our experiments, the social dynamic behaviors of users are first analyzed. The proposed TASC method is then compared with state-of-the-art clustering methods such as StreamKmeans and incremental k-clique; results indicate that TASC can detect communities more efficiently and effectively. Copyright © 2014 Elsevier Ltd. All rights reserved.

Friendship Dissolution Within Social Networks Modeled Through Multilevel Event History Analysis

PubMed Central

Dean, Danielle O.; Bauer, Daniel J.; Prinstein, Mitchell J.

2018-01-01

A social network perspective can bring important insight into the processes that shape human behavior. Longitudinal social network data, measuring relations between individuals over time, has become increasingly common—as have the methods available to analyze such data. A friendship duration model utilizing discrete-time multilevel survival analysis with a multiple membership random effect structure is developed and applied here to study the processes leading to undirected friendship dissolution within a larger social network. While the modeling framework is introduced in terms of understanding friendship dissolution, it can be used to understand microlevel dynamics of a social network more generally. These models can be fit with standard generalized linear mixed-model software, after transforming the data to a pair-period data set. An empirical example highlights how the model can be applied to understand the processes leading to friendship dissolution between high school students, and a simulation study is used to test the use of the modeling framework under representative conditions that would be found in social network data. Advantages of the modeling framework are highlighted, and potential limitations and future directions are discussed. PMID:28463022

Collective decision dynamics in the presence of external drivers

NASA Astrophysics Data System (ADS)

Bassett, Danielle S.; Alderson, David L.; Carlson, Jean M.

2012-09-01

We develop a sequence of models describing information transmission and decision dynamics for a network of individual agents subject to multiple sources of influence. Our general framework is set in the context of an impending natural disaster, where individuals, represented by nodes on the network, must decide whether or not to evacuate. Sources of influence include a one-to-many externally driven global broadcast as well as pairwise interactions, across links in the network, in which agents transmit either continuous opinions or binary actions. We consider both uniform and variable threshold rules on the individual opinion as baseline models for decision making. Our results indicate that (1) social networks lead to clustering and cohesive action among individuals, (2) binary information introduces high temporal variability and stagnation, and (3) information transmission over the network can either facilitate or hinder action adoption, depending on the influence of the global broadcast relative to the social network. Our framework highlights the essential role of local interactions between agents in predicting collective behavior of the population as a whole.

The dynamics of transmission and the dynamics of networks.

PubMed

Farine, Damien

2017-05-01

A toy example depicted here highlighting the results of a study in this issue of the Journal of Animal Ecology that investigates the impact of network dynamics on potential disease outbreaks. Infections (stars) that spread by contact only (left) reduce the predicted outbreak size compared to situations where individuals can become infected by moving through areas that previously contained infected individuals (right). This is potentially important in species where individuals, or in this case groups, have overlapping ranges (as depicted on the top right). Incorporating network dynamics that maintain information about the ordering of contacts (central blocks; including the ordering of spatial overlap as noted by the arrows that highlight the blue group arriving after the red group in top-right of the figure) is important for capturing how a disease might not have the opportunity to spread to all individuals. By contrast, a static or 'average' network (lower blocks) does not capture any of these dynamics. Interestingly, although static networks generally predict larger outbreak sizes, the authors find that in cases when transmission probability is low, this prediction can switch as a result of changes in the estimated intensity of contacts among individuals. [Colour figure can be viewed at wileyonlinelibrary.com]. Springer, A., Kappeler, P.M. & Nunn, C.L. (2017) Dynamic vs. static social networks in models of parasite transmission: Predicting Cryptosporidium spread in wild lemurs. Journal of Animal Ecology, 86, 419-433. The spread of disease or information through networks can be affected by several factors. Whether and how these factors are accounted for can fundamentally change the predicted impact of a spreading epidemic. Springer, Kappeler & Nunn () investigate the role of different modes of transmission and network dynamics on the predicted size of a disease outbreak across several groups of Verreaux's sifakas, a group-living species of lemur. While some factors, such as seasonality, led to consistent differences in the structure of social networks, using dynamic vs. static representations of networks generated differences in the predicted outbreak size of an emergent disease. These findings highlight some of the challenges associated with studying disease dynamics in animal populations, and the importance of continuing efforts to develop the network tools needed to study disease spread. © 2017 The Author. Journal of Animal Ecology © 2017 British Ecological Society.

Inferring Structure and Forecasting Dynamics on Evolving Networks

DTIC Science & Technology

2016-01-05

Graphs ........................................................................................................................ 23 7. Sacred Values...5) Team Formation; (6) Games of Graphs; (7) Sacred Values and Legitimacy in Network Interactions; (8) Network processes in Geo-Social Context. 1...Authority, Cooperation and Competition in Religious Networks Key Papers: McBride 2015a [72] and McBride 2015b [73] McBride (2015a) examines

Multi-Topic Tracking Model for dynamic social network

NASA Astrophysics Data System (ADS)

Li, Yuhua; Liu, Changzheng; Zhao, Ming; Li, Ruixuan; Xiao, Hailing; Wang, Kai; Zhang, Jun

2016-07-01

The topic tracking problem has attracted much attention in the last decades. However, existing approaches rarely consider network structures and textual topics together. In this paper, we propose a novel statistical model based on dynamic bayesian network, namely Multi-Topic Tracking Model for Dynamic Social Network (MTTD). It takes influence phenomenon, selection phenomenon, document generative process and the evolution of textual topics into account. Specifically, in our MTTD model, Gibbs Random Field is defined to model the influence of historical status of users in the network and the interdependency between them in order to consider the influence phenomenon. To address the selection phenomenon, a stochastic block model is used to model the link generation process based on the users' interests to topics. Probabilistic Latent Semantic Analysis (PLSA) is used to describe the document generative process according to the users' interests. Finally, the dependence on the historical topic status is also considered to ensure the continuity of the topic itself in topic evolution model. Expectation Maximization (EM) algorithm is utilized to estimate parameters in the proposed MTTD model. Empirical experiments on real datasets show that the MTTD model performs better than Popular Event Tracking (PET) and Dynamic Topic Model (DTM) in generalization performance, topic interpretability performance, topic content evolution and topic popularity evolution performance.

Impaired social brain network for processing dynamic facial expressions in autism spectrum disorders

PubMed Central

2012-01-01

Background Impairment of social interaction via facial expressions represents a core clinical feature of autism spectrum disorders (ASD). However, the neural correlates of this dysfunction remain unidentified. Because this dysfunction is manifested in real-life situations, we hypothesized that the observation of dynamic, compared with static, facial expressions would reveal abnormal brain functioning in individuals with ASD. We presented dynamic and static facial expressions of fear and happiness to individuals with high-functioning ASD and to age- and sex-matched typically developing controls and recorded their brain activities using functional magnetic resonance imaging (fMRI). Result Regional analysis revealed reduced activation of several brain regions in the ASD group compared with controls in response to dynamic versus static facial expressions, including the middle temporal gyrus (MTG), fusiform gyrus, amygdala, medial prefrontal cortex, and inferior frontal gyrus (IFG). Dynamic causal modeling analyses revealed that bi-directional effective connectivity involving the primary visual cortex–MTG–IFG circuit was enhanced in response to dynamic as compared with static facial expressions in the control group. Group comparisons revealed that all these modulatory effects were weaker in the ASD group than in the control group. Conclusions These results suggest that weak activity and connectivity of the social brain network underlie the impairment in social interaction involving dynamic facial expressions in individuals with ASD. PMID:22889284

Incorporating Resilience into Dynamic Social Models

DTIC Science & Technology

2016-07-20

solved by simply using the information provided by the scenario. Instead, additional knowledge is required from relevant fields that study these...resilience function by leveraging Bayesian Knowledge Bases (BKBs), a probabilistic reasoning network framework[5],[6]. BKBs allow for inferencing...reasoning network framework based on Bayesian Knowledge Bases (BKBs). BKBs are central to our social resilience framework as they are used to

The Dynamics of Network Change for Older People: Social Policy and Social Theory.

ERIC Educational Resources Information Center

Litwak, Eugene; Kulis, Steve

This paper discusses how networks of elderly people change with stages of disability. Senior citizens in 80 nursing homes (40 in New York and 40 in Florida) were interviewed. The authors examine how spouses, relatives, neighbors, friends, and acquaintances help out when the elderly are robust, how these patterns of help change when the elderly…

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

ERIC Educational Resources Information Center

Lockhart, Naorah C.

2017-01-01

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

Amigos and Amistades: The role of men’s social network ties in shaping HIV vulnerability in the Dominican Republic

PubMed Central

Fleming, Paul J.; Barrington, Clare; Perez, Martha; Donastorg, Yeycy; Kerrigan, Deanna

2014-01-01

While men’s social networks have been identified as a source of influence on sexual behaviour, less is known about the different types of friendship ties within men’s networks. We analysed data from qualitative in-depth interviews with 36 men in Santo Domingo, Dominican Republic who were current or former sexual partners of female sex workers to understand how a) men describe trust and communication with different types of friendship ties, b) characteristics of trust and communication reflect norms of masculinity, and c) these friendship ties influence HIV-related behaviours. We identified a distinction between amistades, social drinking buddies who are not trusted, and amigos, trusted friends. The majority of men lacked any amigos and some had neither amigos nor amistades. In general, men reflected traditional norms of masculinity and said they did not feel they could discuss their relationships or emotional topics with other men. Trust and communication dynamics, and how norms of masculinity shape those dynamics, should be understood and addressed in the design of HIV prevention efforts with men’s social networks as they have implications for the potential effectiveness of such efforts. PMID:24939001

Structure-based control of complex networks with nonlinear dynamics

NASA Astrophysics Data System (ADS)

Zanudo, Jorge G. T.; Yang, Gang; Albert, Reka

What can we learn about controlling a system solely from its underlying network structure? Here we use a framework for control of networks governed by a broad class of nonlinear dynamics that includes the major dynamic models of biological, technological, and social processes. This feedback-based framework provides realizable node overrides that steer a system towards any of its natural long term dynamic behaviors, regardless of the dynamic details and system parameters. We use this framework on several real networks, identify the topological characteristics that underlie the predicted node overrides, and compare its predictions to those of classical structural control theory. Finally, we demonstrate this framework's applicability in dynamic models of gene regulatory networks and identify nodes whose override is necessary for control in the general case, but not in specific model instances. This work was supported by NSF Grants PHY 1205840 and IIS 1160995. JGTZ is a recipient of a Stand Up To Cancer - The V Foundation Convergence Scholar Award.

Epidemic dynamics and endemic states in complex networks

NASA Astrophysics Data System (ADS)

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.

Social Networks among the Older Chinese Population in the USA: Findings from the PINE Study.

PubMed

Dong, XinQi; Chang, E-Shien

2017-01-01

Social network research has become central to studies of health and aging. Its results may yield public health insights that are actionable and improve the quality of life of older adults. However, little is known about the social networks of older immigrant adults, whose social relationships often develop in the context of migration, compounded by cultural and linguistic barriers. This report aims to describe the structure, composition, and emotional components of social networks in the Chinese aging population of the USA, and to explore ways in which their social networks may be critical to their health decision-making. Our data come from the PINE study, a population-based epidemiological study of community-dwelling older Chinese American adults, aged 60 years and above, in the greater Chicago area. We conducted individual interviews in participants' homes from 2011 until 2013. Based on sociodemographic and socioeconomic characteristics, this study computed descriptive statistics and trend tests for the social network measures adapted from the National Social Life, Health, and Aging Project study. The findings show that older Chinese adults have a relatively small social network in comparison with their counterparts from other ethnic and racial backgrounds. Only 29.6% of the participants could name 5 close network members, and 2.2% could name 0 members. Their network composition was more heavily kin oriented (95.0%). Relationships with network members differed according to the older adults' sociodemographic and socioeconomic characteristics. Subgroup variations included the likelihood of discussing health-related issues with network members. This study highlights the dynamic nature of social networks in later-life Chinese immigrants. For healthcare practitioners, developing cost-effective strategies that can mobilize social network support remains a critical undertaking in health intervention. Longitudinal studies are needed to examine the causal impact of social networks on various domains of health. © 2017 S. Karger AG, Basel.

Bipartite graphs as models of population structures in evolutionary multiplayer games.

PubMed

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, "games on graphs" study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner's dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner's dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures.

Opinion formation in a social network: The role of human activity

NASA Astrophysics Data System (ADS)

Grabowski, Andrzej

2009-03-01

The model of opinion formation in human population based on social impact theory is investigated numerically. On the basis of a database received from the on-line game server, we examine the structure of social network and human dynamics. We calculate the activity of individuals, i.e. the relative time devoted daily to interactions with others in the artificial society. We study the influence of correlation between the activity of an individual and its connectivity on the process of opinion formation. We find that such correlations have a significant influence on the temperature of the phase transition and the effect of the mass media, modeled as an external stimulation acting on the social network.

Modelling information dissemination under privacy concerns in social media

NASA Astrophysics Data System (ADS)

Zhu, Hui; Huang, Cheng; Lu, Rongxing; Li, Hui

2016-05-01

Social media has recently become an important platform for users to share news, express views, and post messages. However, due to user privacy preservation in social media, many privacy setting tools are employed, which inevitably change the patterns and dynamics of information dissemination. In this study, a general stochastic model using dynamic evolution equations was introduced to illustrate how privacy concerns impact the process of information dissemination. Extensive simulations and analyzes involving the privacy settings of general users, privileged users, and pure observers were conducted on real-world networks, and the results demonstrated that user privacy settings affect information differently. Finally, we also studied the process of information diffusion analytically and numerically with different privacy settings using two classic networks.

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.

Linking behavior in the physics education research coauthorship network

NASA Astrophysics Data System (ADS)

Anderson, Katharine A.; Crespi, Matthew; Sayre, Eleanor C.

2017-06-01

There is considerable long-term interest in understanding the dynamics of collaboration networks, and how these networks form and evolve over time. Most of the work done on the dynamics of social networks focuses on well-established communities. Work examining emerging social networks is rarer, simply because data are difficult to obtain in real time. In this paper, we use thirty years of data from an emerging scientific community to look at that crucial early stage in the development of a social network. We show that when the field was very young, islands of individual researchers labored in relative isolation, and the coauthorship network was disconnected. Thirty years later, rather than a cluster of individuals, we find a true collaborative community, bound together by a robust collaboration network. However, this change did not take place gradually—the network remained a loose assortment of isolated individuals until the mid 2000s, when those smaller parts suddenly knit themselves together into a single whole. In the rest of this paper, we consider the role of three factors in these observed structural changes: growth, changes in social norms, and the introduction of institutions such as field-specific conferences and journals. We have data from the very earliest years of the field, a period which includes the introduction of two different institutions: the first field-specific conference, and the first field-specific journals. We also identify two relevant behavioral shifts: a discrete increase in coauthorship coincident with the first conference, and a shift among established authors away from collaborating with outsiders, towards collaborating with each other. The interaction of these factors gives us insight into the formation of collaboration networks more broadly.

A common neural network differentially mediates direct and social fear learning.

PubMed

Lindström, Björn; Haaker, Jan; Olsson, Andreas

2018-02-15

Across species, fears often spread between individuals through social learning. Yet, little is known about the neural and computational mechanisms underlying social learning. Addressing this question, we compared social and direct (Pavlovian) fear learning showing that they showed indistinguishable behavioral effects, and involved the same cross-modal (self/other) aversive learning network, centered on the amygdala, the anterior insula (AI), and the anterior cingulate cortex (ACC). Crucially, the information flow within this network differed between social and direct fear learning. Dynamic causal modeling combined with reinforcement learning modeling revealed that the amygdala and AI provided input to this network during direct and social learning, respectively. Furthermore, the AI gated learning signals based on surprise (associability), which were conveyed to the ACC, in both learning modalities. Our findings provide insights into the mechanisms underlying social fear learning, with implications for understanding common psychological dysfunctions, such as phobias and other anxiety disorders. Copyright © 2017 Elsevier Inc. All rights reserved.

Analyzing heterogeneity in the effects of physical activity in children on social network structure and peer selection dynamics

PubMed Central

Henry, Teague; Gesell, Sabina B.; Ip, Edward H.

2016-01-01

Background Social networks influence children and adolescents’ physical activity. The focus of this paper is to examine the differences in the effects of physical activity on friendship selection, with eye to the implications on physical activity interventions for young children. Network interventions to increase physical activity are warranted but have not been conducted. Prior to implementing a network intervention in the field, it is important to understand potential heterogeneities in the effects that activity level have on network structure. In this study, the associations between activity level and cross sectional network structure, and activity level and change in network structure are assessed. Methods We studied a real-world friendship network among 81 children (average age 7.96 years) who lived in low SES neighborhoods, attended public schools, and attended one of two structured aftercare programs, of which one has existed and the other was new. We used the exponential random graph model (ERGMs) and its longitudinal extension to evaluate the association between activity level and various demographic factors in having, forming, and dissolving friendship. Due to heterogeneity between the friendship networks within the aftercare programs, separate analyses were conducted for each network. Results There was heterogeneity in the effect of physical activity on both cross sectional network structure and the formation and dissolution processes, both across time and between networks. Conclusions Network analysis could be used to assess the unique structure and dynamics of a social network before an intervention is implemented, so as to optimize the effects of the network intervention for increasing childhood physical activity. Additionally, if peer selection processes are changing within a network, a static network intervention strategy for childhood physical activity could become inefficient as the network evolves. PMID:27867518

Individual heterogeneity generating explosive system network dynamics.

PubMed

Manrique, Pedro D; Johnson, Neil F

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Individual heterogeneity generating explosive system network dynamics

NASA Astrophysics Data System (ADS)

Manrique, Pedro D.; Johnson, Neil F.

2018-03-01

Individual heterogeneity is a key characteristic of many real-world systems, from organisms to humans. However, its role in determining the system's collective dynamics is not well understood. Here we study how individual heterogeneity impacts the system network dynamics by comparing linking mechanisms that favor similar or dissimilar individuals. We find that this heterogeneity-based evolution drives an unconventional form of explosive network behavior, and it dictates how a polarized population moves toward consensus. Our model shows good agreement with data from both biological and social science domains. We conclude that individual heterogeneity likely plays a key role in the collective development of real-world networks and communities, and it cannot be ignored.

Multi-Relational Characterization of Dynamic Social Network Communities

NASA Astrophysics Data System (ADS)

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

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

Stable configurations in social networks

NASA Astrophysics Data System (ADS)

Bronski, Jared C.; DeVille, Lee; Ferguson, Timothy; Livesay, Michael

2018-06-01

We present and analyze a model of opinion formation on an arbitrary network whose dynamics comes from a global energy function. We study the global and local minimizers of this energy, which we call stable opinion configurations, and describe the global minimizers under certain assumptions on the friendship graph. We show a surprising result that the number of stable configurations is not necessarily monotone in the strength of connection in the social network, i.e. the model sometimes supports more stable configurations when the interpersonal connections are made stronger.

Epidemics on adaptive networks with geometric constraints

NASA Astrophysics Data System (ADS)

Shaw, Leah; Schwartz, Ira

2008-03-01

When a population is faced with an epidemic outbreak, individuals may modify their social behavior to avoid exposure to the disease. Recent work has considered models in which the contact network is rewired dynamically so that susceptibles avoid contact with infectives. We consider extensions in which the rewiring is subject to constraints that preserve key properties of the social network structure. Constraining to a fixed degree distribution destroys previously observed bistable behavior. The most effective rewiring strategy is found to depend on the spreading rate.

Quality versus quantity of social ties in experimental cooperative networks.

PubMed

Shirado, Hirokazu; Fu, Feng; Fowler, James H; Christakis, Nicholas A

2013-01-01

Recent studies suggest that allowing individuals to choose their partners can help to maintain cooperation in human social networks; this behaviour can supplement behavioural reciprocity, whereby humans are influenced to cooperate by peer pressure. However, it is unknown how the rate of forming and breaking social ties affects our capacity to cooperate. Here we use a series of online experiments involving 1,529 unique participants embedded in 90 experimental networks, to show that there is a 'Goldilocks' effect of network dynamism on cooperation. When the rate of change in social ties is too low, subjects choose to have many ties, even if they attach to defectors. When the rate is too high, cooperators cannot detach from defectors as much as defectors re-attach and, hence, subjects resort to behavioural reciprocity and switch their behaviour to defection. Optimal levels of cooperation are achieved at intermediate levels of change in social ties.

Quality versus quantity of social ties in experimental cooperative networks

PubMed Central

Shirado, Hirokazu; Fu, Feng; Fowler, James H.; Christakis, Nicholas A.

2013-01-01

Recent studies suggest that allowing individuals to choose their partners can help to maintain cooperation in human social networks; this behaviour can supplement behavioural reciprocity, whereby humans are influenced to cooperate by peer pressure. However, it is unknown how the rate of forming and breaking social ties affects our capacity to cooperate. Here we use a series of online experiments involving 1,529 unique participants embedded in 90 experimental networks, to show that there is a ‘Goldilocks’ effect of network dynamism on cooperation. When the rate of change in social ties is too low, subjects choose to have many ties, even if they attach to defectors. When the rate is too high, cooperators cannot detach from defectors as much as defectors re-attach and, hence, subjects resort to behavioural reciprocity and switch their behaviour to defection. Optimal levels of cooperation are achieved at intermediate levels of change in social ties. PMID:24226079

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.

What's in a crowd? Analysis of face-to-face behavioral networks.

PubMed

Isella, Lorenzo; Stehlé, Juliette; Barrat, Alain; Cattuto, Ciro; Pinton, Jean-François; Van den Broeck, Wouter

2011-02-21

The availability of new data sources on human mobility is opening new avenues for investigating the interplay of social networks, human mobility and dynamical processes such as epidemic spreading. Here we analyze data on the time-resolved face-to-face proximity of individuals in large-scale real-world scenarios. We compare two settings with very different properties, a scientific conference and a long-running museum exhibition. We track the behavioral networks of face-to-face proximity, and characterize them from both a static and a dynamic point of view, exposing differences and similarities. We use our data to investigate the dynamics of a susceptible-infected model for epidemic spreading that unfolds on the dynamical networks of human proximity. The spreading patterns are markedly different for the conference and the museum case, and they are strongly impacted by the causal structure of the network data. A deeper study of the spreading paths shows that the mere knowledge of static aggregated networks would lead to erroneous conclusions about the transmission paths on the dynamical networks. Copyright © 2010 Elsevier Ltd. All rights reserved.

Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants

PubMed Central

Hogan, Bernie; Melville, Joshua R.; Philips, Gregory Lee; Janulis, Patrick; Contractor, Noshir; Mustanski, Brian S.; Birkett, Michelle

2016-01-01

While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed netCanvas that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within–subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks. PMID:28018995

Evaluating the Paper-to-Screen Translation of Participant-Aided Sociograms with High-Risk Participants.

PubMed

Hogan, Bernie; Melville, Joshua R; Philips, Gregory Lee; Janulis, Patrick; Contractor, Noshir; Mustanski, Brian S; Birkett, Michelle

2016-05-01

While much social network data exists online, key network metrics for high-risk populations must still be captured through self-report. This practice has suffered from numerous limitations in workflow and response burden. However, advances in technology, network drawing libraries and databases are making interactive network drawing increasingly feasible. We describe the translation of an analog-based technique for capturing personal networks into a digital framework termed netCanvas that addresses many existing shortcomings such as: 1) complex data entry; 2) extensive interviewer intervention and field setup; 3) difficulties in data reuse; and 4) a lack of dynamic visualizations. We test this implementation within a health behavior study of a high-risk and difficult-to-reach population. We provide a within-subjects comparison between paper and touchscreens. We assert that touchscreen-based social network capture is now a viable alternative for highly sensitive data and social network data entry tasks.

Temporal efficiency evaluation and small-worldness characterization in temporal networks

PubMed Central

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-01-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks. PMID:27682314

Temporal efficiency evaluation and small-worldness characterization in temporal networks

NASA Astrophysics Data System (ADS)

Dai, Zhongxiang; Chen, Yu; Li, Junhua; Fam, Johnson; Bezerianos, Anastasios; Sun, Yu

2016-09-01

Numerous real-world systems can be modeled as networks. To date, most network studies have been conducted assuming stationary network characteristics. Many systems, however, undergo topological changes over time. Temporal networks, which incorporate time into conventional network models, are therefore more accurate representations of such dynamic systems. Here, we introduce a novel generalized analytical framework for temporal networks, which enables 1) robust evaluation of the efficiency of temporal information exchange using two new network metrics and 2) quantitative inspection of the temporal small-worldness. Specifically, we define new robust temporal network efficiency measures by incorporating the time dependency of temporal distance. We propose a temporal regular network model, and based on this plus the redefined temporal efficiency metrics and widely used temporal random network models, we introduce a quantitative approach for identifying temporal small-world architectures (featuring high temporal network efficiency both globally and locally). In addition, within this framework, we can uncover network-specific dynamic structures. Applications to brain networks, international trade networks, and social networks reveal prominent temporal small-world properties with distinct dynamic network structures. We believe that the framework can provide further insight into dynamic changes in the network topology of various real-world systems and significantly promote research on temporal networks.

A spread willingness computing-based information dissemination model.

PubMed

Huang, Haojing; Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network.

A Spread Willingness Computing-Based Information Dissemination Model

PubMed Central

Cui, Zhiming; Zhang, Shukui

2014-01-01

This paper constructs a kind of spread willingness computing based on information dissemination model for social network. The model takes into account the impact of node degree and dissemination mechanism, combined with the complex network theory and dynamics of infectious diseases, and further establishes the dynamical evolution equations. Equations characterize the evolutionary relationship between different types of nodes with time. The spread willingness computing contains three factors which have impact on user's spread behavior: strength of the relationship between the nodes, views identity, and frequency of contact. Simulation results show that different degrees of nodes show the same trend in the network, and even if the degree of node is very small, there is likelihood of a large area of information dissemination. The weaker the relationship between nodes, the higher probability of views selection and the higher the frequency of contact with information so that information spreads rapidly and leads to a wide range of dissemination. As the dissemination probability and immune probability change, the speed of information dissemination is also changing accordingly. The studies meet social networking features and can help to master the behavior of users and understand and analyze characteristics of information dissemination in social network. PMID:25110738

Studies of Opinion Stability for Small Dynamic Networks with Opportunistic Agents

NASA Astrophysics Data System (ADS)

Sobkowicz, Pawel

There are numerous examples of societies with extremely stable mix of contrasting opinions. We argue that this stability is a result of an interplay between society network topology adjustment and opinion changing processes. To support this position we present a computer model of opinion formation based on some novel assumptions, designed to bring the model closer to social reality. In our model, the agents, in addition to changing their opinions due to influence of the rest of society and external propaganda, have the ability to modify their social network, forming links with agents sharing the same opinions and cutting the links with those they disagree with. To improve the model further we divide the agents into "fanatics" and "opportunists," depending on how easy it is to change their opinions. The simulations show significant differences compared to traditional models, where network links are static. In particular, for the dynamical model where inter-agent links are adjustable, the final network structure and opinion distribution is shown to resemble real world observations, such as social structures and persistence of minority groups even when most of the society is against them and the propaganda is strong.

The dynamics of socio-connective trust within support networks accessed by informal caregivers.

PubMed

Ray, Robin A; Street, Annette F

2011-03-01

This article introduces the concept of socio-connective trust, the synapse between the social structures and processes that underpin relationships in supportive care networks. Data from an ethnographic case study of 18 informal caregivers providing in-home care for people with life-limiting illness were analysed drawing on theoretical concepts from the work of Giddens and writings on social capital, as well as the construction of trust in the caregiving literature. While conceptions of trust were found to contribute to understanding supportive care relationships, they did not account for the dynamic nature of the availability and use of support networks. Instead, informal caregivers undertook ongoing reflexive negotiation of relationship boundaries in response to their own conception of the current situation and their perception of trust in their relationships with the various members of the support network. The concept of socio-connective trust describes the movement and flow of the flexible bonds that influence relationships among care networks and determine the type and range of support accessed by informal caregivers. Understanding the complexities of socio-connective trust in caregiving relationships will assist health and social care workers to mobilize relevant resources to support informal caregivers.

Predicting the global spread range via small subnetworks

NASA Astrophysics Data System (ADS)

Sun, Jiachen; Dong, Junyou; Ma, Xiao; Feng, Ling; Hu, Yanqing

2017-04-01

Modern online social network platforms are replacing traditional media due to their effectiveness in both spreading information and communicating opinions. One of the key problems in these online platforms is to predict the global spread range of any given information. Due to its gigantic size as well as time-varying dynamics, an online social network's global structure, however, is usually inaccessible to most researchers. Thus, it raises the very important issue of how to use solely small subnetworks to predict the global influence. In this paper, based on percolation theory, we show that the global spread range can be predicted well from only two small subnetworks. We test our methods in an artificial network and three empirical online social networks, such as the full Sina Weibo network with 99546027 nodes.

Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish

PubMed Central

Teles, Magda C.; Almeida, Olinda; Lopes, João S.; Oliveira, Rui F.

2015-01-01

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance. PMID:26423839

Social interactions elicit rapid shifts in functional connectivity in the social decision-making network of zebrafish.

PubMed

Teles, Magda C; Almeida, Olinda; Lopes, João S; Oliveira, Rui F

2015-10-07

According to the social decision-making (SDM) network hypothesis, SDM is encoded in a network of forebrain and midbrain structures in a distributed and dynamic fashion, such that the expression of a given social behaviour is better reflected by the overall profile of activation across the different loci rather than by the activity of a single node. This proposal has the implicit assumption that SDM relies on integration across brain regions, rather than on regional specialization. Here we tested the occurrence of functional localization and of functional connectivity in the SDM network. For this purpose we used zebrafish to map different social behaviour states into patterns of neuronal activity, as indicated by the expression of the immediate early genes c-fos and egr-1, across the SDM network. The results did not support functional localization, as some loci had similar patterns of activity associated with different social behaviour states, and showed socially driven changes in functional connectivity. Thus, this study provides functional support to the SDM network hypothesis and suggests that the neural context in which a given node of the network is operating (i.e. the state of its interconnected areas) is central to its functional relevance. © 2015 The Author(s).

Social insect colony as a biological regulatory system: modelling information flow in dominance networks.

PubMed

Nandi, Anjan K; Sumana, Annagiri; Bhattacharya, Kunal

2014-12-06

Social insects provide an excellent platform to investigate flow of information in regulatory systems since their successful social organization is essentially achieved by effective information transfer through complex connectivity patterns among the colony members. Network representation of such behavioural interactions offers a powerful tool for structural as well as dynamical analysis of the underlying regulatory systems. In this paper, we focus on the dominance interaction networks in the tropical social wasp Ropalidia marginata-a species where behavioural observations indicate that such interactions are principally responsible for the transfer of information between individuals about their colony needs, resulting in a regulation of their own activities. Our research reveals that the dominance networks of R. marginata are structurally similar to a class of naturally evolved information processing networks, a fact confirmed also by the predominance of a specific substructure-the 'feed-forward loop'-a key functional component in many other information transfer networks. The dynamical analysis through Boolean modelling confirms that the networks are sufficiently stable under small fluctuations and yet capable of more efficient information transfer compared to their randomized counterparts. Our results suggest the involvement of a common structural design principle in different biological regulatory systems and a possible similarity with respect to the effect of selection on the organization levels of such systems. The findings are also consistent with the hypothesis that dominance behaviour has been shaped by natural selection to co-opt the information transfer process in such social insect species, in addition to its primal function of mediation of reproductive competition in the colony. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Multi-scale compositionality: identifying the compositional structures of social dynamics using deep learning.

PubMed

Peng, Huan-Kai; Marculescu, Radu

2015-01-01

Social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signatures interact and form higher-level meanings. In this paper, we propose the Recursive Convolutional Bayesian Model (RCBM) to address both of these fundamental questions. The key idea behind our approach consists of constructing a deep-learning framework using specialized convolution operators that are designed to exploit the inherent heterogeneity of social dynamics. RCBM's runtime and convergence properties are guaranteed by formal analyses. Experimental results show that the proposed method outperforms the state-of-the-art approaches both in terms of solution quality and computational efficiency. Indeed, by applying the proposed method on two social network datasets, Twitter and Yelp, we are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. Finally, our analysis offers new insights on understanding and engineering social media dynamics, with direct applications to opinion spreading and online content promotion.

Multi-Scale Compositionality: Identifying the Compositional Structures of Social Dynamics Using Deep Learning

PubMed Central

Peng, Huan-Kai; Marculescu, Radu

2015-01-01

Objective Social media exhibit rich yet distinct temporal dynamics which cover a wide range of different scales. In order to study this complex dynamics, two fundamental questions revolve around (1) the signatures of social dynamics at different time scales, and (2) the way in which these signatures interact and form higher-level meanings. Method In this paper, we propose the Recursive Convolutional Bayesian Model (RCBM) to address both of these fundamental questions. The key idea behind our approach consists of constructing a deep-learning framework using specialized convolution operators that are designed to exploit the inherent heterogeneity of social dynamics. RCBM’s runtime and convergence properties are guaranteed by formal analyses. Results Experimental results show that the proposed method outperforms the state-of-the-art approaches both in terms of solution quality and computational efficiency. Indeed, by applying the proposed method on two social network datasets, Twitter and Yelp, we are able to identify the compositional structures that can accurately characterize the complex social dynamics from these two social media. We further show that identifying these patterns can enable new applications such as anomaly detection and improved social dynamics forecasting. Finally, our analysis offers new insights on understanding and engineering social media dynamics, with direct applications to opinion spreading and online content promotion. PMID:25830775

Exploring the Factors Contributing to Sibling Correlations in BMI: A Study Using the Panel Study of Income Dynamics

PubMed Central

Brown, Heather W.; Roberts, Jennifer

2012-01-01

Understanding the mechanisms contributing to correlated BMI outcomes in a social network such as siblings will help policy makers reduce the burden of disease associated with obesity. There are two potential mechanisms explaining correlated BMI outcomes in a biologically related social network: (i) time constant factors such as genetic heritability and habits formed during childhood and (ii) factors that change over time some of which are dependent on the frequency of interactions between the social network, for example, social norms shaped by the social network's shifting attitudes towards weight and behaviors related to weight, or environmental factors like opportunities for exercise. This study aims to distinguish between time constant factors from factors that are likely to change over time to gain a better understanding of the mechanisms explaining the correlation in sibling BMI. We exploit data from the Panel Study of Income Dynamics (PSID) over 1999–2007 estimating the correlation in BMI for adult siblings who currently live in separate households but grew-up in the same household and adolescent siblings currently living in the same household to isolate the influence of factors that change over time. The findings indicate that time constant factors explain some of the overall correlation in sibling BMI for both cohorts of siblings. Factors that change over time only significantly impact on the overall correlation in BMI for adolescent siblings suggesting if there is a social network influence on correlations in BMI this is facilitated by sharing the same household. PMID:22173572

Digging Deep: Teaching Social Studies through the Study of Archaeology.

ERIC Educational Resources Information Center

Wolf, Dennie Palmer, Ed.; Balick, Dana, Ed.; Craven, Julie, Ed.

This book outlines how to combine the skills of archaeology with the exploration of social studies in the classroom and illustrates how a network of teachers transformed their social studies courses into dynamic, multicultural inquiries using the tools and questions of archaeology. It explains how middle school social studies teachers tamed their…

Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility.

PubMed

Saggar, Manish; Shelly, Elizabeth Walter; Lepage, Jean-Francois; Hoeft, Fumiko; Reiss, Allan L

2014-01-01

Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks was found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus was found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) was partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom. © 2013.

Revealing the neural networks associated with processing of natural social interaction and the related effects of actor-orientation and face-visibility

PubMed Central

Saggar, Manish; Shelly, Elizabeth Walter; Lepage, Jean-Francois; Hoeft, Fumiko; Reiss, Allan L.

2013-01-01

Understanding the intentions and desires of those around us is vital for adapting to a dynamic social environment. In this paper, a novel event-related functional Magnetic Resonance Imaging (fMRI) paradigm with dynamic and natural stimuli (2s video clips) was developed to directly examine the neural networks associated with processing of gestures with social intent as compared to nonsocial intent. When comparing social to nonsocial gestures, increased activation in both the mentalizing (or theory of mind) and amygdala networks were found. As a secondary aim, a factor of actor-orientation was included in the paradigm to examine how the neural mechanisms differ with respect to personal engagement during a social interaction versus passively observing an interaction. Activity in the lateral occipital cortex and precentral gyrus were found sensitive to actor-orientation during social interactions. Lastly, by manipulating face-visibility we tested whether facial information alone is the primary driver of neural activation differences observed between social and nonsocial gestures. We discovered that activity in the posterior superior temporal sulcus (pSTS) and fusiform gyrus (FFG) were partially driven by observing facial expressions during social gestures. Altogether, using multiple factors associated with processing of natural social interaction, we conceptually advance our understanding of how social stimuli is processed in the brain and discuss the application of this paradigm to clinical populations where atypical social cognition is manifested as a key symptom. PMID:24084068

Interacting opinion and disease dynamics in multiplex networks: Discontinuous phase transition and nonmonotonic consensus times

NASA Astrophysics Data System (ADS)

Velásquez-Rojas, Fátima; Vazquez, Federico

2017-05-01

Opinion formation and disease spreading are among the most studied dynamical processes on complex networks. In real societies, it is expected that these two processes depend on and affect each other. However, little is known about the effects of opinion dynamics over disease dynamics and vice versa, since most studies treat them separately. In this work we study the dynamics of the voter model for opinion formation intertwined with that of the contact process for disease spreading, in a population of agents that interact via two types of connections, social and contact. These two interacting dynamics take place on two layers of networks, coupled through a fraction q of links present in both networks. The probability that an agent updates its state depends on both the opinion and disease states of the interacting partner. We find that the opinion dynamics has striking consequences on the statistical properties of disease spreading. The most important is that the smooth (continuous) transition from a healthy to an endemic phase observed in the contact process, as the infection probability increases beyond a threshold, becomes abrupt (discontinuous) in the two-layer system. Therefore, disregarding the effects of social dynamics on epidemics propagation may lead to a misestimation of the real magnitude of the spreading. Also, an endemic-healthy discontinuous transition is found when the coupling q overcomes a threshold value. Furthermore, we show that the disease dynamics delays the opinion consensus, leading to a consensus time that varies nonmonotonically with q in a large range of the model's parameters. A mean-field approach reveals that the coupled dynamics of opinions and disease can be approximately described by the dynamics of the voter model decoupled from that of the contact process, with effective probabilities of opinion and disease transmission.

Modeling and analyzing malware propagation in social networks with heterogeneous infection rates

NASA Astrophysics Data System (ADS)

Jia, Peng; Liu, Jiayong; Fang, Yong; Liu, Liang; Liu, Luping

2018-10-01

With the rapid development of social networks, hackers begin to try to spread malware more widely by utilizing various kinds of social networks. Thus, studying malware epidemic dynamics in these networks is becoming a popular subject in the literature. Most of the previous works focus on the effects of factors, such as network topology and user behavior, on malware propagation. Some researchers try to analyze the heterogeneity of infection rates, but the common problem of their works is the factors they mentioned that could affect the heterogeneity are not comprehensive enough. In this paper, focusing on the effects of heterogeneous infection rates, we propose a novel model called HSID (heterogeneous-susceptible-infectious-dormant model) to characterize virus propagation in social networks, in which a connection factor is presented to evaluate the heterogeneous relationships between nodes, and a resistance factor is introduced to represent node's mutable resistant ability. We analyzed how key parameters in the two factors affect the heterogeneity and then performed simulations to explore the effects in three real-world social networks. The results indicate: heterogeneous relationship could lead to wider diffusion in directed network, and heterogeneous security awareness could lead to wider diffusion in both directed and undirected networks; heterogeneous relationship could restrain the outbreak of malware but heterogeneous initial security awareness would increase the probability; furthermore, the increasing resistibility along with infected times would lead to malware's disappearance in social networks.

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.

Assortative model for social networks

NASA Astrophysics Data System (ADS)

Catanzaro, Michele; Caldarelli, Guido; Pietronero, Luciano

2004-09-01

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

Predicting the Lifetime of Dynamic Networks Experiencing Persistent Random Attacks.

PubMed

Podobnik, Boris; Lipic, Tomislav; Horvatic, Davor; Majdandzic, Antonio; Bishop, Steven R; Eugene Stanley, H

2015-09-21

Estimating the critical points at which complex systems abruptly flip from one state to another is one of the remaining challenges in network science. Due to lack of knowledge about the underlying stochastic processes controlling critical transitions, it is widely considered difficult to determine the location of critical points for real-world networks, and it is even more difficult to predict the time at which these potentially catastrophic failures occur. We analyse a class of decaying dynamic networks experiencing persistent failures in which the magnitude of the overall failure is quantified by the probability that a potentially permanent internal failure will occur. When the fraction of active neighbours is reduced to a critical threshold, cascading failures can trigger a total network failure. For this class of network we find that the time to network failure, which is equivalent to network lifetime, is inversely dependent upon the magnitude of the failure and logarithmically dependent on the threshold. We analyse how permanent failures affect network robustness using network lifetime as a measure. These findings provide new methodological insight into system dynamics and, in particular, of the dynamic processes of networks. We illustrate the network model by selected examples from biology, and social science.

Network structure, topology, and dynamics in generalized models of synchronization

NASA Astrophysics Data System (ADS)

Lerman, Kristina; Ghosh, Rumi

2012-08-01

Network structure is a product of both its topology and interactions between its nodes. We explore this claim using the paradigm of distributed synchronization in a network of coupled oscillators. As the network evolves to a global steady state, nodes synchronize in stages, revealing the network's underlying community structure. Traditional models of synchronization assume that interactions between nodes are mediated by a conservative process similar to diffusion. However, social and biological processes are often nonconservative. We propose a model of synchronization in a network of oscillators coupled via nonconservative processes. We study the dynamics of synchronization of a synthetic and real-world networks and show that the traditional and nonconservative models of synchronization reveal different structures within the same network.

Associative nature of event participation dynamics: A network theory approach

PubMed Central

Smiljanić, Jelena; Mitrović Dankulov, Marija

2017-01-01

The affiliation with various social groups can be a critical factor when it comes to quality of life of each individual, making such groups an essential element of every society. The group dynamics, longevity and effectiveness strongly depend on group’s ability to attract new members and keep them engaged in group activities. It was shown that high heterogeneity of scientist’s engagement in conference activities of the specific scientific community depends on the balance between the numbers of previous attendances and non-attendances and is directly related to scientist’s association with that community. Here we show that the same holds for leisure groups of the Meetup website and further quantify individual members’ association with the group. We examine how structure of personal social networks is evolving with the event attendance. Our results show that member’s increasing engagement in the group activities is primarily associated with the strengthening of already existing ties and increase in the bonding social capital. We also show that Meetup social networks mostly grow trough big events, while small events contribute to the groups cohesiveness. PMID:28166305

The Dynamics of Initiative in Communication Networks.

PubMed

Mollgaard, Anders; Mathiesen, Joachim

2016-01-01

Human social interaction is often intermittent. Two acquainted persons can have extended periods without social interaction punctuated by periods of repeated interaction. In this case, the repeated interaction can be characterized by a seed initiative by either of the persons and a number of follow-up interactions. The tendency to initiate social interaction plays an important role in the formation of social networks and is in general not symmetric between persons. In this paper, we study the dynamics of initiative by analysing and modeling a detailed call and text message network sampled from a group of 700 individuals. We show that in an average relationship between two individuals, one part is almost twice as likely to initiate communication compared to the other part. The asymmetry has social consequences and ultimately might lead to the discontinuation of a relationship. We explain the observed asymmetry by a positive feedback mechanism where individuals already taking initiative are more likely to take initiative in the future. In general, people with many initiatives receive attention from a broader spectrum of friends than people with few initiatives. Lastly, we compare the likelihood of taking initiative with the basic personality traits of the five factor model.

Spatial-Temporal Dynamics of High-Resolution Animal Networks: What Can We Learn from Domestic Animals?

PubMed

Chen, Shi; Ilany, Amiyaal; White, Brad J; Sanderson, Michael W; Lanzas, Cristina

2015-01-01

Animal social network is the key to understand many ecological and epidemiological processes. We used real-time location system (RTLS) to accurately track cattle position, analyze their proximity networks, and tested the hypothesis of temporal stationarity and spatial homogeneity in these networks during different daily time periods and in different areas of the pen. The network structure was analyzed using global network characteristics (network density), subgroup clustering (modularity), triadic property (transitivity), and dyadic interactions (correlation coefficient from a quadratic assignment procedure) at hourly level. We demonstrated substantial spatial-temporal heterogeneity in these networks and potential link between indirect animal-environment contact and direct animal-animal contact. But such heterogeneity diminished if data were collected at lower spatial (aggregated at entire pen level) or temporal (aggregated at daily level) resolution. The network structure (described by the characteristics such as density, modularity, transitivity, etc.) also changed substantially at different time and locations. There were certain time (feeding) and location (hay) that the proximity network structures were more consistent based on the dyadic interaction analysis. These results reveal new insights for animal network structure and spatial-temporal dynamics, provide more accurate descriptions of animal social networks, and allow more accurate modeling of multiple (both direct and indirect) disease transmission pathways.

Tracking the Evolution of Infrastructure Systems and Mass Responses Using Publically Available Data

PubMed Central

Guan, Xiangyang; Chen, Cynthia; Work, Dan

2016-01-01

Networks can evolve even on a short-term basis. This phenomenon is well understood by network scientists, but receive little attention in empirical literature involving real-world networks. On one hand, this is due to the deceitfully fixed topology of some networks such as many physical infrastructures, whose evolution is often deemed unlikely to occur in short term; on the other hand, the lack of data prohibits scientists from studying subjects such as social networks that seem likely to evolve on a short-term basis. We show that both networks—the infrastructure network and social network—are able to demonstrate evolutionary dynamics at the system level even in the short-term, characterized by shifting between different phases as predicted in network science. We develop a methodology of tracking the evolutionary dynamics of the two networks by incorporating flows and the microstructure of networks such as motifs. This approach is applied to the human interaction network and two transportation networks (subway and taxi) in the context of Hurricane Sandy, using publically available Twitter data and transportation data. Our result shows that significant changes in the system-level structure of networks can be detected on a continuous basis. This result provides a promising channel for real-time tracking in the future. PMID:27907061

Importance of tie strengths in the prisoner's dilemma game on social networks

NASA Astrophysics Data System (ADS)

Xu, Bo; Liu, Lu; You, Weijia

2011-06-01

Though numerous researches have shown that tie strengths play a key role in the formation of collective behavior in social networks, little work has been done to explore their impact on the outcome of evolutionary games. In this Letter, we studied the effect of tie strength in the dynamics of evolutionary prisoner's dilemma games by using online social network datasets. The results show that the fraction of cooperators has a non-trivial dependence on tie strength. Weak ties, just like previous researches on epidemics and information diffusion have shown, play a key role by the maintenance of cooperators in evolutionary prisoner's dilemma games.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lesperance, Ann M.; Olson, Jarrod; Godinez, Melanie A.

On March 10, 2010 the workshop titled Social Networking for Emergency Management and Public Safety was held in Seattle, WA. The objective of this workshop was to showcase ways social media networking technologies can be used to support emergency management and public safety operations. The workshop highlighted the current state of social networking and where this dynamic engagement is heading, demonstrated some of the more commonly used technologies, highlighted case studies on how these tools have been used in a variety of jurisdictions and engaged the private sector on how these tools might serve as a conduit for two waymore » communication between with the public sector to address regional recovery issues and decision making.« less

Statistical Mechanics of Temporal and Interacting Networks

NASA Astrophysics Data System (ADS)

Zhao, Kun

In the last ten years important breakthroughs in the understanding of the topology of complexity have been made in the framework of network science. Indeed it has been found that many networks belong to the universality classes called small-world networks or scale-free networks. Moreover it was found that the complex architecture of real world networks strongly affects the critical phenomena defined on these structures. Nevertheless the main focus of the research has been the characterization of single and static networks. Recently, temporal networks and interacting networks have attracted large interest. Indeed many networks are interacting or formed by a multilayer structure. Example of these networks are found in social networks where an individual might be at the same time part of different social networks, in economic and financial networks, in physiology or in infrastructure systems. Moreover, many networks are temporal, i.e. the links appear and disappear on the fast time scale. Examples of these networks are social networks of contacts such as face-to-face interactions or mobile-phone communication, the time-dependent correlations in the brain activity and etc. Understanding the evolution of temporal and multilayer networks and characterizing critical phenomena in these systems is crucial if we want to describe, predict and control the dynamics of complex system. In this thesis, we investigate several statistical mechanics models of temporal and interacting networks, to shed light on the dynamics of this new generation of complex networks. First, we investigate a model of temporal social networks aimed at characterizing human social interactions such as face-to-face interactions and phone-call communication. Indeed thanks to the availability of data on these interactions, we are now in the position to compare the proposed model to the real data finding good agreement. Second, we investigate the entropy of temporal networks and growing networks , to provide a new framework to quantify the information encoded in these networks and to answer a fundamental problem in network science: how complex are temporal and growing networks. Finally, we consider two examples of critical phenomena in interacting networks. In particular, on one side we investigate the percolation of interacting networks by introducing antagonistic interactions. On the other side, we investigate a model of political election based on the percolation of antagonistic networks. The aim of this research is to show how antagonistic interactions change the physics of critical phenomena on interacting networks. We believe that the work presented in these thesis offers the possibility to appreciate the large variability of problems that can be addressed in the new framework of temporal and interacting networks.

Macroscopic description of complex adaptive networks coevolving with dynamic node states

NASA Astrophysics Data System (ADS)

Wiedermann, Marc; Donges, Jonathan F.; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Macroscopic description of complex adaptive networks coevolving with dynamic node states.

PubMed

Wiedermann, Marc; Donges, Jonathan F; Heitzig, Jobst; Lucht, Wolfgang; Kurths, Jürgen

2015-05-01

In many real-world complex systems, the time evolution of the network's structure and the dynamic state of its nodes are closely entangled. Here we study opinion formation and imitation on an adaptive complex network which is dependent on the individual dynamic state of each node and vice versa to model the coevolution of renewable resources with the dynamics of harvesting agents on a social network. The adaptive voter model is coupled to a set of identical logistic growth models and we mainly find that, in such systems, the rate of interactions between nodes as well as the adaptive rewiring probability are crucial parameters for controlling the sustainability of the system's equilibrium state. We derive a macroscopic description of the system in terms of ordinary differential equations which provides a general framework to model and quantify the influence of single node dynamics on the macroscopic state of the network. The thus obtained framework is applicable to many fields of study, such as epidemic spreading, opinion formation, or socioecological modeling.

Infering and Calibrating Triadic Closure in a Dynamic Network

NASA Astrophysics Data System (ADS)

Mantzaris, Alexander V.; Higham, Desmond J.

In the social sciences, the hypothesis of triadic closure contends that new links in a social contact network arise preferentially between those who currently share neighbours. Here, in a proof-of-principle study, we show how to calibrate a recently proposed evolving network model to time-dependent connectivity data. The probabilistic edge birth rate in the model contains a triadic closure term, so we are also able to assess statistically the evidence for this effect. The approach is shown to work on data generated synthetically from the model. We then apply this methodology to some real, large-scale data that records the build up of connections in a business-related social networking site, and find evidence for triadic closure.

Animal Social Network Theory Can Help Wildlife Conservation.

PubMed

Snijders, Lysanne; Blumstein, Daniel T; Stanley, Christina R; Franks, Daniel W

2017-08-01

Many animals preferentially associate with certain other individuals. This social structuring can influence how populations respond to changes to their environment, thus making network analysis a promising technique for understanding, predicting, and potentially manipulating population dynamics. Various network statistics can correlate with individual fitness components and key population-level processes, yet the logical role and formal application of animal social network theory for conservation and management have not been well articulated. We outline how understanding of direct and indirect relationships between animals can be profitably applied by wildlife managers and conservationists. By doing so, we aim to stimulate the development and implementation of practical tools for wildlife conservation and management and to inspire novel behavioral research in this field. Copyright © 2017 Elsevier Ltd. All rights reserved.

Opinion dynamics within a virtual small group: the stubbornness effect

NASA Astrophysics Data System (ADS)

Guazzini, Andrea; Cini, Alessandro; Bagnoli, Franco; Ramasco, José

2015-09-01

The modeling of opinion dynamics is social systems has attracted a good deal of attention in the last decade. Even though based on intuition and observation, the mechanisms behind many of these models need solid empirical grounding. In this work, we investigate the relation among subjective variables (such as the personality), the dynamics of the affinity network dynamics, the communication patterns emerging throughout the social interactions and the opinions dynamics in a series of experiments with five small groups of ten people each. In order to ignite the discussion, the polemic topic of animal experimentation was proposed. The groups essentially polarized in two factions with a set of stubborn individuals (those not changing their opinions in time) playing the role of anchors. Our results suggest that the different layers present in the group dynamics (i.e., individual level, group dynamics and meso-communication) are deeply intermingled, specifically the stubbornness effect appears to be related to the dynamical features of the network topologies, and only in an undirected way to the personality of the participants.

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

Network dynamics of social influence in the wisdom of crowds

PubMed Central

Brackbill, Devon; Centola, Damon

2017-01-01

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton’s discovery of the “wisdom of crowds” [Galton F (1907) Nature 75:450–451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals’ estimates became more similar when subjects observed each other’s beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020–9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error. PMID:28607070

Network dynamics of social influence in the wisdom of crowds.

PubMed

Becker, Joshua; Brackbill, Devon; Centola, Damon

2017-06-27

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies ]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.

Social network and dominance hierarchy analyses at Chimpanzee Sanctuary Northwest

PubMed Central

Mayhew, Jessica A.; Mulcahy, John B.

2018-01-01

Different aspects of sociality bear considerable weight on the individual- and group-level welfare of captive nonhuman primates. Social Network Analysis (SNA) is a useful tool for gaining a holistic understanding of the dynamic social relationships of captive primate groups. Gaining a greater understanding of captive chimpanzees through investigations of centrality, preferred and avoided relationships, dominance hierarchy, and social network diagrams can be useful in advising current management practices in sanctuaries and other captive settings. In this study, we investigated the dyadic social relationships, group-level social networks, and dominance hierarchy of seven chimpanzees (Pan troglodytes) at Chimpanzee Sanctuary Northwest. We used focal-animal and instantaneous scan sampling to collect 106.75 total hours of associative, affiliative, and agonistic data from June to September 2016. We analyzed our data using SOCPROG to derive dominance hierarchies and network statistics, and we diagrammed the group’s social networks in NetDraw. Three individuals were most central in the grooming network, while two others had little connection. Through agonistic networks, we found that group members reciprocally exhibited agonism, and the group’s dominance hierarchy was statistically non-linear. One chimpanzee emerged as the most dominant through agonism but was least connected to other group members across affiliative networks. Our results indicate that the conventional methods used to calculate individuals’ dominance rank may be inadequate to wholly depict a group’s social relationships in captive sanctuary populations. Our results have an applied component that can aid sanctuary staff in a variety of ways to best ensure the improvement of group welfare. PMID:29444112

Social network and dominance hierarchy analyses at Chimpanzee Sanctuary Northwest.

PubMed

Funkhouser, Jake A; Mayhew, Jessica A; Mulcahy, John B

2018-01-01

Different aspects of sociality bear considerable weight on the individual- and group-level welfare of captive nonhuman primates. Social Network Analysis (SNA) is a useful tool for gaining a holistic understanding of the dynamic social relationships of captive primate groups. Gaining a greater understanding of captive chimpanzees through investigations of centrality, preferred and avoided relationships, dominance hierarchy, and social network diagrams can be useful in advising current management practices in sanctuaries and other captive settings. In this study, we investigated the dyadic social relationships, group-level social networks, and dominance hierarchy of seven chimpanzees (Pan troglodytes) at Chimpanzee Sanctuary Northwest. We used focal-animal and instantaneous scan sampling to collect 106.75 total hours of associative, affiliative, and agonistic data from June to September 2016. We analyzed our data using SOCPROG to derive dominance hierarchies and network statistics, and we diagrammed the group's social networks in NetDraw. Three individuals were most central in the grooming network, while two others had little connection. Through agonistic networks, we found that group members reciprocally exhibited agonism, and the group's dominance hierarchy was statistically non-linear. One chimpanzee emerged as the most dominant through agonism but was least connected to other group members across affiliative networks. Our results indicate that the conventional methods used to calculate individuals' dominance rank may be inadequate to wholly depict a group's social relationships in captive sanctuary populations. Our results have an applied component that can aid sanctuary staff in a variety of ways to best ensure the improvement of group welfare.

Research on Social Networking Sites and Social Support from 2004 to 2015: A Narrative Review and Directions for Future Research.

PubMed

Meng, Jingbo; Martinez, Lourdes; Holmstrom, Amanda; Chung, Minwoong; Cox, Jeff

2017-01-01

The article presents a narrative review of scholarship on social support through social networking sites (SNSs) published from 2004 to 2015. By searching keywords related to social support and SNSs in major databases for social sciences, we identified and content analyzed directly relevant articles (N = 88). The article summarizes the prevalence of theory usage; the function of theory usage (e.g., testing a theory, developing a theory); major theories referenced; and methodologies, including research designs, measurement, and the roles of social support and SNS examined in this literature. It also reports four themes identified across the studies, indicating the trends in the current research. Based on the review, the article presents a discussion about study sites, conceptualization of social support, theoretical coherence, the role of social networks, and the dynamic relationships between SNS use and social support, which points out potential avenues for shaping a future research agenda.

Infectious disease transmission and contact networks in wildlife and livestock.

PubMed

Craft, Meggan E

2015-05-26

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Infectious disease transmission and contact networks in wildlife and livestock

PubMed Central

Craft, Meggan E.

2015-01-01

The use of social and contact networks to answer basic and applied questions about infectious disease transmission in wildlife and livestock is receiving increased attention. Through social network analysis, we understand that wild animal and livestock populations, including farmed fish and poultry, often have a heterogeneous contact structure owing to social structure or trade networks. Network modelling is a flexible tool used to capture the heterogeneous contacts of a population in order to test hypotheses about the mechanisms of disease transmission, simulate and predict disease spread, and test disease control strategies. This review highlights how to use animal contact data, including social networks, for network modelling, and emphasizes that researchers should have a pathogen of interest in mind before collecting or using contact data. This paper describes the rising popularity of network approaches for understanding transmission dynamics in wild animal and livestock populations; discusses the common mismatch between contact networks as measured in animal behaviour and relevant parasites to match those networks; and highlights knowledge gaps in how to collect and analyse contact data. Opportunities for the future include increased attention to experiments, pathogen genetic markers and novel computational tools. PMID:25870393

Emergence of consensus as a modular-to-nested transition in communication dynamics

NASA Astrophysics Data System (ADS)

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics.

PubMed

Borge-Holthoefer, Javier; Baños, Raquel A; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-30

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources -visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems.

Emergence of consensus as a modular-to-nested transition in communication dynamics

PubMed Central

Borge-Holthoefer, Javier; Baños, Raquel A.; Gracia-Lázaro, Carlos; Moreno, Yamir

2017-01-01

Online social networks have transformed the way in which humans communicate and interact, leading to a new information ecosystem where people send and receive information through multiple channels, including traditional communication media. Despite many attempts to characterize the structure and dynamics of these techno-social systems, little is known about fundamental aspects such as how collective attention arises and what determines the information life-cycle. Current approaches to these problems either focus on human temporal dynamics or on semiotic dynamics. In addition, as recently shown, information ecosystems are highly competitive, with humans and memes striving for scarce resources –visibility and attention, respectively. Inspired by similar problems in ecology, here we develop a methodology that allows to cast all the previous aspects into a compact framework and to characterize, using microblogging data, information-driven systems as mutualistic networks. Our results show that collective attention around a topic is reached when the user-meme network self-adapts from a modular to a nested structure, which ultimately allows minimizing competition and attaining consensus. Beyond a sociological interpretation, we explore such resemblance to natural mutualistic communities via well-known dynamics of ecological systems. PMID:28134358

Analyzing traffic layout using dynamic social network analysis.

DOT National Transportation Integrated Search

2014-07-12

it is essential to build, maintain, and use our transportation systems in a manner that meets our current : needs while addressing the social and economic needs of future generations. In todays world, : transportation congestion causes serious neg...

Social network models predict movement and connectivity in ecological landscapes

USGS Publications Warehouse

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

2011-01-01

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

Small-World Network Spectra in Mean-Field Theory

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Timme, Marc

2012-05-01

Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science.

Toward a generalized theory of epidemic awareness in social networks

NASA Astrophysics Data System (ADS)

Wu, Qingchu; Zhu, Wenfang

We discuss the dynamics of a susceptible-infected-susceptible (SIS) model with local awareness in networks. Individual awareness to the infectious disease is characterized by a general function of epidemic information in its neighborhood. We build a high-accuracy approximate equation governing the spreading dynamics and derive an approximate epidemic threshold above which the epidemic spreads over the whole network. Our results extend the previous work and show that the epidemic threshold is dependent on the awareness function in terms of one infectious neighbor. Interestingly, when a pow-law awareness function is chosen, the epidemic threshold can emerge in infinite networks.

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

Evolution of individual versus social learning on social networks.

PubMed

Tamura, Kohei; Kobayashi, Yutaka; Ihara, Yasuo

2015-03-06

A number of studies have investigated the roles played by individual and social learning in cultural phenomena and the relative advantages of the two learning strategies in variable environments. Because social learning involves the acquisition of behaviours from others, its utility depends on the availability of 'cultural models' exhibiting adaptive behaviours. This indicates that social networks play an essential role in the evolution of learning. However, possible effects of social structure on the evolution of learning have not been fully explored. Here, we develop a mathematical model to explore the evolutionary dynamics of learning strategies on social networks. We first derive the condition under which social learners (SLs) are selectively favoured over individual learners in a broad range of social network. We then obtain an analytical approximation of the long-term average frequency of SLs in homogeneous networks, from which we specify the condition, in terms of three relatedness measures, for social structure to facilitate the long-term evolution of social learning. Finally, we evaluate our approximation by Monte Carlo simulations in complete graphs, regular random graphs and scale-free networks. We formally show that whether social structure favours the evolution of social learning is determined by the relative magnitudes of two effects of social structure: localization in competition, by which competition between learning strategies is evaded, and localization in cultural transmission, which slows down the spread of adaptive traits. In addition, our estimates of the relatedness measures suggest that social structure disfavours the evolution of social learning when selection is weak. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Effect of node attributes on the temporal dynamics of network structure

NASA Astrophysics Data System (ADS)

Momeni, Naghmeh; Fotouhi, Babak

2017-03-01

Many natural and social networks evolve in time and their structures are dynamic. In most networks, nodes are heterogeneous, and their roles in the evolution of structure differ. This paper focuses on the role of individual attributes on the temporal dynamics of network structure. We focus on a basic model for growing networks that incorporates node attributes (which we call "quality"), and we focus on the problem of forecasting the structural properties of the network in arbitrary times for an arbitrary initial network. That is, we address the following question: If we are given a certain initial network with given arbitrary structure and known node attributes, then how does the structure change in time as new nodes with given distribution of attributes join the network? We solve the model analytically and obtain the quality-degree joint distribution and degree correlations. We characterize the role of individual attributes in the position of individual nodes in the hierarchy of connections. We confirm the theoretical findings with Monte Carlo simulations.

Online Networks and the Diffusion of Protests

NASA Astrophysics Data System (ADS)

Moreno, Yamir

2013-03-01

Undoubtedly, online social networks have an enormous impact on opinions and cultural trends. Also, these platforms have revealed as a fundamental organizing mechanism in country-wide social movements. Recent events in the Middle East and North Africa (the wave of protests in the Arab world), across Europe (in the form of anti-cuts demonstrations or riots) and United States (the OWS movement) have generated much discussion on how digital media is connected to the diffusion of protests. In this talk, we investigate the mechanisms driving the emergence, development and stabilization of unrest movements in Spain and the USA by analyzing data from Twitter. Messages related to the protests are analyzed at both static and dynamic levels. We show that the online trace of the protests provides a unique opportunity to tackle central issues like recruitment patterns, information cascades and their spatiotemporal dynamics. Our findings shed light on the connection between online networks and social movements, and offer an empirical test to elusive sociological questions about collective action.

Evidence of complex contagion of information in social media: An experiment using Twitter bots.

PubMed

Mønsted, Bjarke; Sapieżyński, Piotr; Ferrara, Emilio; Lehmann, Sune

2017-01-01

It has recently become possible to study the dynamics of information diffusion in techno-social systems at scale, due to the emergence of online platforms, such as Twitter, with millions of users. One question that systematically recurs is whether information spreads according to simple or complex dynamics: does each exposure to a piece of information have an independent probability of a user adopting it (simple contagion), or does this probability depend instead on the number of sources of exposure, increasing above some threshold (complex contagion)? Most studies to date are observational and, therefore, unable to disentangle the effects of confounding factors such as social reinforcement, homophily, limited attention, or network community structure. Here we describe a novel controlled experiment that we performed on Twitter using 'social bots' deployed to carry out coordinated attempts at spreading information. We propose two Bayesian statistical models describing simple and complex contagion dynamics, and test the competing hypotheses. We provide experimental evidence that the complex contagion model describes the observed information diffusion behavior more accurately than simple contagion. Future applications of our results include more effective defenses against malicious propaganda campaigns on social media, improved marketing and advertisement strategies, and design of effective network intervention techniques.

Dynamic Social Networks in High Performance Football Coaching

ERIC Educational Resources Information Center

Occhino, Joseph; Mallett, Cliff; Rynne, Steven

2013-01-01

Background: Sports coaching is largely a social activity where engagement with athletes and support staff can enhance the experiences for all involved. This paper examines how high performance football coaches develop knowledge through their interactions with others within a social learning theory framework. Purpose: The key purpose of this study…

Occupation, Class, and Social Networks in Urban China

ERIC Educational Resources Information Center

Bian, Yanjie; Breiger, Ronald; Davis, Deborah; Galaskiewicz, Joseph

2005-01-01

China's class structure is changing dramatically in the wake of post-1978 market-oriented economic reforms. The creation of a mixed "market-socialist" economy has eroded the institutional bases of a cadre-dominated social hierarchy and created conditions for a new pattern of social stratification. Although conditions remain dynamic,…

Epidemic dynamics on a risk-based evolving social network

NASA Astrophysics Data System (ADS)

Antwi, Shadrack; Shaw, Leah

2013-03-01

Social network models have been used to study how behavior affects the dynamics of an infection in a population. Motivated by HIV, we consider how a trade-off between benefits and risks of sexual connections determine network structure and disease prevalence. We define a stochastic network model with formation and breaking of links as changes in sexual contacts. Each node has an intrinsic benefit its neighbors derive from connecting to it. Nodes' infection status is not apparent to others, but nodes with more connections (higher degree) are assumed more likely to be infected. The probability to form and break links is determined by a payoff computed from the benefit and degree-dependent risk. The disease is represented by a SI (susceptible-infected) model. We study network and epidemic evolution via Monte Carlo simulation and analytically predict the behavior with a heterogeneous mean field approach. The dependence of network connectivity and infection threshold on parameters is determined, and steady state degree distribution and epidemic levels are obtained. We also study a situation where system-wide infection levels alter perception of risk and cause nodes to adjust their behavior. This is a case of an adaptive network, where node status feeds back to change network geometry.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

PubMed Central

Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

2017-01-01

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living. PMID:28373567

Unraveling the disease consequences and mechanisms of modular structure in animal social networks

USGS Publications Warehouse

Sah, Pratha; Leu, Stephan T.; Cross, Paul C.; Hudson, Peter J.; Bansal, Shweta

2017-01-01

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

Unraveling the disease consequences and mechanisms of modular structure in animal social networks.

PubMed

Sah, Pratha; Leu, Stephan T; Cross, Paul C; Hudson, Peter J; Bansal, Shweta

2017-04-18

Disease risk is a potential cost of group living. Although modular organization is thought to reduce this cost in animal societies, empirical evidence toward this hypothesis has been conflicting. We analyzed empirical social networks from 43 animal species to motivate our study of the epidemiological consequences of modular structure in animal societies. From these empirical studies, we identified the features of interaction patterns associated with network modularity and developed a theoretical network model to investigate when and how subdivisions in social networks influence disease dynamics. Contrary to prior work, we found that disease risk is largely unaffected by modular structure, although social networks beyond a modular threshold experience smaller disease burden and longer disease duration. Our results illustrate that the lowering of disease burden in highly modular social networks is driven by two mechanisms of modular organization: network fragmentation and subgroup cohesion. Highly fragmented social networks with cohesive subgroups are able to structurally trap infections within a few subgroups and also cause a structural delay to the spread of disease outbreaks. Finally, we show that network models incorporating modular structure are necessary only when prior knowledge suggests that interactions within the population are highly subdivided. Otherwise, null networks based on basic knowledge about group size and local contact heterogeneity may be sufficient when data-limited estimates of epidemic consequences are necessary. Overall, our work does not support the hypothesis that modular structure universally mitigates the disease impact of group living.

A Diffusive Strategic Dynamics for Social Systems

NASA Astrophysics Data System (ADS)

Agliari, Elena; Burioni, Raffaella; Contucci, Pierluigi

2010-05-01

We propose a model for the dynamics of a social system, which includes diffusive effects and a biased rule for spin-flips, reproducing the effect of strategic choices. This model is able to mimic some phenomena taking place during marketing or political campaigns. Using a cost function based on the Ising model defined on the typical quenched interaction environments for social systems (Erdös-Renyi graph, small-world and scale-free networks), we find, by numerical simulations, that a stable stationary state is reached, and we compare the final state to the one obtained with standard dynamics, by means of total magnetization and magnetic susceptibility. Our results show that the diffusive strategic dynamics features a critical interaction parameter strictly lower than the standard one. We discuss the relevance of our findings in social systems.

Fractional Dynamics of Network Growth Constrained by Aging Node Interactions

PubMed Central

Safdari, Hadiseh; Zare Kamali, Milad; Shirazi, Amirhossein; Khalighi, Moein; Jafari, Gholamreza; Ausloos, Marcel

2016-01-01

In many social complex systems, in which agents are linked by non-linear interactions, the history of events strongly influences the whole network dynamics. However, a class of “commonly accepted beliefs” seems rarely studied. In this paper, we examine how the growth process of a (social) network is influenced by past circumstances. In order to tackle this cause, we simply modify the well known preferential attachment mechanism by imposing a time dependent kernel function in the network evolution equation. This approach leads to a fractional order Barabási-Albert (BA) differential equation, generalizing the BA model. Our results show that, with passing time, an aging process is observed for the network dynamics. The aging process leads to a decay for the node degree values, thereby creating an opposing process to the preferential attachment mechanism. On one hand, based on the preferential attachment mechanism, nodes with a high degree are more likely to absorb links; but, on the other hand, a node’s age has a reduced chance for new connections. This competitive scenario allows an increased chance for younger members to become a hub. Simulations of such a network growth with aging constraint confirm the results found from solving the fractional BA equation. We also report, as an exemplary application, an investigation of the collaboration network between Hollywood movie actors. It is undubiously shown that a decay in the dynamics of their collaboration rate is found, even including a sex difference. Such findings suggest a widely universal application of the so generalized BA model. PMID:27171424

Social behaviour and gut microbiota in red-bellied lemurs (Eulemur rubriventer): In search of the role of immunity in the evolution of sociality.

PubMed

Raulo, Aura; Ruokolainen, Lasse; Lane, Avery; Amato, Katherine; Knight, Rob; Leigh, Steven; Stumpf, Rebecca; White, Bryan; Nelson, Karen E; Baden, Andrea L; Tecot, Stacey R

2018-03-01

Vertebrate gut microbiota form a key component of immunity and a dynamic link between an individual and the ecosystem. Microbiota might play a role in social systems as well, because microbes are transmitted during social contact and can affect host behaviour. Combining methods from behavioural and molecular research, we describe the relationship between social dynamics and gut microbiota of a group-living cooperative species of primate, the red-bellied lemur (Eulemur rubriventer). Specifically, we ask whether patterns of social contact (group membership, group size, position in social network, individual sociality) are associated with patterns of gut microbial composition (diversity and similarity) between individuals and across time. Red-bellied lemurs were found to have gut microbiota with slight temporal fluctuations and strong social group-specific composition. Contrary to expectations, individual sociality was negatively associated with gut microbial diversity. However, position within the social network predicted gut microbial composition. These results emphasize the role of the social environment in determining the microbiota of adult animals. Since social transmission of gut microbiota has the potential to enhance immunity, microbiota might have played an escalating role in the evolution of sociality. © 2017 The Authors. Journal of Animal Ecology © 2017 British Ecological Society.

Social Networks and Smoking: Exploring the Effects of Peer Influence and Smoker Popularity through Simulations

ERIC Educational Resources Information Center

Schaefer, David R.; adams, jimi; Haas, Steven A.

2013-01-01

Adolescent smoking and friendship networks are related in many ways that can amplify smoking prevalence. Understanding and developing interventions within such a complex system requires new analytic approaches. We draw on recent advances in dynamic network modeling to develop a technique that explores the implications of various intervention…

Persistence of social signatures in human communication.

PubMed

Saramäki, Jari; Leicht, E A; López, Eduardo; Roberts, Sam G B; Reed-Tsochas, Felix; Dunbar, Robin I M

2014-01-21

The social network maintained by a focal individual, or ego, is intrinsically dynamic and typically exhibits some turnover in membership over time as personal circumstances change. However, the consequences of such changes on the distribution of an ego's network ties are not well understood. Here we use a unique 18-mo dataset that combines mobile phone calls and survey data to track changes in the ego networks and communication patterns of students making the transition from school to university or work. Our analysis reveals that individuals display a distinctive and robust social signature, captured by how interactions are distributed across different alters. Notably, for a given ego, these social signatures tend to persist over time, despite considerable turnover in the identity of alters in the ego network. Thus, as new network members are added, some old network members either are replaced or receive fewer calls, preserving the overall distribution of calls across network members. This is likely to reflect the consequences of finite resources such as the time available for communication, the cognitive and emotional effort required to sustain close relationships, and the ability to make emotional investments.

Persistence of social signatures in human communication

PubMed Central

Saramäki, Jari; Leicht, E. A.; López, Eduardo; Roberts, Sam G. B.; Reed-Tsochas, Felix; Dunbar, Robin I. M.

2014-01-01

The social network maintained by a focal individual, or ego, is intrinsically dynamic and typically exhibits some turnover in membership over time as personal circumstances change. However, the consequences of such changes on the distribution of an ego’s network ties are not well understood. Here we use a unique 18-mo dataset that combines mobile phone calls and survey data to track changes in the ego networks and communication patterns of students making the transition from school to university or work. Our analysis reveals that individuals display a distinctive and robust social signature, captured by how interactions are distributed across different alters. Notably, for a given ego, these social signatures tend to persist over time, despite considerable turnover in the identity of alters in the ego network. Thus, as new network members are added, some old network members either are replaced or receive fewer calls, preserving the overall distribution of calls across network members. This is likely to reflect the consequences of finite resources such as the time available for communication, the cognitive and emotional effort required to sustain close relationships, and the ability to make emotional investments. PMID:24395777

What the Tech Is Going On? Social Media and Your Music Classroom

ERIC Educational Resources Information Center

Giebelhausen, Robin

2015-01-01

Social media is a dynamic tool capable of helping music teachers in various capacities. This article will explore two levels of involvement, including the personal learning network and the social classroom. When teachers use social media to its fullest potential in the music classroom, it allows for many new possibilities for the classroom,…

Movement Patterns, Social Dynamics, and the Evolution of Cooperation

PubMed Central

Smaldino, Paul E.; Schank, Jeffrey C.

2012-01-01

The structure of social interactions influences many aspects of social life, including the spread of information and behavior, and the evolution of social phenotypes. After dispersal, organisms move around throughout their lives, and the patterns of their movement influence their social encounters over the course of their lifespan. Though both space and mobility are known to influence social evolution, there is little analysis of the influence of specific movement patterns on evolutionary dynamics. We explored the effects of random movement strategies on the evolution of cooperation using an agent-based prisoner’s dilemma model with mobile agents. This is the first systematic analysis of a model in which cooperators and defectors can use different random movement strategies, which we chose to fall on a spectrum between highly exploratory and highly restricted in their search tendencies. Because limited dispersal and restrictions to local neighborhood size are known to influence the ability of cooperators to effectively assort, we also assessed the robustness of our findings with respect to dispersal and local capacity constraints. We show that differences in patterns of movement can dramatically influence the likelihood of cooperator success, and that the effects of different movement patterns are sensitive to environmental assumptions about offspring dispersal and local space constraints. Since local interactions implicitly generate dynamic social interaction networks, we also measured the average number of unique and total interactions over a lifetime and considered how these emergent network dynamics helped explain the results. This work extends what is known about mobility and the evolution of cooperation, and also has general implications for social models with randomly moving agents. PMID:22838026

Bipartite Graphs as Models of Population Structures in Evolutionary Multiplayer Games

PubMed Central

Peña, Jorge; Rochat, Yannick

2012-01-01

By combining evolutionary game theory and graph theory, “games on graphs” study the evolutionary dynamics of frequency-dependent selection in population structures modeled as geographical or social networks. Networks are usually represented by means of unipartite graphs, and social interactions by two-person games such as the famous prisoner’s dilemma. Unipartite graphs have also been used for modeling interactions going beyond pairwise interactions. In this paper, we argue that bipartite graphs are a better alternative to unipartite graphs for describing population structures in evolutionary multiplayer games. To illustrate this point, we make use of bipartite graphs to investigate, by means of computer simulations, the evolution of cooperation under the conventional and the distributed N-person prisoner’s dilemma. We show that several implicit assumptions arising from the standard approach based on unipartite graphs (such as the definition of replacement neighborhoods, the intertwining of individual and group diversity, and the large overlap of interaction neighborhoods) can have a large impact on the resulting evolutionary dynamics. Our work provides a clear example of the importance of construction procedures in games on graphs, of the suitability of bigraphs and hypergraphs for computational modeling, and of the importance of concepts from social network analysis such as centrality, centralization and bipartite clustering for the understanding of dynamical processes occurring on networked population structures. PMID:22970237

Alignment of dynamic networks.

PubMed

Vijayan, V; Critchlow, D; Milenkovic, T

2017-07-15

Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems' static network representations, as is currently done. For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. http://nd.edu/∼cone/DynaMAGNA++/ . tmilenko@nd.edu. Supplementary data are available at Bioinformatics online. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com

Alignment of dynamic networks

PubMed Central

Vijayan, V.; Critchlow, D.; Milenković, T.

2017-01-01

Abstract Motivation: Network alignment (NA) aims to find a node mapping that conserves similar regions between compared networks. NA is applicable to many fields, including computational biology, where NA can guide the transfer of biological knowledge from well- to poorly-studied species across aligned network regions. Existing NA methods can only align static networks. However, most complex real-world systems evolve over time and should thus be modeled as dynamic networks. We hypothesize that aligning dynamic network representations of evolving systems will produce superior alignments compared to aligning the systems’ static network representations, as is currently done. Results: For this purpose, we introduce the first ever dynamic NA method, DynaMAGNA ++. This proof-of-concept dynamic NA method is an extension of a state-of-the-art static NA method, MAGNA++. Even though both MAGNA++ and DynaMAGNA++ optimize edge as well as node conservation across the aligned networks, MAGNA++ conserves static edges and similarity between static node neighborhoods, while DynaMAGNA++ conserves dynamic edges (events) and similarity between evolving node neighborhoods. For this purpose, we introduce the first ever measure of dynamic edge conservation and rely on our recent measure of dynamic node conservation. Importantly, the two dynamic conservation measures can be optimized with any state-of-the-art NA method and not just MAGNA++. We confirm our hypothesis that dynamic NA is superior to static NA, on synthetic and real-world networks, in computational biology and social domains. DynaMAGNA++ is parallelized and has a user-friendly graphical interface. Availability and implementation: http://nd.edu/∼cone/DynaMAGNA++/. Contact: tmilenko@nd.edu Supplementary information: Supplementary data are available at Bioinformatics online. PMID:28881980

Dynamics of influence and social balance in spatially-embedded regular and random networks

NASA Astrophysics Data System (ADS)

Singh, P.; Sreenivasan, S.; Szymanski, B.; Korniss, G.

2015-03-01

Structural balance - the tendency of social relationship triads to prefer specific states of polarity - can be a fundamental driver of beliefs, behavior, and attitudes on social networks. Here we study how structural balance affects deradicalization in an otherwise polarized population of leftists and rightists constituting the nodes of a low-dimensional social network. Specifically, assuming an externally moderating influence that converts leftists or rightists to centrists with probability p, we study the critical value p =pc , below which the presence of metastable mixed population states exponentially delay the achievement of centrist consensus. Above the critical value, centrist consensus is the only fixed point. Complementing our previously shown results for complete graphs, we present results for the process on low-dimensional networks, and show that the low-dimensional embedding of the underlying network significantly affects the critical value of probability p. Intriguingly, on low-dimensional networks, the critical value pc can show non-monotonicity as the dimensionality of the network is varied. We conclude by analyzing the scaling behavior of temporal variation of unbalanced triad density in the network for different low-dimensional network topologies. Supported in part by ARL NS-CTA, ONR, and ARO.

Metric projection for dynamic multiplex networks.

PubMed

Jurman, Giuseppe

2016-08-01

Evolving multiplex networks are a powerful model for representing the dynamics along time of different phenomena, such as social networks, power grids, biological pathways. However, exploring the structure of the multiplex network time series is still an open problem. Here we propose a two-step strategy to tackle this problem based on the concept of distance (metric) between networks. Given a multiplex graph, first a network of networks is built for each time step, and then a real valued time series is obtained by the sequence of (simple) networks by evaluating the distance from the first element of the series. The effectiveness of this approach in detecting the occurring changes along the original time series is shown on a synthetic example first, and then on the Gulf dataset of political events.

Dynamic functional connectivity and individual differences in emotions during social stress.

PubMed

Tobia, Michael J; Hayashi, Koby; Ballard, Grey; Gotlib, Ian H; Waugh, Christian E

2017-12-01

Exposure to acute stress induces multiple emotional responses, each with their own unique temporal dynamics. Dynamic functional connectivity (dFC) measures the temporal variability of network synchrony and captures individual differences in network neurodynamics. This study investigated the relationship between dFC and individual differences in emotions induced by an acute psychosocial stressor. Sixteen healthy adult women underwent fMRI scanning during a social evaluative threat (SET) task, and retrospectively completed questionnaires that assessed individual differences in subjectively experienced positive and negative emotions about stress and stress relief during the task. Group dFC was decomposed with parallel factor analysis (PARAFAC) into 10 components, each with a temporal signature, spatial network of functionally connected regions, and vector of participant loadings that captures individual differences in dFC. Participant loadings of two networks were positively correlated with stress-related emotions, indicating the existence of networks for positive and negative emotions. The emotion-related networks involved the ventromedial prefrontal cortex, cingulate cortex, anterior insula, and amygdala, among other distributed brain regions, and time signatures for these emotion-related networks were uncorrelated. These findings demonstrate that individual differences in stress-induced positive and negative emotions are each uniquely associated with large-scale brain networks, and suggest that dFC is a mechanism that generates individual differences in the emotional components of the stress response. Hum Brain Mapp 38:6185-6205, 2017. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

An evolutionary game approach for determination of the structural conflicts in signed networks

PubMed Central

Tan, Shaolin; Lü, Jinhu

2016-01-01

Social or biochemical networks can often divide into two opposite alliances in response to structural conflicts between positive (friendly, activating) and negative (hostile, inhibiting) interactions. Yet, the underlying dynamics on how the opposite alliances are spontaneously formed to minimize the structural conflicts is still unclear. Here, we demonstrate that evolutionary game dynamics provides a felicitous possible tool to characterize the evolution and formation of alliances in signed networks. Indeed, an evolutionary game dynamics on signed networks is proposed such that each node can adaptively adjust its choice of alliances to maximize its own fitness, which yet leads to a minimization of the structural conflicts in the entire network. Numerical experiments show that the evolutionary game approach is universally efficient in quality and speed to find optimal solutions for all undirected or directed, unweighted or weighted signed networks. Moreover, the evolutionary game approach is inherently distributed. These characteristics thus suggest the evolutionary game dynamic approach as a feasible and effective tool for determining the structural conflicts in large-scale on-line signed networks. PMID:26915581

Brown spider monkeys (Ateles hybridus): a model for differentiating the role of social networks and physical contact on parasite transmission dynamics

PubMed Central

Rimbach, Rebecca; Bisanzio, Donal; Galvis, Nelson; Link, Andrés; Di Fiore, Anthony; Gillespie, Thomas R.

2015-01-01

Elevated risk of disease transmission is considered a major cost of sociality, although empirical evidence supporting this idea remains scant. Variation in spatial cohesion and the occurrence of social interactions may have profound implications for patterns of interindividual parasite transmission. We used a social network approach to shed light on the importance of different aspects of group-living (i.e. within-group associations versus physical contact) on patterns of parasitism in a neotropical primate, the brown spider monkey (Ateles hybridus), which exhibits a high degree of fission–fusion subgrouping. We used daily subgroup composition records to create a ‘proximity’ network, and built a separate ‘contact’ network using social interactions involving physical contact. In the proximity network, connectivity between individuals was homogeneous, whereas the contact network highlighted high between-individual variation in the extent to which animals had physical contact with others, which correlated with an individual's age and sex. The gastrointestinal parasite species richness of highly connected individuals was greater than that of less connected individuals in the contact network, but not in the proximity network. Our findings suggest that among brown spider monkeys, physical contact impacts the spread of several common parasites and supports the idea that pathogen transmission is one cost associated with social contact. PMID:25870396

Brown spider monkeys (Ateles hybridus): a model for differentiating the role of social networks and physical contact on parasite transmission dynamics.

PubMed

Rimbach, Rebecca; Bisanzio, Donal; Galvis, Nelson; Link, Andrés; Di Fiore, Anthony; Gillespie, Thomas R

2015-05-26

Elevated risk of disease transmission is considered a major cost of sociality, although empirical evidence supporting this idea remains scant. Variation in spatial cohesion and the occurrence of social interactions may have profound implications for patterns of interindividual parasite transmission. We used a social network approach to shed light on the importance of different aspects of group-living (i.e. within-group associations versus physical contact) on patterns of parasitism in a neotropical primate, the brown spider monkey (Ateles hybridus), which exhibits a high degree of fission-fusion subgrouping. We used daily subgroup composition records to create a 'proximity' network, and built a separate 'contact' network using social interactions involving physical contact. In the proximity network, connectivity between individuals was homogeneous, whereas the contact network highlighted high between-individual variation in the extent to which animals had physical contact with others, which correlated with an individual's age and sex. The gastrointestinal parasite species richness of highly connected individuals was greater than that of less connected individuals in the contact network, but not in the proximity network. Our findings suggest that among brown spider monkeys, physical contact impacts the spread of several common parasites and supports the idea that pathogen transmission is one cost associated with social contact. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market

NASA Astrophysics Data System (ADS)

Guo, Kun; Sun, Yi; Qian, Xin

2017-03-01

With the development of the social network, the interaction between investors in stock market became more fast and convenient. Thus, investor sentiment which can influence their investment decisions may be quickly spread and magnified through the network, and to a certain extent the stock market can be affected. This paper collected the user comments data from a popular professional social networking site of China stock market called Xueqiu, then the investor sentiment data can be obtained through semantic analysis. The dynamic analysis on relationship between investor sentiment and stock market is proposed based on Thermal Optimal Path (TOP) method. The results show that the sentiment data was not always leading over stock market price, and it can be used to predict the stock price only when the stock has high investor attention.

Collective dynamics of 'small-world' networks.

PubMed

Watts, D J; Strogatz, S H

1998-06-04

Networks of coupled dynamical systems have been used to model biological oscillators, Josephson junction arrays, excitable media, neural networks, spatial games, genetic control networks and many other self-organizing systems. Ordinarily, the connection topology is assumed to be either completely regular or completely random. But many biological, technological and social networks lie somewhere between these two extremes. Here we explore simple models of networks that can be tuned through this middle ground: regular networks 'rewired' to introduce increasing amounts of disorder. We find that these systems can be highly clustered, like regular lattices, yet have small characteristic path lengths, like random graphs. We call them 'small-world' networks, by analogy with the small-world phenomenon (popularly known as six degrees of separation. The neural network of the worm Caenorhabditis elegans, the power grid of the western United States, and the collaboration graph of film actors are shown to be small-world networks. Models of dynamical systems with small-world coupling display enhanced signal-propagation speed, computational power, and synchronizability. In particular, infectious diseases spread more easily in small-world networks than in regular lattices.

Individual, network, and neighborhood correlates of exchange sex among female non-injection drug users in Baltimore, MD (2005–2007)

PubMed Central

Rudolph, Abby E; Linton, Sabriya; Dyer, Typhanye Penniman; Latkin, Carl

2012-01-01

The “HIV risk environment” has been characterized as a dynamic interplay between structural and network factors. However, most HIV prevention research has not examined the independent and combined impact of network and structural factors. We aimed to identify individual, network, and neighborhood correlates of exchange sex (≥1 exchange sex partner, past 90 days) among female non-injection drug users (NIDUs). We used baseline data from 417 NIDUs enrolled in a randomized HIV prevention trial in Baltimore (2005–2007). Surveys ascertained demographic variables, drug/sex risk behaviors, neighborhood perceptions, and social/sexual network characteristics. Correlates of exchange sex were identified with descriptive statistics and log-binomial regression. Our findings suggest that sex and drug relationships among female NIDUs are interlinked and may be difficult to modify without altering social norms. Strengthening ties that provide social support but not drug support and reducing ties that provide both drug and social support may facilitate reductions in individual-level HIV-risk behaviors. PMID:22983502

Spreading gossip in social networks.

PubMed

Lind, Pedro G; da Silva, Luciano R; Andrade, José S; Herrmann, Hans J

2007-09-01

We study a simple model of information propagation in social networks, where two quantities are introduced: the spread factor, which measures the average maximal reachability of the neighbors of a given node that interchange information among each other, and the spreading time needed for the information to reach such a fraction of nodes. When the information refers to a particular node at which both quantities are measured, the model can be taken as a model for gossip propagation. In this context, we apply the model to real empirical networks of social acquaintances and compare the underlying spreading dynamics with different types of scale-free and small-world networks. We find that the number of friendship connections strongly influences the probability of being gossiped. Finally, we discuss how the spread factor is able to be applied to other situations.

Spreading gossip in social networks

NASA Astrophysics Data System (ADS)

Lind, Pedro G.; da Silva, Luciano R.; Andrade, José S., Jr.; Herrmann, Hans J.

2007-09-01

We study a simple model of information propagation in social networks, where two quantities are introduced: the spread factor, which measures the average maximal reachability of the neighbors of a given node that interchange information among each other, and the spreading time needed for the information to reach such a fraction of nodes. When the information refers to a particular node at which both quantities are measured, the model can be taken as a model for gossip propagation. In this context, we apply the model to real empirical networks of social acquaintances and compare the underlying spreading dynamics with different types of scale-free and small-world networks. We find that the number of friendship connections strongly influences the probability of being gossiped. Finally, we discuss how the spread factor is able to be applied to other situations.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow.

PubMed

Zubek, Julian; Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems.

Social adaptation in multi-agent model of linguistic categorization is affected by network information flow

PubMed Central

Denkiewicz, Michał; Barański, Juliusz; Wróblewski, Przemysław; Rączaszek-Leonardi, Joanna; Plewczynski, Dariusz

2017-01-01

This paper explores how information flow properties of a network affect the formation of categories shared between individuals, who are communicating through that network. Our work is based on the established multi-agent model of the emergence of linguistic categories grounded in external environment. We study how network information propagation efficiency and the direction of information flow affect categorization by performing simulations with idealized network topologies optimizing certain network centrality measures. We measure dynamic social adaptation when either network topology or environment is subject to change during the experiment, and the system has to adapt to new conditions. We find that both decentralized network topology efficient in information propagation and the presence of central authority (information flow from the center to peripheries) are beneficial for the formation of global agreement between agents. Systems with central authority cope well with network topology change, but are less robust in the case of environment change. These findings help to understand which network properties affect processes of social adaptation. They are important to inform the debate on the advantages and disadvantages of centralized systems. PMID:28809957

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

PubMed Central

2013-01-01

Methods for analysis of network dynamics have seen great progress in the past decade. This article shows how Dynamic Network Logistic Regression techniques (a special case of the Temporal Exponential Random Graph Models) can be used to implement decision theoretic models for network dynamics in a panel data context. We also provide practical heuristics for model building and assessment. We illustrate the power of these techniques by applying them to a dynamic blog network sampled during the 2004 US presidential election cycle. This is a particularly interesting case because it marks the debut of Internet-based media such as blogs and social networking web sites as institutionally recognized features of the American political landscape. Using a longitudinal sample of all Democratic National Convention/Republican National Convention–designated blog citation networks, we are able to test the influence of various strategic, institutional, and balance-theoretic mechanisms as well as exogenous factors such as seasonality and political events on the propensity of blogs to cite one another over time. Using a combination of deviance-based model selection criteria and simulation-based model adequacy tests, we identify the combination of processes that best characterizes the choice behavior of the contending blogs. PMID:24143060

Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami

PubMed Central

Lu, Xin; Brelsford, Christa

2014-01-01

To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the 2011 earthquake and tsunami in Japan. We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average. By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their current community. While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content. This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events. PMID:25346468

Network Structure and Community Evolution on Twitter: Human Behavior Change in Response to the 2011 Japanese Earthquake and Tsunami

NASA Astrophysics Data System (ADS)

Lu, Xin; Brelsford, Christa

2014-10-01

To investigate the dynamics of social networks and the formation and evolution of online communities in response to extreme events, we collected three datasets from Twitter shortly before and after the 2011 earthquake and tsunami in Japan. We find that while almost all users increased their online activity after the earthquake, Japanese speakers, who are assumed to be more directly affected by the event, expanded the network of people they interact with to a much higher degree than English speakers or the global average. By investigating the evolution of communities, we find that the behavior of joining or quitting a community is far from random: users tend to stay in their current status and are less likely to join new communities from solitary or shift to other communities from their current community. While non-Japanese speakers did not change their conversation topics significantly after the earthquake, nearly all Japanese users changed their conversations to earthquake-related content. This study builds a systematic framework for investigating human behaviors under extreme events with online social network data and our findings on the dynamics of networks and communities may provide useful insight for understanding how patterns of social interaction are influenced by extreme events.

Modeling Misbehavior in Cooperative Diversity: A Dynamic Game Approach

NASA Astrophysics Data System (ADS)

Dehnie, Sintayehu; Memon, Nasir

2009-12-01

Cooperative diversity protocols are designed with the assumption that terminals always help each other in a socially efficient manner. This assumption may not be valid in commercial wireless networks where terminals may misbehave for selfish or malicious intentions. The presence of misbehaving terminals creates a social-dilemma where terminals exhibit uncertainty about the cooperative behavior of other terminals in the network. Cooperation in social-dilemma is characterized by a suboptimal Nash equilibrium where wireless terminals opt out of cooperation. Hence, without establishing a mechanism to detect and mitigate effects of misbehavior, it is difficult to maintain a socially optimal cooperation. In this paper, we first examine effects of misbehavior assuming static game model and show that cooperation under existing cooperative protocols is characterized by a noncooperative Nash equilibrium. Using evolutionary game dynamics we show that a small number of mutants can successfully invade a population of cooperators, which indicates that misbehavior is an evolutionary stable strategy (ESS). Our main goal is to design a mechanism that would enable wireless terminals to select reliable partners in the presence of uncertainty. To this end, we formulate cooperative diversity as a dynamic game with incomplete information. We show that the proposed dynamic game formulation satisfied the conditions for the existence of perfect Bayesian equilibrium.

Effects of Heterogeneous Social Interactions on Flocking Dynamics

NASA Astrophysics Data System (ADS)

Miguel, M. Carmen; Parley, Jack T.; Pastor-Satorras, Romualdo

2018-02-01

Social relationships characterize the interactions that occur within social species and may have an important impact on collective animal motion. Here, we consider a variation of the standard Vicsek model for collective motion in which interactions are mediated by an empirically motivated scale-free topology that represents a heterogeneous pattern of social contacts. We observe that the degree of order of the model is strongly affected by network heterogeneity: more heterogeneous networks show a more resilient ordered state, while less heterogeneity leads to a more fragile ordered state that can be destroyed by sufficient external noise. Our results challenge the previously accepted equivalence between the static Vicsek model and the equilibrium X Y model on the network of connections, and point towards a possible equivalence with models exhibiting a different symmetry.

Dynamic Infinite Mixed-Membership Stochastic Blockmodel.

PubMed

Fan, Xuhui; Cao, Longbing; Xu, Richard Yi Da

2015-09-01

Directional and pairwise measurements are often used to model interactions in a social network setting. The mixed-membership stochastic blockmodel (MMSB) was a seminal work in this area, and its ability has been extended. However, models such as MMSB face particular challenges in modeling dynamic networks, for example, with the unknown number of communities. Accordingly, this paper proposes a dynamic infinite mixed-membership stochastic blockmodel, a generalized framework that extends the existing work to potentially infinite communities inside a network in dynamic settings (i.e., networks are observed over time). Additional model parameters are introduced to reflect the degree of persistence among one's memberships at consecutive time stamps. Under this framework, two specific models, namely mixture time variant and mixture time invariant models, are proposed to depict two different time correlation structures. Two effective posterior sampling strategies and their results are presented, respectively, using synthetic and real-world data.

Dynamic Social Networks and Physical Aggression: The Moderating Role of Gender and Social Status Among Peers

PubMed Central

Rulison, Kelly L.; Gest, Scott D.; Loken, Eric

2013-01-01

We examined three interrelated questions: (1) Who selects physically aggressive friends?; (2) Are physically aggressive adolescents influential?; and (3) Who is susceptible to influence from these friends? Using stochastic actor-based modeling, we tested our hypotheses using a sample of 480 adolescents (ages 11–13) who were followed across four assessments (fall and spring of 6th and 7th grade). After controlling for other factors that drive network and behavioral dynamics, we found that physically aggressive adolescents were attractive as friends, physically aggressive adolescents and girls were more likely to select physically aggressive friends, and peer-rejected adolescents were less likely to select physically aggressive friends. There was an overall peer influence effect, but gender and social status were not significant moderators of influence. PMID:24068860

The Potential for Altmetrics to Measure Other Types of Impact in Scientific Production: Academic and Social Impact Dynamics in Social Media and Networks

ERIC Educational Resources Information Center

Maricato, João de Melo; Vilan Filho, Jayme Leiro

2018-01-01

Introduction: Altmetrics is an area under construction, with a potential to study the impacts of academic products from social media data. It is believed that altmetrics can capture social and academic impacts, going beyond measures obtained using bibliometric and scientometric indicators. This research aimed to analyse aspects, characteristics…

Transmission of severe acute respiratory syndrome in dynamical small-world networks

NASA Astrophysics Data System (ADS)

Masuda, Naoki; Konno, Norio; Aihara, Kazuyuki

2004-03-01

The outbreak of severe acute respiratory syndrome (SARS) is still threatening the world because of a possible resurgence. In the current situation that effective medical treatments such as antiviral drugs are not discovered yet, dynamical features of the epidemics should be clarified for establishing strategies for tracing, quarantine, isolation, and regulating social behavior of the public at appropriate costs. Here we propose a network model for SARS epidemics and discuss why superspreaders emerged and why SARS spread especially in hospitals, which were key factors of the recent outbreak. We suggest that superspreaders are biologically contagious patients, and they may amplify the spreads by going to potentially contagious places such as hospitals. To avoid mass transmission in hospitals, it may be a good measure to treat suspected cases without hospitalizing them. Finally, we indicate that SARS probably propagates in small-world networks associated with human contacts and that the biological nature of individuals and social group properties are factors more important than the heterogeneous rates of social contacts among individuals. This is in marked contrast with epidemics of sexually transmitted diseases or computer viruses to which scale-free network models often apply.

The association between social network factors and mental health at different life stages.

PubMed

Levula, Andrew; Wilson, Andrew; Harré, Michael

2016-07-01

Psychosocial factors are important determinants of an individual's health. This study examines the association between health scores and social network factors on mental health across different life stages. Data were drawn from the Household Income and Labour Dynamics in Australia survey for adolescents (n = 1739), adults (n = 10,309) and seniors (n = 2287). Hierarchical regression modelling was applied to examine effects within and across age groups. All the variables were derived from the self-completion questionnaire. The social network factors were statistically significant predictors of mental health outcomes for all three life stages. For adolescents, the three social network factors were statistically significant with social isolation having the largest impact (β = -.284, p < .001), followed by social connection (β = .084, p < .001) and social trust having a similar effect (β = .073, p < .001). For adults social isolation had the highest impact (β = -.203, p < .001), followed by social connection (β = .110, p < .001) and social trust (β = .087, p < .001).The results for seniors were social isolation (β = -.188, p < .001), social connection (β = .147, p < .001) and social trust (β = .032, p < .05). After adding the social network factors, the models improved significantly with social isolation playing the most significant role across all life stages, whereas the other social network factors played a differentiated role depending upon the life stage. These findings have practical implications in the design of mental health interventions across different life stages.

Network Dynamics: Modeling And Generation Of Very Large Heterogeneous Social Networks

DTIC Science & Technology

2015-11-23

P11035 (2014). [19] P. L. Krapivsky and S. Redner, Phys. Rev. E. 71, 036118 (2005). [20] M. O. Jackson and B. W. Rogers, Amer. Econ . Rev. 97, 890...P06004 (2010). [24] M. E. J. Newman, Networks: An Introduction (Oxford Univ. Press, Oxford, 2010). [25] P. J. Flory, Principles of Polymer Chemistry

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.

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 to the spread of susceptible/infected/recovered (SIR) epidemics. To compute VCSIR, we propose a novel individual-based approach to model the spread of SIR epidemics in networks, which captures the infection size for a given effective infection rate. Thus, VCSIR quantitatively integrates the infection strength with the corresponding infection size. To optimize the VCSIR metric, a new mitigation strategy is proposed, based on a temporary reduction of contacts in social networks. The social contact network is modeled as a weighted graph that describes the frequency of contacts among the individuals. Thus, we consider the spread of an epidemic as a dynamical system, and the total number of infection cases as the state of the system, while the weight reduction in the social network is the controller variable leading to slow/reduce the spread of epidemics. Using optimal control theory, the obtained solution represents an optimal adaptive weighted network defined over a finite time interval. Moreover, given the high complexity of the optimization problem, we propose two heuristics to find the near optimal solutions by reducing the contacts among the individuals in a decentralized way. Finally, the cascading failures that can take place in power grids and have recently caused several blackouts are studied. We propose a new metric to assess the robustness of the power grid with respect to the cascading failures. The power grid topology is modeled as a network, which consists of nodes and links representing power substations and transmission lines, respectively. We also propose an optimal islanding strategy to protect the power grid when a cascading failure event takes place in the grid. The robustness metrics are numerically evaluated using real and synthetic networks to quantify their robustness with respect to disturbing dynamics. We show that the proposed metrics outperform the classical metrics in quantifying the robustness of networks and the efficiency of the mitigation strategies. In summary, our work advances the network science field in assessing the robustness of complex networks with respect to various disturbing dynamics.

Challenges to inferring causality from viral information dispersion in dynamic social networks

NASA Astrophysics Data System (ADS)

Ternovski, John

2014-06-01

Understanding the mechanism behind large-scale information dispersion through complex networks has important implications for a variety of industries ranging from cyber-security to public health. With the unprecedented availability of public data from online social networks (OSNs) and the low cost nature of most OSN outreach, randomized controlled experiments, the "gold standard" of causal inference methodologies, have been used with increasing regularity to study viral information dispersion. And while these studies have dramatically furthered our understanding of how information disseminates through social networks by isolating causal mechanisms, there are still major methodological concerns that need to be addressed in future research. This paper delineates why modern OSNs are markedly different from traditional sociological social networks and why these differences present unique challenges to experimentalists and data scientists. The dynamic nature of OSNs is particularly troublesome for researchers implementing experimental designs, so this paper identifies major sources of bias arising from network mutability and suggests strategies to circumvent and adjust for these biases. This paper also discusses the practical considerations of data quality and collection, which may adversely impact the efficiency of the estimator. The major experimental methodologies used in the current literature on virality are assessed at length, and their strengths and limits identified. Other, as-yetunsolved threats to the efficiency and unbiasedness of causal estimators--such as missing data--are also discussed. This paper integrates methodologies and learnings from a variety of fields under an experimental and data science framework in order to systematically consolidate and identify current methodological limitations of randomized controlled experiments conducted in OSNs.

Exploring relationship between human mobility and social ties: Physical distance is not dead

NASA Astrophysics Data System (ADS)

Jin, Bo; Liao, Binbing; Yuan, Ning; Wang, Wenjun

2015-06-01

Partly due to the difficulty of the access to a worldwide dataset that simultaneously captures the location history and social networks, our understanding of the relationship between human mobility and the social ties has been limited. However, this topic is essential for a deeper study from human dynamics and social networks aspects. In this paper, we examine the location history data and social networks data of 712 email users and 399 offline events users from a map-editing based social network website. Based on these data, we expand all our experiment both from individual aspect and community aspect. We find that the physical distance is still the most influential factor to social ties among the nine representative human mobility features extracted from our GPS trajectory dataset, although Internet revolution has made long-distance communication dramatically faster, easier and cheaper than ever before, and in turn, partly expand the physical scope of social networks. Furthermore, we find that to a certain extent, the proximity of South-North direction is more influential than East-West direction to social ties. To the our best of our knowledge, this difference between South-North and East-West is the first time to be raised and quantitatively supported by a large dataset. We believe our findings on the interplay of human mobility and social ties offer a new perspective to this field of study.

Dynamic Creation of Social Networks for Syndromic Surveillance Using Information Fusion

NASA Astrophysics Data System (ADS)

Holsopple, Jared; Yang, Shanchieh; Sudit, Moises; Stotz, Adam

To enhance the effectiveness of health care, many medical institutions have started transitioning to electronic health and medical records and sharing these records between institutions. The large amount of complex and diverse data makes it difficult to identify and track relationships and trends, such as disease outbreaks, from the data points. INFERD: Information Fusion Engine for Real-Time Decision-Making is an information fusion tool that dynamically correlates and tracks event progressions. This paper presents a methodology that utilizes the efficient and flexible structure of INFERD to create social networks representing progressions of disease outbreaks. Individual symptoms are treated as features allowing multiple hypothesis being tracked and analyzed for effective and comprehensive syndromic surveillance.

Network evolution by nonlinear preferential rewiring of edges

NASA Astrophysics Data System (ADS)

Xu, Xin-Jian; Hu, Xiao-Ming; Zhang, Li-Jie

2011-06-01

The mathematical framework for small-world networks proposed in a seminal paper by Watts and Strogatz sparked a widespread interest in modeling complex networks in the past decade. However, most of research contributing to static models is in contrast to real-world dynamic networks, such as social and biological networks, which are characterized by rearrangements of connections among agents. In this paper, we study dynamic networks evolved by nonlinear preferential rewiring of edges. The total numbers of vertices and edges of the network are conserved, but edges are continuously rewired according to the nonlinear preference. Assuming power-law kernels with exponents α and β, the network structures in stationary states display a distinct behavior, depending only on β. For β>1, the network is highly heterogeneous with the emergence of starlike structures. For β<1, the network is widely homogeneous with a typical connectivity. At β=1, the network is scale free with an exponential cutoff.

Sexual mixing patterns among social networks of HIV-positive and HIV-negative Beijing men who have sex with men: a multilevel comparison using roundtable network mapping.

PubMed

Ruan, Yuhua; Pan, Stephen W; Chamot, Eric; Qian, Han-Zhu; Li, Dongliang; Li, Qing-Chun; Liang, Hong-Yuan; Spittal, Patricia; Shao, Yiming; Kristensen, Sibylle

2011-08-01

Men who have sex with men (MSM) are of immediate concern in China's HIV epidemic. In 2008, approximately 2.5-6.5% of China's eight million MSM were HIV positive, while MSM represented 11% of all new HIV cases. Two factors that will in-part determine HIV-transmission dynamics among MSM, are sexual mixing patterns and the social networks which shape them. Sexual mixing patterns and social networks of Chinese MSM, however, remain poorly understood with little refined data available. One reason is that stigma discourages disclosure of names and identifiers to researchers. Using an alternative network-mapping approach, matched case-control design, and snowball sampling, this pilot study sought to compare characteristics of social networks of HIV-positive and HIV-negative Beijing MSM at the individual, dyad, and network levels. First, HIV-negative MSM controls were matched to HIV-positive MSM cases based on age, education, residency, and ethnicity. Then, each case or control and their MSM social network convened at a specific time and location with study investigators. Venues included health clinics, karaoke clubs, brothels, and community centers. Then, using arbitrarily assigned numbers in lieu of actual names, all participants simultaneously completed self-administered surveys regarding their sexual relationships with other participants of the same social network. These new findings indicate that cross-generational sex (anal or oral sex between men with ≥10 years age difference) was more prevalent among social networks of HIV-positive MSM, and was due to older age structure of the social network, rather than behavioral differences in sex-partner selection. Members of social networks of HIV-positive MSM were also less likely to have ever disclosed their MSM identity to non-MSM. Future studies should partner with MSM advocacy groups to explore behavioral and structural interventions as possible means of reducing the cross-generational sex and sexual identity-development issues elevating HIV risk for young Chinese MSM.

How events determine spreading patterns: information transmission via internal and external influences on social networks

NASA Astrophysics Data System (ADS)

Liu, Chuang; Zhan, Xiu-Xiu; Zhang, Zi-Ke; Sun, Gui-Quan; Hui, Pak Ming

2015-11-01

Recently, information transmission models motivated by the classical epidemic propagation, have been applied to a wide-range of social systems, generally assume that information mainly transmits among individuals via peer-to-peer interactions on social networks. In this paper, we consider one more approach for users to get information: the out-of-social-network influence. Empirical analyzes of eight typical events’ diffusion on a very large micro-blogging system, Sina Weibo, show that the external influence has significant impact on information spreading along with social activities. In addition, we propose a theoretical model to interpret the spreading process via both internal and external channels, considering three essential properties: (i) memory effect; (ii) role of spreaders; and (iii) non-redundancy of contacts. Experimental and mathematical results indicate that the information indeed spreads much quicker and broader with mutual effects of the internal and external influences. More importantly, the present model reveals that the event characteristic would highly determine the essential spreading patterns once the network structure is established. The results may shed some light on the in-depth understanding of the underlying dynamics of information transmission on real social networks.

Theoretical approaches of online social network interventions and implications for behavioral change: a systematic review.

PubMed

Arguel, Amaël; Perez-Concha, Oscar; Li, Simon Y W; Lau, Annie Y S

2018-02-01

The aim of this review was to identify general theoretical frameworks used in online social network interventions for behavioral change. To address this research question, a PRISMA-compliant systematic review was conducted. A systematic review (PROSPERO registration number CRD42014007555) was conducted using 3 electronic databases (PsycINFO, Pubmed, and Embase). Four reviewers screened 1788 abstracts. 15 studies were selected according to the eligibility criteria. Randomized controlled trials and controlled studies were assessed using Cochrane Collaboration's "risk-of-bias" tool, and narrative synthesis. Five eligible articles used the social cognitive theory as a framework to develop interventions targeting behavioral change. Other theoretical frameworks were related to the dynamics of social networks, intention models, and community engagement theories. Only one of the studies selected in the review mentioned a well-known theory from the field of health psychology. Conclusions were that guidelines are lacking in the design of online social network interventions for behavioral change. Existing theories and models from health psychology that are traditionally used for in situ behavioral change should be considered when designing online social network interventions in a health care setting. © 2016 John Wiley & Sons, Ltd.

Dynamics of Disagreement: Large-Scale Temporal Network Analysis Reveals Negative Interactions in Online Collaboration

NASA Astrophysics Data System (ADS)

Tsvetkova, Milena; García-Gavilanes, Ruth; Yasseri, Taha

2016-11-01

Disagreement and conflict are a fact of social life. However, negative interactions are rarely explicitly declared and recorded and this makes them hard for scientists to study. In an attempt to understand the structural and temporal features of negative interactions in the community, we use complex network methods to analyze patterns in the timing and configuration of reverts of article edits to Wikipedia. We investigate how often and how fast pairs of reverts occur compared to a null model in order to control for patterns that are natural to the content production or are due to the internal rules of Wikipedia. Our results suggest that Wikipedia editors systematically revert the same person, revert back their reverter, and come to defend a reverted editor. We further relate these interactions to the status of the involved editors. Even though the individual reverts might not necessarily be negative social interactions, our analysis points to the existence of certain patterns of negative social dynamics within the community of editors. Some of these patterns have not been previously explored and carry implications for the knowledge collection practice conducted on Wikipedia. Our method can be applied to other large-scale temporal collaboration networks to identify the existence of negative social interactions and other social processes.

The effects of reputational and social knowledge on cooperation

PubMed Central

Gallo, Edoardo; Yan, Chang

2015-01-01

The emergence and sustenance of cooperative behavior is fundamental for a society to thrive. Recent experimental studies have shown that cooperation increases in dynamic networks in which subjects can choose their partners. However, these studies did not vary reputational knowledge, or what subjects know about other’s past actions, which has long been recognized as an important factor in supporting cooperation. They also did not give subjects access to global social knowledge, or information on who is connected to whom in the group. As a result, it remained unknown how reputational and social knowledge foster cooperative behavior in dynamic networks both independently and by complementing each other. In an experimental setting, we show that global reputational knowledge is crucial to sustaining a high level of cooperation and welfare. Cooperation is associated with the emergence of dense and clustered networks with highly cooperative hubs. Global social knowledge has no effect on the aggregate level of cooperation. A community analysis shows that the addition of global social knowledge to global reputational knowledge affects the distribution of cooperative activity: cooperators form a separate community that achieves a higher cooperation level than the community of defectors. Members of the community of cooperators achieve a higher payoff from interactions within the community than members of the less cooperative community. PMID:25775544

Matrix product algorithm for stochastic dynamics on networks applied to nonequilibrium Glauber dynamics

NASA Astrophysics Data System (ADS)

Barthel, Thomas; De Bacco, Caterina; Franz, Silvio

2018-01-01

We introduce and apply an efficient method for the precise simulation of stochastic dynamical processes on locally treelike graphs. Networks with cycles are treated in the framework of the cavity method. Such models correspond, for example, to spin-glass systems, Boolean networks, neural networks, or other technological, biological, and social networks. Building upon ideas from quantum many-body theory, our approach is based on a matrix product approximation of the so-called edge messages—conditional probabilities of vertex variable trajectories. Computation costs and accuracy can be tuned by controlling the matrix dimensions of the matrix product edge messages (MPEM) in truncations. In contrast to Monte Carlo simulations, the algorithm has a better error scaling and works for both single instances as well as the thermodynamic limit. We employ it to examine prototypical nonequilibrium Glauber dynamics in the kinetic Ising model. Because of the absence of cancellation effects, observables with small expectation values can be evaluated accurately, allowing for the study of decay processes and temporal correlations.

The Structural Underpinnings of Policy Learning: A Classroom Policy Simulation

NASA Astrophysics Data System (ADS)

Bird, Stephen

This paper investigates the relationship between the centrality of individual actors in a social network structure and their policy learning performance. In a dynamic comparable to real-world policy networks, results from a classroom simulation demonstrate a strong relationship between centrality in social learning networks and grade performance. Previous research indicates that social network centrality should have a positive effect on learning in other contexts and this link is tested in a policy learning context. Second, the distinction between collaborative learning versus information diffusion processes in policy learning is examined. Third, frequency of interaction is analyzed to determine whether consistent, frequent tics have a greater impact on the learning process. Finally, the data arc analyzed to determine if the benefits of centrality have limitations or thresholds when benefits no longer accrue. These results demonstrate the importance of network structure, and support a collaborative conceptualization of the policy learning process.

Effects of Network Structure, Competition and Memory Time on Social Spreading Phenomena

NASA Astrophysics Data System (ADS)

Gleeson, James P.; O'Sullivan, Kevin P.; Baños, Raquel A.; Moreno, Yamir

2016-04-01

Online social media has greatly affected the way in which we communicate with each other. However, little is known about what fundamental mechanisms drive dynamical information flow in online social systems. Here, we introduce a generative model for online sharing behavior that is analytically tractable and that can reproduce several characteristics of empirical micro-blogging data on hashtag usage, such as (time-dependent) heavy-tailed distributions of meme popularity. The presented framework constitutes a null model for social spreading phenomena that, in contrast to purely empirical studies or simulation-based models, clearly distinguishes the roles of two distinct factors affecting meme popularity: the memory time of users and the connectivity structure of the social network.

Trust and compactness in social network groups.

PubMed

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

2015-02-01

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

Searching for superspreaders of information in real-world social media.

PubMed

Pei, Sen; Muchnik, Lev; Andrade, José S; Zheng, Zhiming; Makse, Hernán A

2014-07-03

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for "viral" information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

NASA Astrophysics Data System (ADS)

Pei, Sen; Muchnik, Lev; Andrade, José S., Jr.; Zheng, Zhiming; Makse, Hernán A.

2014-07-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for ``viral'' information dissemination in relevant applications.

Searching for superspreaders of information in real-world social media

PubMed Central

Pei, Sen; Muchnik, Lev; Andrade, Jr., José S.; Zheng, Zhiming; Makse, Hernán A.

2014-01-01

A number of predictors have been suggested to detect the most influential spreaders of information in online social media across various domains such as Twitter or Facebook. In particular, degree, PageRank, k-core and other centralities have been adopted to rank the spreading capability of users in information dissemination media. So far, validation of the proposed predictors has been done by simulating the spreading dynamics rather than following real information flow in social networks. Consequently, only model-dependent contradictory results have been achieved so far for the best predictor. Here, we address this issue directly. We search for influential spreaders by following the real spreading dynamics in a wide range of networks. We find that the widely-used degree and PageRank fail in ranking users' influence. We find that the best spreaders are consistently located in the k-core across dissimilar social platforms such as Twitter, Facebook, Livejournal and scientific publishing in the American Physical Society. Furthermore, when the complete global network structure is unavailable, we find that the sum of the nearest neighbors' degree is a reliable local proxy for user's influence. Our analysis provides practical instructions for optimal design of strategies for “viral” information dissemination in relevant applications. PMID:24989148

Social network diagnostics: a tool for monitoring group interventions

PubMed Central

2013-01-01

Background Many behavioral interventions designed to improve health outcomes are delivered in group settings. To date, however, group interventions have not been evaluated to determine if the groups generate interaction among members and how changes in group interaction may affect program outcomes at the individual or group level. Methods This article presents a model and practical tool for monitoring how social ties and social structure are changing within the group during program implementation. The approach is based on social network analysis and has two phases: collecting network measurements at strategic intervention points to determine if group dynamics are evolving in ways anticipated by the intervention, and providing the results back to the group leader to guide implementation next steps. This process aims to initially increase network connectivity and ultimately accelerate the diffusion of desirable behaviors through the new network. This article presents the Social Network Diagnostic Tool and, as proof of concept, pilot data collected during the formative phase of a childhood obesity intervention. Results The number of reported advice partners and discussion partners increased during program implementation. Density, the number of ties among people in the network expressed as a percentage of all possible ties, increased from 0.082 to 0.182 (p < 0.05) in the advice network, and from 0.027 to 0.055 (p > 0.05) in the discussion network. Conclusions The observed two-fold increase in network density represents a significant shift in advice partners over the intervention period. Using the Social Network Tool to empirically guide program activities of an obesity intervention was feasible. PMID:24083343

ClueNet: Clustering a temporal network based on topological similarity rather than denseness.

PubMed

Crawford, Joseph; Milenković, Tijana

2018-01-01

Network clustering is a very popular topic in the network science field. Its goal is to divide (partition) the network into groups (clusters or communities) of "topologically related" nodes, where the resulting topology-based clusters are expected to "correlate" well with node label information, i.e., metadata, such as cellular functions of genes/proteins in biological networks, or age or gender of people in social networks. Even for static data, the problem of network clustering is complex. For dynamic data, the problem is even more complex, due to an additional dimension of the data-their temporal (evolving) nature. Since the problem is computationally intractable, heuristic approaches need to be sought. Existing approaches for dynamic network clustering (DNC) have drawbacks. First, they assume that nodes should be in the same cluster if they are densely interconnected within the network. We hypothesize that in some applications, it might be of interest to cluster nodes that are topologically similar to each other instead of or in addition to requiring the nodes to be densely interconnected. Second, they ignore temporal information in their early steps, and when they do consider this information later on, they do so implicitly. We hypothesize that capturing temporal information earlier in the clustering process and doing so explicitly will improve results. We test these two hypotheses via our new approach called ClueNet. We evaluate ClueNet against six existing DNC methods on both social networks capturing evolving interactions between individuals (such as interactions between students in a high school) and biological networks capturing interactions between biomolecules in the cell at different ages. We find that ClueNet is superior in over 83% of all evaluation tests. As more real-world dynamic data are becoming available, DNC and thus ClueNet will only continue to gain importance.

Modular and hierarchical structure of social contact networks

NASA Astrophysics Data System (ADS)

Ge, Yuanzheng; Song, Zhichao; Qiu, Xiaogang; Song, Hongbin; Wang, Yong

2013-10-01

Social contact networks exhibit overlapping qualities of communities, hierarchical structure and spatial-correlated nature. We propose a mixing pattern of modular and growing hierarchical structures to reconstruct social contact networks by using an individual’s geospatial distribution information in the real world. The hierarchical structure of social contact networks is defined based on the spatial distance between individuals, and edges among individuals are added in turn from the modular layer to the highest layer. It is a gradual process to construct the hierarchical structure: from the basic modular model up to the global network. The proposed model not only shows hierarchically increasing degree distribution and large clustering coefficients in communities, but also exhibits spatial clustering features of individual distributions. As an evaluation of the method, we reconstruct a hierarchical contact network based on the investigation data of a university. Transmission experiments of influenza H1N1 are carried out on the generated social contact networks, and results show that the constructed network is efficient to reproduce the dynamic process of an outbreak and evaluate interventions. The reproduced spread process exhibits that the spatial clustering of infection is accordant with the clustering of network topology. Moreover, the effect of individual topological character on the spread of influenza is analyzed, and the experiment results indicate that the spread is limited by individual daily contact patterns and local clustering topology rather than individual degree.

Can Multilayer Networks Advance Animal Behavior Research?

PubMed

Silk, Matthew J; Finn, Kelly R; Porter, Mason A; Pinter-Wollman, Noa

2018-06-01

Interactions among individual animals - and between these individuals and their environment - yield complex, multifaceted systems. The development of multilayer network analysis offers a promising new approach for studying animal social behavior and its relation to eco-evolutionary dynamics. Copyright © 2018 Elsevier Ltd. All rights reserved.

The diminishing role of hubs in dynamical processes on complex networks.

PubMed

Quax, Rick; Apolloni, Andrea; Sloot, Peter M A

2013-11-06

It is notoriously difficult to predict the behaviour of a complex self-organizing system, where the interactions among dynamical units form a heterogeneous topology. Even if the dynamics of each microscopic unit is known, a real understanding of their contributions to the macroscopic system behaviour is still lacking. Here, we develop information-theoretical methods to distinguish the contribution of each individual unit to the collective out-of-equilibrium dynamics. We show that for a system of units connected by a network of interaction potentials with an arbitrary degree distribution, highly connected units have less impact on the system dynamics when compared with intermediately connected units. In an equilibrium setting, the hubs are often found to dictate the long-term behaviour. However, we find both analytically and experimentally that the instantaneous states of these units have a short-lasting effect on the state trajectory of the entire system. We present qualitative evidence of this phenomenon from empirical findings about a social network of product recommendations, a protein-protein interaction network and a neural network, suggesting that it might indeed be a widespread property in nature.

Do Narcissism and Emotional Intelligence Win Us Friends? Modeling Dynamics of Peer Popularity Using Inferential Network Analysis.

PubMed

Czarna, Anna Z; Leifeld, Philip; Śmieja, Magdalena; Dufner, Michael; Salovey, Peter

2016-09-27

This research investigated effects of narcissism and emotional intelligence (EI) on popularity in social networks. In a longitudinal field study, we examined the dynamics of popularity in 15 peer groups in two waves (N = 273). We measured narcissism, ability EI, and explicit and implicit self-esteem. In addition, we measured popularity at zero acquaintance and 3 months later. We analyzed the data using inferential network analysis (temporal exponential random graph modeling, TERGM) accounting for self-organizing network forces. People high in narcissism were popular, but increased less in popularity over time than people lower in narcissism. In contrast, emotionally intelligent people increased more in popularity over time than less emotionally intelligent people. The effects held when we controlled for explicit and implicit self-esteem. These results suggest that narcissism is rather disadvantageous and that EI is rather advantageous for long-term popularity. © 2016 by the Society for Personality and Social Psychology, Inc.

Layer-switching cost and optimality in information spreading on multiplex networks

PubMed Central

Min, Byungjoon; Gwak, Sang-Hwan; Lee, Nanoom; Goh, K. -I.

2016-01-01

We study a model of information spreading on multiplex networks, in which agents interact through multiple interaction channels (layers), say online vs. offline communication layers, subject to layer-switching cost for transmissions across different interaction layers. The model is characterized by the layer-wise path-dependent transmissibility over a contact, that is dynamically determined dependently on both incoming and outgoing transmission layers. We formulate an analytical framework to deal with such path-dependent transmissibility and demonstrate the nontrivial interplay between the multiplexity and spreading dynamics, including optimality. It is shown that the epidemic threshold and prevalence respond to the layer-switching cost non-monotonically and that the optimal conditions can change in abrupt non-analytic ways, depending also on the densities of network layers and the type of seed infections. Our results elucidate the essential role of multiplexity that its explicit consideration should be crucial for realistic modeling and prediction of spreading phenomena on multiplex social networks in an era of ever-diversifying social interaction layers. PMID:26887527

Social networks and health: a systematic review of sociocentric network studies in low- and middle-income countries.

PubMed

Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

2015-01-01

In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex webs of dynamic social relationships. Harnessing such information may be especially important in contexts where resources are limited and people depend on their direct and indirect connections for support. Copyright © 2014 Elsevier Ltd. All rights reserved.

Social Networks and Health: A Systematic Review of Sociocentric Network Studies in Low- and Middle-Income Countries

PubMed Central

Perkins, Jessica M; Subramanian, S V; Christakis, Nicholas A

2015-01-01

In low- and middle-income countries (LMICs), naturally occurring social networks may be particularly vital to health outcomes as extended webs of social ties often are the principal source of various resources. Understanding how social network structure, and influential individuals within the network, may amplify the effects of interventions in LMICs, by creating, for example, cascade effects to non-targeted participants, presents an opportunity to improve the efficiency and effectiveness of public health interventions in such settings. We conducted a systematic review of PubMed, Econlit, Sociological Abstracts, and PsycINFO to identify a sample of 17 sociocentric network papers (arising from 10 studies) that specifically examined health issues in LMICs. We also separately selected to review 19 sociocentric network papers (arising from 10 other studies) on development topics related to wellbeing in LMICs. First, to provide a methodological resource, we discuss the sociocentric network study designs employed in the selected papers, and then provide a catalog of 105 name generator questions used to measure social ties across all the LMIC network papers (including both ego- and sociocentric network papers) cited in this review. Second, we show that network composition, individual network centrality, and network structure are associated with important health behaviors and health and development outcomes in different contexts across multiple levels of analysis and across distinct network types. Lastly, we highlight the opportunities for health researchers and practitioners in LMICs to 1) design effective studies and interventions in LMICs that account for the sociocentric network positions of certain individuals and overall network structure, 2) measure the spread of outcomes or intervention externalities, and 3) enhance the effectiveness and efficiency of aid based on knowledge of social structure. In summary, human health and wellbeing are connected through complex webs of dynamic social relationships. Harnessing such information may be especially important in contexts where resources are limited and people depend on their direct and indirect connections for support. PMID:25442969

Percolation and epidemics in random clustered networks

NASA Astrophysics Data System (ADS)

Miller, Joel C.

2009-08-01

The social networks that infectious diseases spread along are typically clustered. Because of the close relation between percolation and epidemic spread, the behavior of percolation in such networks gives insight into infectious disease dynamics. A number of authors have studied percolation or epidemics in clustered networks, but the networks often contain preferential contacts in high degree nodes. We introduce a class of random clustered networks and a class of random unclustered networks with the same preferential mixing. Percolation in the clustered networks reduces the component sizes and increases the epidemic threshold compared to the unclustered networks.

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

Three degrees of inclusion: the emergence of self-organizing social beliefs

NASA Astrophysics Data System (ADS)

Szvetelszky, Zsuzsanna; Szekfu˝, Balázs

2005-07-01

Using the scientific definition of gossip, an ancient and ubiquitous phenomenon of the social networks, we present our preliminary study and its results on how to measure the networks based on dissemination of connections and information. We try to accurately calculate the gossip-effects in networks with our hypothesis of "three degrees of inclusion". Our preliminary study on the subject of "three degrees of inclusion" gives latency to a very important property of social networks. Observing the human communication of closely knit social groups we came to the conclusion that the human networks are based on not more than three degrees of links. Taking the strong human ties into account our research indicates that whoever is on the fourth degree rarely counts as an in-group member. Our close friend's close friend's close friend — that is about the farthest — three steps — our network can reach out when it comes to telling a story or asking for a favor. Up to now no investigations have been performed to see whether the effects of gossip lead to the phase transition of the content of network's self-organizing communication. Our conclusion is that the gossip-effect must be considered as the prefactor of the news and opinions diffusion and dynamics at the social level.

Rumor spreading in online social networks by considering the bipolar social reinforcement

NASA Astrophysics Data System (ADS)

Ma, Jing; Li, Dandan; Tian, Zihao

2016-04-01

Considering the bipolar social reinforcement which includes positive and negative effects, in this paper we explore the rumor spreading dynamics in online social networks. By means of the generation function and cavity method developed from statistical physics of disordered system, the rumor spreading threshold can be theoretically drawn. Simulation results indicate that decreasing the positive reinforcement factor or increasing the negative reinforcement factor can suppress the rumor spreading effectively. By analyzing the topological properties of the real world social network, we find that the nodes with lower degree usually have smaller weight. However, the nodes with lower degree may have larger k-shell. In order to curb rumor spreading, some control strategies that are based on the nodes' degree, k-shell and weight are presented. By comparison, we show that controlling those nodes that have larger degree or weight are two effective strategies to prevent the rumor spreading.

Representing Trust in Cognitive Social Simulations

NASA Astrophysics Data System (ADS)

Pollock, Shawn S.; Alt, Jonathan K.; Darken, Christian J.

Trust plays a critical role in communications, strength of relationships, and information processing at the individual and group level. Cognitive social simulations show promise in providing an experimental platform for the examination of social phenomena such as trust formation. This paper describes the initial attempts at representation of trust in a cognitive social simulation using reinforcement learning algorithms centered around a cooperative Public Commodity game within a dynamic social network.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks.

PubMed

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks

NASA Astrophysics Data System (ADS)

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

Modeling rises and falls in money addicted social hierarchies

NASA Astrophysics Data System (ADS)

Dybiec, Bartłomiej; Mitarai, Namiko; Sneppen, Kim

2014-08-01

The emergence of large communities is inherently associated with the creation of social structures. Connections between individuals are indispensable for cooperative action of agents building social groups. Moreover, social groups usually evolve and their structure changes over time. Consequently, an underlying network connecting individuals is not static, reflecting an ongoing adaptation to new conditions. The evolution of social connections is influenced by the relative position (hierarchy) of individuals building the system as well as by the availability of resources. We explore this aspect of human ambition by modeling the interplay of social networking and an uneven distribution of external resources. The model naturally generates social hierarchies. Remarkably, this social structure exhibits a rise-and-fall behavior. A well pronounced quasi-periodic dynamics, which is closely associated with the dissipation of resources that are needed to sustain the social links, is revealed.

Theorising big IT programmes in healthcare: strong structuration theory meets actor-network theory.

PubMed

Greenhalgh, Trisha; Stones, Rob

2010-05-01

The UK National Health Service is grappling with various large and controversial IT programmes. We sought to develop a sharper theoretical perspective on the question "What happens - at macro-, meso- and micro-level - when government tries to modernise a health service with the help of big IT?" Using examples from data fragments at the micro-level of clinical work, we considered how structuration theory and actor-network theory (ANT) might be combined to inform empirical investigation. Giddens (1984) argued that social structures and human agency are recursively linked and co-evolve. ANT studies the relationships that link people and technologies in dynamic networks. It considers how discourses become inscribed in data structures and decision models of software, making certain network relations irreversible. Stones' (2005) strong structuration theory (SST) is a refinement of Giddens' work, systematically concerned with empirical research. It views human agents as linked in dynamic networks of position-practices. A quadripartite approcach considers [a] external social structures (conditions for action); [b] internal social structures (agents' capabilities and what they 'know' about the social world); [c] active agency and actions and [d] outcomes as they feed back on the position-practice network. In contrast to early structuration theory and ANT, SST insists on disciplined conceptual methodology and linking this with empirical evidence. In this paper, we adapt SST for the study of technology programmes, integrating elements from material interactionism and ANT. We argue, for example, that the position-practice network can be a socio-technical one in which technologies in conjunction with humans can be studied as 'actants'. Human agents, with their complex socio-cultural frames, are required to instantiate technology in social practices. Structurally relevant properties inscribed and embedded in technological artefacts constrain and enable human agency. The fortunes of healthcare IT programmes might be studied in terms of the interplay between these factors. Copyright 2010 Elsevier Ltd. All rights reserved.

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

PubMed

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

2010-07-01

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

Matching Navy Recruiting Needs with Social Network Profiles Using Lexical Link Analysis. N1 FY10 Research Project

DTIC Science & Technology

2010-01-01

recruiting needs and candidate profiles – Link the features in context of dynamic social network environments, learn from on-going market...universities, companies, etc.) • Friends list fandom (fan of) , • Endorsements (supporter of) • Navy Enlisted Rating descriptions – Hard Cards...the samples into a validation and a learning set Set aside . the validation set. Use the learning set to match the recruit ratings with the

Detection of communities with Naming Game-based methods

PubMed Central

Ribeiro, Carlos Henrique Costa

2017-01-01

Complex networks are often organized in groups or communities of agents that share the same features and/or functions, and this structural organization is built naturally with the formation of the system. In social networks, we argue that the dynamic of linguistic interactions of agreement among people can be a crucial factor in generating this community structure, given that sharing opinions with another person bounds them together, and disagreeing constantly would probably weaken the relationship. We present here a computational model of opinion exchange that uncovers the community structure of a network. Our aim is not to present a new community detection method proper, but to show how a model of social communication dynamics can reveal the (simple and overlapping) community structure in an emergent way. Our model is based on a standard Naming Game, but takes into consideration three social features: trust, uncertainty and opinion preference, that are built over time as agents communicate among themselves. We show that the separate addition of each social feature in the Naming Game results in gradual improvements with respect to community detection. In addition, the resulting uncertainty and trust values classify nodes and edges according to role and position in the network. Also, our model has shown a degree of accuracy both for non-overlapping and overlapping communities that are comparable with most algorithms specifically designed for topological community detection. PMID:28797097

Intergroup Variation of Social Relationships in Wild Vervet Monkeys: A Dynamic Network Approach.

PubMed

Borgeaud, Christèle; Sosa, Sebastian; Bshary, Redouan; Sueur, Cédric; van de Waal, Erica

2016-01-01

Social network analysis is a powerful tool that enables us to describe and quantify relationships between individuals. So far most of the studies rely on the analyses of various network snapshots, but do not capture changes over time. Here we use a stochastic actor-oriented model (SAOM) to test both the structure and the dynamics of relationships of three groups of wild vervet monkeys. We found that triadic closure (i.e., the friend of a friend is a friend) was significant in all three groups while degree popularity (i.e., the willingness to associate with individuals with high degree of connections) was significant in only two groups (AK, BD). The structure and dynamics of relationships according to the attributes of sex, matrilineand age differed significantly among groups. With respect to the structure, when analyzing the likelihood of bonds according to the different attributes, we found that individuals associate themselves preferably to individuals of the same sex only in two groups (AK, NH), while significant results for attachment to individuals of the same matriline were found also in two groups (BD, NH). With respect to the dynamics, i.e., how quickly relationships are modified, we found in two groups (AK, BD) that females' relationships were more prone to variation than males.' In the BD group, relationships within high-ranking matrilines were less stable than low-ranking ones while in the NH group, juveniles' relationships were also less stable than adults' ones. The intergroup variation indicates that establishing species-specific or even population specific characteristics of social networks for later between-species comparisons will be challenging. Although, such variation could also indicate some methodological issue, we are quite confident that data was collected similarly within the different groups. Our study therefore provides a potential new method to quantify social complexity according to natural demographic variation.

The Fragility of Individual-Based Explanations of Social Hierarchies: A Test Using Animal Pecking Orders

PubMed Central

2016-01-01

The standard approach in accounting for hierarchical differentiation in biology and the social sciences considers a hierarchy as a static distribution of individuals possessing differing amounts of some valued commodity, assumes that the hierarchy is generated by micro-level processes involving individuals, and attempts to reverse engineer the processes that produced the hierarchy. However, sufficient experimental and analytical results are available to evaluate this standard approach in the case of animal dominance hierarchies (pecking orders). Our evaluation using evidence from hierarchy formation in small groups of both hens and cichlid fish reveals significant deficiencies in the three tenets of the standard approach in accounting for the organization of dominance hierarchies. In consequence, we suggest that a new approach is needed to explain the organization of pecking orders and, very possibly, by implication, for other kinds of social hierarchies. We develop an example of such an approach that considers dominance hierarchies to be dynamic networks, uses dynamic sequences of interaction (dynamic network motifs) to explain the organization of dominance hierarchies, and derives these dynamic sequences directly from observation of hierarchy formation. We test this dynamical explanation using computer simulation and find a good fit with actual dynamics of hierarchy formation in small groups of hens. We hypothesize that the same dynamic sequences are used in small groups of many other animal species forming pecking orders, and we discuss the data required to evaluate our hypothesis. Finally, we briefly consider how our dynamic approach may be generalized to other kinds of social hierarchies using the example of the distribution of empty gastropod (snail) shells occupied in populations of hermit crabs. PMID:27410230

Predation Risk Shapes Social Networks in Fission-Fusion Populations

PubMed Central

Kelley, Jennifer L.; Morrell, Lesley J.; Inskip, Chloe; Krause, Jens; Croft, Darren P.

2011-01-01

Predation risk is often associated with group formation in prey, but recent advances in methods for analysing the social structure of animal societies make it possible to quantify the effects of risk on the complex dynamics of spatial and temporal organisation. In this paper we use social network analysis to investigate the impact of variation in predation risk on the social structure of guppy shoals and the frequency and duration of shoal splitting (fission) and merging (fusion) events. Our analyses revealed that variation in the level of predation risk was associated with divergent social dynamics, with fish in high-risk populations displaying a greater number of associations with overall greater strength and connectedness than those from low-risk sites. Temporal patterns of organisation also differed according to predation risk, with fission events more likely to occur over two short time periods (5 minutes and 20 minutes) in low-predation fish and over longer time scales (>1.5 hours) in high-predation fish. Our findings suggest that predation risk influences the fine-scale social structure of prey populations and that the temporal aspects of organisation play a key role in defining social systems. PMID:21912627

Social networks and spreading of epidemics

NASA Astrophysics Data System (ADS)

Trimper, Steffen; Zheng, Dafang; Brandau, Marian

2004-05-01

Epidemiological processes are studied within a recently proposed social network model using the susceptible-infected-refractory dynamics (SIR) of an epidemic. Within the network model, a population of individuals may be characterized by H independent hierarchies or dimensions, each of which consists of groupings of individuals into layers of subgroups. Detailed numerical simulations reveals that for H > 1, the global spreading results regardless of the degree of homophily α of the individuals forming a social circle. For H = 1, a transition from a global to a local spread occurs as the population becomes decomposed into increasingly homophilous groups. Multiple dimensions in classifying individuals (nodes) thus make a society (computer network) highly susceptible to large scale outbreaks of infectious diseases (viruses). The SIR-model can be extended by the inclusion of waiting times resulting in modified distribution function of the recovered.

Knowledge diffusion of dynamical network in terms of interaction frequency.

PubMed

Liu, Jian-Guo; Zhou, Qing; Guo, Qiang; Yang, Zhen-Hua; Xie, Fei; Han, Jing-Ti

2017-09-07

In this paper, we present a knowledge diffusion (SKD) model for dynamic networks by taking into account the interaction frequency which always used to measure the social closeness. A set of agents, which are initially interconnected to form a random network, either exchange knowledge with their neighbors or move toward a new location through an edge-rewiring procedure. The activity of knowledge exchange between agents is determined by a knowledge transfer rule that the target node would preferentially select one neighbor node to transfer knowledge with probability p according to their interaction frequency instead of the knowledge distance, otherwise, the target node would build a new link with its second-order neighbor preferentially or select one node in the system randomly with probability 1 - p. The simulation results show that, comparing with the Null model defined by the random selection mechanism and the traditional knowledge diffusion (TKD) model driven by knowledge distance, the knowledge would spread more fast based on SKD driven by interaction frequency. In particular, the network structure of SKD would evolve as an assortative one, which is a fundamental feature of social networks. This work would be helpful for deeply understanding the coevolution of the knowledge diffusion and network structure.

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

PubMed

Patterson, Megan S; Goodson, Patricia

2017-05-01

Compulsive exercise, a form of unhealthy exercise often associated with prioritizing exercise and feeling guilty when exercise is missed, is a common precursor to and symptom of eating disorders. College-aged women are at high risk of exercising compulsively compared with other groups. Social network analysis (SNA) is a theoretical perspective and methodology allowing researchers to observe the effects of relational dynamics on the behaviors of people. SNA was used to assess the relationship between compulsive exercise and body dissatisfaction, physical activity, and network variables. Descriptive statistics were conducted using SPSS, and quadratic assignment procedure (QAP) analyses were conducted using UCINET. QAP regression analysis revealed a statistically significant model (R 2 = .375, P < .0001) predicting compulsive exercise behavior. Physical activity, body dissatisfaction, and network variables were statistically significant predictor variables in the QAP regression model. In our sample, women who are connected to "important" or "powerful" people in their network are likely to have higher compulsive exercise scores. This result provides healthcare practitioners key target points for intervention within similar groups of women. For scholars researching eating disorders and associated behaviors, this study supports looking into group dynamics and network structure in conjunction with body dissatisfaction and exercise frequency.

Network neuroscience

PubMed Central

Bassett, Danielle S; Sporns, Olaf

2017-01-01

Despite substantial recent progress, our understanding of the principles and mechanisms underlying complex brain function and cognition remains incomplete. Network neuroscience proposes to tackle these enduring challenges. Approaching brain structure and function from an explicitly integrative perspective, network neuroscience pursues new ways to map, record, analyze and model the elements and interactions of neurobiological systems. Two parallel trends drive the approach: the availability of new empirical tools to create comprehensive maps and record dynamic patterns among molecules, neurons, brain areas and social systems; and the theoretical framework and computational tools of modern network science. The convergence of empirical and computational advances opens new frontiers of scientific inquiry, including network dynamics, manipulation and control of brain networks, and integration of network processes across spatiotemporal domains. We review emerging trends in network neuroscience and attempt to chart a path toward a better understanding of the brain as a multiscale networked system. PMID:28230844

Vertical Interaction in Open Software Engineering Communities

DTIC Science & Technology

2009-03-01

Program in CASOS (NSF,DGE-9972762), the Office of Naval Research under Dynamic Network Analysis program (N00014-02-1-0973, the Air Force Office of...W91WAW07C0063) for research in the area of dynamic network analysis. Additional support was provided by CASOS - the center for Computational Analysis of Social...methods across the domain. For a given project, de - velopers can choose from dozens of models, tools, platforms, and languages for specification, design

Integrating association data and disease dynamics: an illustration using African Buffalo in Kruger National Park

USGS Publications Warehouse

Cross, Paul C.; James O, Lloyd-Smith; Bowers, Justin A.; Hay, Craig T.; Hofmeyr, Markus; Getz, Wayne M.

2004-01-01

Recognition is a prerequisite for non-random association amongst individuals. We explore how non-random association patterns (i.e. who spends time with whom) affect disease dynamics. We estimated the amount of time individuals spent together per month using radio-tracking data from African buffalo and incorporated these data into a dynamic social network model. The dynamic nature of the network has a strong influence on simulated disease dynamics particularly for diseases with shorter infectious periods. Cluster analyses of the association data demonstrated that buffalo herds were not as well defined as previously thought. Associations were more tightly clustered in 2002 than 2003, perhaps due to drier conditions in 2003. As a result, diseases may spread faster during drought conditions due to increased population mixing. Association data are often collected but this is the first use of empirical data in a network disease model in a wildlife population.

True and fake information spreading over the Facebook

NASA Astrophysics Data System (ADS)

Yang, Dong; Chow, Tommy W. S.; Zhong, Lu; Tian, Zhaoyang; Zhang, Qingpeng; Chen, Guanrong

2018-09-01

Social networks have involved more and more users who search for and share information extensively and frequently. Tremendous evidence in Facebook, Twitter, Flickr and Google+ alike shows that such social networks are the major information sources as well as the most effective platforms for information transmission and exchange. The dynamic propagation of various information may gradually disseminate, drastically increase, strongly compete with each other, or slowly decrease. These observations had led to the present study of the spreading process of true and fake information over social networks, particularly the Facebook. Specifically, in this paper the topological structure of two huge-scale Facebook network datasets are investigated regarding their statistical properties. Based on that, an information model for simulating the true and fake information spreading over the Facebook is established. Through controlling the spreading parameters in extensive large-scale simulations, it is found that the final density of stiflers increases with the growth of the spreading rate, while it would decline with the increase of the removal rate. Moreover, it is found that the spreading process of the true-fake information is closely related to the node degrees on the network. Hub-individuals with high degrees have large probabilities to learn hidden information and then spread it. Interestingly, it is found that the spreading rate of the true information but not of the fake information has a great effect on the information spreading process, reflecting the human nature in believing and spreading truths in social activities. The new findings validate the proposed model to be capable of characterizing the dynamic evolution of true and fake information over the Facebook, useful and informative for future social science studies.

Global brain dynamics during social exclusion predict subsequent behavioral conformity

PubMed Central

Wasylyshyn, Nick; Hemenway Falk, Brett; Garcia, Javier O; Cascio, Christopher N; O’Donnell, Matthew Brook; Bingham, C Raymond; Simons-Morton, Bruce; Vettel, Jean M; Falk, Emily B

2018-01-01

Abstract Individuals react differently to social experiences; for example, people who are more sensitive to negative social experiences, such as being excluded, may be more likely to adapt their behavior to fit in with others. We examined whether functional brain connectivity during social exclusion in the fMRI scanner can be used to predict subsequent conformity to peer norms. Adolescent males (n = 57) completed a two-part study on teen driving risk: a social exclusion task (Cyberball) during an fMRI session and a subsequent driving simulator session in which they drove alone and in the presence of a peer who expressed risk-averse or risk-accepting driving norms. We computed the difference in functional connectivity between social exclusion and social inclusion from each node in the brain to nodes in two brain networks, one previously associated with mentalizing (medial prefrontal cortex, temporoparietal junction, precuneus, temporal poles) and another with social pain (dorsal anterior cingulate cortex, anterior insula). Using predictive modeling, this measure of global connectivity during exclusion predicted the extent of conformity to peer pressure during driving in the subsequent experimental session. These findings extend our understanding of how global neural dynamics guide social behavior, revealing functional network activity that captures individual differences. PMID:29529310

A Novel Experimental and Analytical Approach to the Multimodal Neural Decoding of Intent During Social Interaction in Freely-behaving Human Infants.

PubMed

Cruz-Garza, Jesus G; Hernandez, Zachery R; Tse, Teresa; Caducoy, Eunice; Abibullaev, Berdakh; Contreras-Vidal, Jose L

2015-10-04

Understanding typical and atypical development remains one of the fundamental questions in developmental human neuroscience. Traditionally, experimental paradigms and analysis tools have been limited to constrained laboratory tasks and contexts due to technical limitations imposed by the available set of measuring and analysis techniques and the age of the subjects. These limitations severely limit the study of developmental neural dynamics and associated neural networks engaged in cognition, perception and action in infants performing "in action and in context". This protocol presents a novel approach to study infants and young children as they freely organize their own behavior, and its consequences in a complex, partly unpredictable and highly dynamic environment. The proposed methodology integrates synchronized high-density active scalp electroencephalography (EEG), inertial measurement units (IMUs), video recording and behavioral analysis to capture brain activity and movement non-invasively in freely-behaving infants. This setup allows for the study of neural network dynamics in the developing brain, in action and context, as these networks are recruited during goal-oriented, exploration and social interaction tasks.

Users’ participation and social influence during information spreading on Twitter

PubMed Central

Zhang, Xin; Han, Ding-Ding; Yang, Ruiqi; Zhang, Ziqiao

2017-01-01

Online Social Networks generate a prodigious wealth of real-time information at an incessant rate. In this paper we study the empirical data that crawled from Twitter to describe the topology and information spreading dynamics of Online Social Networks. We propose a measurement with three measures to state the efforts of users on Twitter to get their information spreading, based on the unique mechanisms for information retransmission on Twitter. It is noticed that small fraction of users with special performance on participation can gain great influence, while most other users play a role as middleware during the information propagation. Thus a community analysis is performed and four categories of users are found with different kinds of participation that cause the information dissemination dynamics. These suggest that exiting topological measures alone may reflect little about the influence of individuals and provide new insights for information spreading. PMID:28902906

The dynamics of meaningful social interactions and the emergence of collective knowledge

PubMed Central

Dankulov, Marija Mitrović; Melnik, Roderick; Tadić, Bosiljka

2015-01-01

Collective knowledge as a social value may arise in cooperation among actors whose individual expertise is limited. The process of knowledge creation requires meaningful, logically coordinated interactions, which represents a challenging problem to physics and social dynamics modeling. By combining two-scale dynamics model with empirical data analysis from a well-known Questions & Answers system Mathematics, we show that this process occurs as a collective phenomenon in an enlarged network (of actors and their artifacts) where the cognitive recognition interactions are properly encoded. The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities. These measures elucidate the impact of each cognitive element and the individual actor’s expertise in the collective dynamics. The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games. PMID:26174482

The dynamics of meaningful social interactions and the emergence of collective knowledge

NASA Astrophysics Data System (ADS)

Dankulov, Marija Mitrović; Melnik, Roderick; Tadić, Bosiljka

2015-07-01

Collective knowledge as a social value may arise in cooperation among actors whose individual expertise is limited. The process of knowledge creation requires meaningful, logically coordinated interactions, which represents a challenging problem to physics and social dynamics modeling. By combining two-scale dynamics model with empirical data analysis from a well-known Questions & Answers system Mathematics, we show that this process occurs as a collective phenomenon in an enlarged network (of actors and their artifacts) where the cognitive recognition interactions are properly encoded. The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities. These measures elucidate the impact of each cognitive element and the individual actor’s expertise in the collective dynamics. The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games.

The dynamics of meaningful social interactions and the emergence of collective knowledge.

PubMed

Dankulov, Marija Mitrović; Melnik, Roderick; Tadić, Bosiljka

2015-07-15

Collective knowledge as a social value may arise in cooperation among actors whose individual expertise is limited. The process of knowledge creation requires meaningful, logically coordinated interactions, which represents a challenging problem to physics and social dynamics modeling. By combining two-scale dynamics model with empirical data analysis from a well-known Questions &Answers system Mathematics, we show that this process occurs as a collective phenomenon in an enlarged network (of actors and their artifacts) where the cognitive recognition interactions are properly encoded. The emergent behavior is quantified by the information divergence and innovation advancing of knowledge over time and the signatures of self-organization and knowledge sharing communities. These measures elucidate the impact of each cognitive element and the individual actor's expertise in the collective dynamics. The results are relevant to stochastic processes involving smart components and to collaborative social endeavors, for instance, crowdsourcing scientific knowledge production with online games.

Extraordinary variability and sharp transitions in a maximally frustrated dynamic network

NASA Astrophysics Data System (ADS)

Liu, Wenjia; Schmittmann, Beate; Zia, R. K. P.

2013-03-01

Most previous studies of complex networks have focused on single, static networks. However, in the real world, networks are dynamic and interconnected. Inspired by the presence of extroverts and introverts in the general population, we investigate a highly simplified model of a social network, involving two types of nodes: one preferring the highest degree possible, and one preferring no connections whatsoever. There are only two control parameters in the model: the number of ``introvert'' and ``extrovert'' nodes, NI and NE. Our key findings are as follows: As a function of NI and NE, the system exhibits a highly unusual transition, displaying extraordinary fluctuations (as in 2nd order transitions) and discontinuous jumps (characteristic of 1st order transitions). Most remarkably, the system can be described by an Ising-like Hamiltonian with long-range multi-spin interactions and some of its properties can be obtained analytically. This is in stark contrast with other dynamic network models which rely almost exclusively on simulations. NSF-DMR-1005417/1244666 and and ICTAS Virginia Tech

Social significance of community structure: Statistical view

NASA Astrophysics Data System (ADS)

Li, Hui-Jia; Daniels, Jasmine J.

2015-01-01

Community structure analysis is a powerful tool for social networks that can simplify their topological and functional analysis considerably. However, since community detection methods have random factors and real social networks obtained from complex systems always contain error edges, evaluating the significance of a partitioned community structure is an urgent and important question. In this paper, integrating the specific characteristics of real society, we present a framework to analyze the significance of a social community. The dynamics of social interactions are modeled by identifying social leaders and corresponding hierarchical structures. Instead of a direct comparison with the average outcome of a random model, we compute the similarity of a given node with the leader by the number of common neighbors. To determine the membership vector, an efficient community detection algorithm is proposed based on the position of the nodes and their corresponding leaders. Then, using a log-likelihood score, the tightness of the community can be derived. Based on the distribution of community tightness, we establish a connection between p -value theory and network analysis, and then we obtain a significance measure of statistical form . Finally, the framework is applied to both benchmark networks and real social networks. Experimental results show that our work can be used in many fields, such as determining the optimal number of communities, analyzing the social significance of a given community, comparing the performance among various algorithms, etc.

Functional cortical network in alpha band correlates with social bargaining.

PubMed

Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

2014-01-01

Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals' alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts.

Functional Cortical Network in Alpha Band Correlates with Social Bargaining

PubMed Central

Billeke, Pablo; Zamorano, Francisco; Chavez, Mario; Cosmelli, Diego; Aboitiz, Francisco

2014-01-01

Solving demanding tasks requires fast and flexible coordination among different brain areas. Everyday examples of this are the social dilemmas in which goals tend to clash, requiring one to weigh alternative courses of action in limited time. In spite of this fact, there are few studies that directly address the dynamics of flexible brain network integration during social interaction. To study the preceding, we carried out EEG recordings while subjects played a repeated version of the Ultimatum Game in both human (social) and computer (non-social) conditions. We found phase synchrony (inter-site-phase-clustering) modulation in alpha band that was specific to the human condition and independent of power modulation. The strength and patterns of the inter-site-phase-clustering of the cortical networks were also modulated, and these modulations were mainly in frontal and parietal regions. Moreover, changes in the individuals’ alpha network structure correlated with the risk of the offers made only in social conditions. This correlation was independent of changes in power and inter-site-phase-clustering strength. Our results indicate that, when subjects believe they are participating in a social interaction, a specific modulation of functional cortical networks in alpha band takes place, suggesting that phase synchrony of alpha oscillations could serve as a mechanism by which different brain areas flexibly interact in order to adapt ongoing behavior in socially demanding contexts. PMID:25286240

Adaptive contact networks change effective disease infectiousness and dynamics.

PubMed

Van Segbroeck, Sven; Santos, Francisco C; Pacheco, Jorge M

2010-08-19

Human societies are organized in complex webs that are constantly reshaped by a social dynamic which is influenced by the information individuals have about others. Similarly, epidemic spreading may be affected by local information that makes individuals aware of the health status of their social contacts, allowing them to avoid contact with those infected and to remain in touch with the healthy. Here we study disease dynamics in finite populations in which infection occurs along the links of a dynamical contact network whose reshaping may be biased based on each individual's health status. We adopt some of the most widely used epidemiological models, investigating the impact of the reshaping of the contact network on the disease dynamics. We derive analytical results in the limit where network reshaping occurs much faster than disease spreading and demonstrate numerically that this limit extends to a much wider range of time scales than one might anticipate. Specifically, we show that from a population-level description, disease propagation in a quickly adapting network can be formulated equivalently as disease spreading on a well-mixed population but with a rescaled infectiousness. We find that for all models studied here--SI, SIS and SIR--the effective infectiousness of a disease depends on the population size, the number of infected in the population, and the capacity of healthy individuals to sever contacts with the infected. Importantly, we indicate how the use of available information hinders disease progression, either by reducing the average time required to eradicate a disease (in case recovery is possible), or by increasing the average time needed for a disease to spread to the entire population (in case recovery or immunity is impossible).

Theoretical and Experimental Investigation of Opinion Dynamics in Small Social Networks

DTIC Science & Technology

2016-07-01

Sciences, Social Informatics and Telecommunications Engineering 2013 96 M. Gabbay described. Section 4 illustrates the application of the methodology...group of cyber terrorists has already gained access to multiple computers. The attack will attempt to disrupt and destroy a large oil refinery; at

Quantifying the propagation of distress and mental disorders in social networks.

PubMed

Scatà, Marialisa; Di Stefano, Alessandro; La Corte, Aurelio; Liò, Pietro

2018-03-22

Heterogeneity of human beings leads to think and react differently to social phenomena. Awareness and homophily drive people to weigh interactions in social multiplex networks, influencing a potential contagion effect. To quantify the impact of heterogeneity on spreading dynamics, we propose a model of coevolution of social contagion and awareness, through the introduction of statistical estimators, in a weighted multiplex network. Multiplexity of networked individuals may trigger propagation enough to produce effects among vulnerable subjects experiencing distress, mental disorder, which represent some of the strongest predictors of suicidal behaviours. The exposure to suicide is emotionally harmful, since talking about it may give support or inadvertently promote it. To disclose the complex effect of the overlapping awareness on suicidal ideation spreading among disordered people, we also introduce a data-driven approach by integrating different types of data. Our modelling approach unveils the relationship between distress and mental disorders propagation and suicidal ideation spreading, shedding light on the role of awareness in a social network for suicide prevention. The proposed model is able to quantify the impact of overlapping awareness on suicidal ideation spreading and our findings demonstrate that it plays a dual role on contagion, either reinforcing or delaying the contagion outbreak.

Formation of raiding parties for intergroup violence is mediated by social network structure

PubMed Central

Glowacki, Luke; Isakov, Alexander; Wrangham, Richard W.; McDermott, Rose; Fowler, James H.; Christakis, Nicholas A.

2016-01-01

Intergroup violence is common among humans worldwide. To assess how within-group social dynamics contribute to risky, between-group conflict, we conducted a 3-y longitudinal study of the formation of raiding parties among the Nyangatom, a group of East African nomadic pastoralists currently engaged in small-scale warfare. We also mapped the social network structure of potential male raiders. Here, we show that the initiation of raids depends on the presence of specific leaders who tend to participate in many raids, to have more friends, and to occupy more central positions in the network. However, despite the different structural position of raid leaders, raid participants are recruited from the whole population, not just from the direct friends of leaders. An individual’s decision to participate in a raid is strongly associated with the individual’s social network position in relation to other participants. Moreover, nonleaders have a larger total impact on raid participation than leaders, despite leaders’ greater connectivity. Thus, we find that leaders matter more for raid initiation than participant mobilization. Social networks may play a role in supporting risky collective action, amplify the emergence of raiding parties, and hence facilitate intergroup violence in small-scale societies. PMID:27790996

Three Degrees of Inclusion: the Gossip-Effect in Human Networks

NASA Astrophysics Data System (ADS)

Szekfu˝, Balázs; Szvetelszky, Zsuzsanna

2005-06-01

Using the scientific definition of gossip, an ancient and ubiquitous phenomenon of the social networks, we present our preliminary study and its results on how to measure the networks based on dissemination of connections and information. We try to accurately calculate the gossip-effects in networks with our hypothesis of "three degrees of inclusion". Our preliminary study on the subject of "three degrees of inclusion" gives latency to a very important property of social networks. Observing the human communication of closely knit social groups we came to the conclusion that the human networks are based on not more than three degrees of links. Taking the strong human ties into account our research indicates that whoever is on the fourth degree rarely counts as an in-group member. Our close friend's close friend's close friend — that is about the farthest — three steps — our network can reach out when it comes to telling a story or asking for a favor. Up to now no investigations have been performed to see whether the effects of gossip lead to the phase transition of the content of network's self-organizing communication. Our conclusion is that the gossip-effect must be considered as the prefactor of the news and opinions diffusion and dynamics at the social level.

The spreading of opposite opinions on online social networks with authoritative nodes

NASA Astrophysics Data System (ADS)

Yan, Shu; Tang, Shaoting; Pei, Sen; Jiang, Shijin; Zhang, Xiao; Ding, Wenrui; Zheng, Zhiming

2013-09-01

The study of opinion dynamics, such as spreading and controlling of rumors, has become an important issue on social networks. Numerous models have been devised to describe this process, including epidemic models and spin models, which mainly focus on how opinions spread and interact with each other, respectively. In this paper, we propose a model that combines the spreading stage and the interaction stage for opinions to illustrate the process of dispelling a rumor. Moreover, we set up authoritative nodes, which disseminate positive opinion to counterbalance the negative opinion prevailing on online social networking sites. With analysis of the relationship among positive opinion proportion, opinion strength and the density of authoritative nodes in networks with different topologies, we demonstrate that the positive opinion proportion grows with the density of authoritative nodes until the positive opinion prevails in the entire network. In particular, the relationship is linear in homogeneous topologies. Besides, it is also noteworthy that initial locations of the negative opinion source and authoritative nodes do not influence positive opinion proportion in homogeneous networks but have a significant impact on heterogeneous networks. The results are verified by numerical simulations and are helpful to understand the mechanism of two different opinions interacting with each other on online social networking sites.

Modeling of information diffusion in Twitter-like social networks under information overload.

PubMed

Li, Pei; Li, Wei; Wang, Hui; Zhang, Xin

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations.

Modeling of Information Diffusion in Twitter-Like Social Networks under Information Overload

PubMed Central

Li, Wei

2014-01-01

Due to the existence of information overload in social networks, it becomes increasingly difficult for users to find useful information according to their interests. This paper takes Twitter-like social networks into account and proposes models to characterize the process of information diffusion under information overload. Users are classified into different types according to their in-degrees and out-degrees, and user behaviors are generalized into two categories: generating and forwarding. View scope is introduced to model the user information-processing capability under information overload, and the average number of times a message appears in view scopes after it is generated by a given type user is adopted to characterize the information diffusion efficiency, which is calculated theoretically. To verify the accuracy of theoretical analysis results, we conduct simulations and provide the simulation results, which are consistent with the theoretical analysis results perfectly. These results are of importance to understand the diffusion dynamics in social networks, and this analysis framework can be extended to consider more realistic situations. PMID:24795541

Stochastic opinion formation in scale-free networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

M. Bartolozzi; D. B. Leinweber; A. W. Thomas

2005-10-01

The dynamics of opinion formation in large groups of people is a complex nonlinear phenomenon whose investigation is just beginning. Both collective behavior and personal views play an important role in this mechanism. In the present work we mimic the dynamics of opinion formation of a group of agents, represented by two states 1, as a stochastic response of each agent to the opinion of his/her neighbors in the social network and to feedback from the average opinion of the whole. In the light of recent studies, a scale-free Barabsi-Albert network has been selected to simulate the topology of themore » interactions. A turbulent-like dynamics, characterized by an intermittent behavior, is observed for a certain range of the model parameters. The problem of uncertainty in decision taking is also addressed both from a topological point of view, using random and targeted removal of agents from the network, and by implementing a three-state model, where the third state, zero, is related to the information available to each agent. Finally, the results of the model are tested against the best known network of social interactions: the stock market. A time series of daily closures of the Dow-Jones index has been used as an indicator of the possible applicability of our model in the financial context. Good qualitative agreement is found.« less

Formation of homophily in academic performance: Students change their friends rather than performance

PubMed Central

Smirnov, Ivan

2017-01-01

Homophily, the tendency of individuals to associate with others who share similar traits, has been identified as a major driving force in the formation and evolution of social ties. In many cases, it is not clear if homophily is the result of a socialization process, where individuals change their traits according to the dominance of that trait in their local social networks, or if it results from a selection process, in which individuals reshape their social networks so that their traits match those in the new environment. Here we demonstrate the detailed temporal formation of strong homophily in academic achievements of high school and university students. We analyze a unique dataset that contains information about the detailed time evolution of a friendship network of 6,000 students across 42 months. Combining the evolving social network data with the time series of the academic performance (GPA) of individual students, we show that academic homophily is a result of selection: students prefer to gradually reorganize their social networks according to their performance levels, rather than adapting their performance to the level of their local group. We find no signs for a pull effect, where a social environment of good performers motivates bad students to improve their performance. We are able to understand the underlying dynamics of grades and networks with a simple model. The lack of a social pull effect in classical educational settings could have important implications for the understanding of the observed persistence of segregation, inequality and social immobility in societies. PMID:28854202

When perceptions defy reality: The relationships between depression and actual and perceived Facebook social support.

PubMed

Park, Jiyoung; Lee, David Seungjae; Shablack, Holly; Verduyn, Philippe; Deldin, Patricia; Ybarra, Oscar; Jonides, John; Kross, Ethan

2016-08-01

Although the relationship between depression and "offline" social support is well established, numerous questions surround the relationship between "online" social support and depression. We explored this issue by examining the social support dynamics that characterize the way individuals with varying levels of depression (Study 1) and SCID-diagnosed clinically depressed and non-depressed individuals (Study 2) interact with Facebook, the world's largest online social network. Using a novel methodology, we examined how disclosing positive or negative information on Facebook influences the amount of social support depressed individuals (a) actually receive (based on actual social support transactions recorded on Facebook walls) and (b) think they receive (based on subjective assessments) from their Facebook network. Contrary to prior research indicating that depression correlates with less actual social support from "offline" networks, across both studies depression was positively correlated with social support from Facebook networks when participants disclosed negative information (p=.02 in Study 1 and p=.06 in Study 2). Yet, depression was negatively correlated with how much social support participants thought they received from their Facebook networks (p=.005 in Study 1 and p=.001 in Study 2). The sample size was relatively small in Study 2, reflecting difficulties of recruiting individuals with Major Depressive Disorder. These results demonstrate that an asymmetry characterizes the relationship between depression and different types of Facebook social support and further identify perceptions of Facebook social support as a potential intervention target. (243 words; 250 max). Copyright © 2016 Elsevier B.V. All rights reserved.

The dynamics of injection drug users' personal networks and HIV risk behaviors.

PubMed

Costenbader, Elizabeth C; Astone, Nan M; Latkin, Carl A

2006-07-01

While studies of the social networks of injection drug users (IDUs) have provided insight into how the structures of interpersonal relationships among IDUs affect HIV risk behaviors, the majority of these studies have been cross-sectional. The present study examined the dynamics of IDUs' social networks and HIV risk behaviors over time. Using data from a longitudinal HIV-intervention study conducted in Baltimore, MD, this study assessed changes in the composition of the personal networks of 409 IDUs. We used a multi-nomial logistic regression analysis to assess the association between changes in network composition and simultaneous changes in levels of injection HIV risk behaviors. Using the regression parameters generated by the multi-nomial model, we estimated the predicted probability of being in each of four HIV risk behavior change groups. Compared to the base case, individuals who reported an entirely new set of drug-using network contacts at follow-up were more than three times as likely to be in the increasing risk group. In contrast, reporting all new non-drug-using contacts at follow-up increased the likelihood of being in the stable low-risk group by almost 50% and decreased the probability of being in the consistently high-risk group by more than 70%. The findings from this study show that, over and above IDUs' baseline characteristics, changes in their personal networks are associated with changes in individuals' risky injection behaviors. They also suggest that interventions aimed at reducing HIV risk among IDUs might benefit from increasing IDUs' social contacts with individuals who are not drug users.

Pricing a Protest: Forecasting the Dynamics of Civil Unrest Activity in Social Media.

PubMed

Goode, Brian J; Krishnan, Siddharth; Roan, Michael; Ramakrishnan, Naren

2015-01-01

Online social media activity can often be a precursor to disruptive events such as protests, strikes, and "occupy" movements. We have observed that such civil unrest can galvanize supporters through social networks and help recruit activists to their cause. Understanding the dynamics of social network cascades and extrapolating their future growth will enable an analyst to detect or forecast major societal events. Existing work has primarily used structural and temporal properties of cascades to predict their future behavior. But factors like societal pressure, alignment of individual interests with broader causes, and perception of expected benefits also affect protest participation in social media. Here we develop an analysis framework using a differential game theoretic approach to characterize the cost of participating in a cascade, and demonstrate how we can combine such cost features with classical properties to forecast the future behavior of cascades. Using data from Twitter, we illustrate the effectiveness of our models on the "Brazilian Spring" and Venezuelan protests that occurred in June 2013 and November 2013, respectively. We demonstrate how our framework captures both qualitative and quantitative aspects of how these uprisings manifest through the lens of tweet volume on Twitter social media.

A social activity and physical contact-based routing algorithm in mobile opportunistic networks for emergency response to sudden disasters

NASA Astrophysics Data System (ADS)

Wang, Xiaoming; Lin, Yaguang; Zhang, Shanshan; Cai, Zhipeng

2017-05-01

Sudden disasters such as earthquake, flood and hurricane necessitate the employment of communication networks to carry out emergency response activities. Routing has a significant impact on the functionality, performance and flexibility of communication networks. In this article, the routing problem is studied considering the delivery ratio of messages, the overhead ratio of messages and the average delay of messages in mobile opportunistic networks (MONs) for enterprise-level emergency response communications in sudden disaster scenarios. Unlike the traditional routing methods for MONS, this article presents a new two-stage spreading and forwarding dynamic routing algorithm based on the proposed social activity degree and physical contact factor for mobile customers. A new modelling method for describing a dynamic evolving process of the topology structure of a MON is first proposed. Then a multi-copy spreading strategy based on the social activity degree of nodes and a single-copy forwarding strategy based on the physical contact factor between nodes are designed. Compared with the most relevant routing algorithms such as Epidemic, Prophet, Labelled-sim, Dlife-comm and Distribute-sim, the proposed routing algorithm can significantly increase the delivery ratio of messages, and decrease the overhead ratio and average delay of messages.

Using the relational event model (REM) to investigate the temporal dynamics of animal social networks.

PubMed

Tranmer, Mark; Marcum, Christopher Steven; Morton, F Blake; Croft, Darren P; de Kort, Selvino R

2015-03-01

Social dynamics are of fundamental importance in animal societies. Studies on nonhuman animal social systems often aggregate social interaction event data into a single network within a particular time frame. Analysis of the resulting network can provide a useful insight into the overall extent of interaction. However, through aggregation, information is lost about the order in which interactions occurred, and hence the sequences of actions over time. Many research hypotheses relate directly to the sequence of actions, such as the recency or rate of action, rather than to their overall volume or presence. Here, we demonstrate how the temporal structure of social interaction sequences can be quantified from disaggregated event data using the relational event model (REM). We first outline the REM, explaining why it is different from other models for longitudinal data, and how it can be used to model sequences of events unfolding in a network. We then discuss a case study on the European jackdaw, Corvus monedula , in which temporal patterns of persistence and reciprocity of action are of interest, and present and discuss the results of a REM analysis of these data. One of the strengths of a REM analysis is its ability to take into account different ways in which data are collected. Having explained how to take into account the way in which the data were collected for the jackdaw study, we briefly discuss the application of the model to other studies. We provide details of how the models may be fitted in the R statistical software environment and outline some recent extensions to the REM framework.

Social contagions on correlated multiplex networks

NASA Astrophysics Data System (ADS)

Wang, Wei; Cai, Meng; Zheng, Muhua

2018-06-01

The existence of interlayer degree correlations has been disclosed by abundant multiplex network analysis. However, how they impose on the dynamics of social contagions are remain largely unknown. In this paper, we propose a non-Markovian social contagion model in multiplex networks with inter-layer degree correlations to delineate the behavior spreading, and develop an edge-based compartmental (EBC) theory to describe the model. We find that multiplex networks promote the final behavior adoption size. Remarkably, it can be observed that the growth pattern of the final behavior adoption size, versus the behavioral information transmission probability, changes from discontinuous to continuous once decreasing the behavior adoption threshold in one layer. We finally unravel that the inter-layer degree correlations play a role on the final behavior adoption size but have no effects on the growth pattern, which is coincidence with our prediction by using the suggested theory.

Dynamics of organizational culture: Individual beliefs vs. social conformity.

PubMed

Ellinas, Christos; Allan, Neil; Johansson, Anders

2017-01-01

The complex nature of organizational culture challenges our ability to infer its underlying dynamics from observational studies. Recent computational studies have adopted a distinctly different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work-(a) omittance of an individual's strive for achieving cognitive coherence; (b) limited integration of important contextual factors-by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of its organizational culture, yet be composed of individuals with reduced levels of coherence; (ii) the components of social conformity-peer-pressure and social rank-are influential at different aggregation levels.

Social judgment theory based model on opinion formation, polarization and evolution

NASA Astrophysics Data System (ADS)

Chau, H. F.; Wong, C. Y.; Chow, F. K.; Fung, Chi-Hang Fred

2014-12-01

The dynamical origin of opinion polarization in the real world is an interesting topic that physical scientists may help to understand. To properly model the dynamics, the theory must be fully compatible with findings by social psychologists on microscopic opinion change. Here we introduce a generic model of opinion formation with homogeneous agents based on the well-known social judgment theory in social psychology by extending a similar model proposed by Jager and Amblard. The agents’ opinions will eventually cluster around extreme and/or moderate opinions forming three phases in a two-dimensional parameter space that describes the microscopic opinion response of the agents. The dynamics of this model can be qualitatively understood by mean-field analysis. More importantly, first-order phase transition in opinion distribution is observed by evolving the system under a slow change in the system parameters, showing that punctuated equilibria in public opinion can occur even in a fully connected social network.

Dynamics of organizational culture: Individual beliefs vs. social conformity

PubMed Central

Allan, Neil; Johansson, Anders

2017-01-01

The complex nature of organizational culture challenges our ability to infer its underlying dynamics from observational studies. Recent computational studies have adopted a distinctly different view, where plausible mechanisms are proposed to describe a wide range of social phenomena, including the onset and evolution of organizational culture. In this spirit, this work introduces an empirically-grounded, agent-based model which relaxes a set of assumptions that describes past work–(a) omittance of an individual’s strive for achieving cognitive coherence; (b) limited integration of important contextual factors—by utilizing networks of beliefs and incorporating social rank into the dynamics. As a result, we illustrate that: (i) an organization may appear to be increasingly coherent in terms of its organizational culture, yet be composed of individuals with reduced levels of coherence; (ii) the components of social conformity—peer-pressure and social rank—are influential at different aggregation levels. PMID:28665960

Persistent ISR: the social network analysis connection

NASA Astrophysics Data System (ADS)

Bowman, Elizabeth K.

2012-06-01

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

Controllability of social networks and the strategic use of random information.

PubMed

Cremonini, Marco; Casamassima, Francesca

2017-01-01

This work is aimed at studying realistic social control strategies for social networks based on the introduction of random information into the state of selected driver agents. Deliberately exposing selected agents to random information is a technique already experimented in recommender systems or search engines, and represents one of the few options for influencing the behavior of a social context that could be accepted as ethical, could be fully disclosed to members, and does not involve the use of force or of deception. Our research is based on a model of knowledge diffusion applied to a time-varying adaptive network and considers two well-known strategies for influencing social contexts: One is the selection of few influencers for manipulating their actions in order to drive the whole network to a certain behavior; the other, instead, drives the network behavior acting on the state of a large subset of ordinary, scarcely influencing users. The two approaches have been studied in terms of network and diffusion effects. The network effect is analyzed through the changes induced on network average degree and clustering coefficient, while the diffusion effect is based on two ad hoc metrics which are defined to measure the degree of knowledge diffusion and skill level, as well as the polarization of agent interests. The results, obtained through simulations on synthetic networks, show a rich dynamics and strong effects on the communication structure and on the distribution of knowledge and skills. These findings support our hypothesis that the strategic use of random information could represent a realistic approach to social network controllability, and that with both strategies, in principle, the control effect could be remarkable.

Neural dynamics of social tie formation in economic decision-making.

PubMed

Bault, Nadège; Pelloux, Benjamin; Fahrenfort, Johannes J; Ridderinkhof, K Richard; van Winden, Frans

2015-06-01

The disposition for prosocial conduct, which contributes to cooperation as arising during social interaction, requires cortical network dynamics responsive to the development of social ties, or care about the interests of specific interaction partners. Here, we formulate a dynamic computational model that accurately predicted how tie formation, driven by the interaction history, influences decisions to contribute in a public good game. We used model-driven functional MRI to test the hypothesis that brain regions key to social interactions keep track of dynamics in tie strength. Activation in the medial prefrontal cortex (mPFC) and posterior cingulate cortex tracked the individual's public good contributions. Activation in the bilateral posterior superior temporal sulcus (pSTS), and temporo-parietal junction was modulated parametrically by the dynamically developing social tie-as estimated by our model-supporting a role of these regions in social tie formation. Activity in these two regions further reflected inter-individual differences in tie persistence and sensitivity to behavior of the interaction partner. Functional connectivity between pSTS and mPFC activations indicated that the representation of social ties is integrated in the decision process. These data reveal the brain mechanisms underlying the integration of interaction dynamics into a social tie representation which in turn influenced the individual's prosocial decisions. © The Author (2014). Published by Oxford University Press. For Permissions, please email: journals.permissions@oup.com.

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 individuals protect themselves by disconnecting their links to infected neighbors with probability w and reconnecting them to other susceptible individuals chosen at random. Starting from a single infected individual, we show by an analytical approach and simulations that there is a phase transition at a critical rewiring (quarantine) threshold wc separating a phase (w < wc) where the disease reaches a large fraction of the population from a phase (w > wc) where the disease does not spread out. We find that in our model the topology of the network strongly affects the size of the propagation and that wc increases with the mean degree and heterogeneity of the network. We also find that wc is reduced if we perform a preferential rewiring, in which the rewiring probability is proportional to the degree of infected nodes. In the fourth chapter, we study epidemic processes on interconnected network systems, and find two distinct regimes. In strongly-coupled network systems, epidemics occur simultaneously across the entire system at a critical value betac. In contrast, in weakly-coupled network systems, a mixed phase exists below betac where an epidemic occurs in one network but does not spread to the coupled network. We derive an expression for the network and disease parameters that allow this mixed phase and verify it numerically. Public health implications of communities comprising these two classes of network systems are also mentioned.

Clinical social networking--a new revolution in provider communication and delivery of clinical information across providers of care?

PubMed

Kolowitz, Brian J; Lauro, Gonzalo Romero; Venturella, James; Georgiev, Veliyan; Barone, Michael; Deible, Christopher; Shrestha, Rasu

2014-04-01

The adoption of social media technologies appears to enhance clinical outcomes through improved communications as reported by Bacigalupe (Fam Syst Heal 29(1):1-14, 2011). The ability of providers to more effectively, directly, and rapidly communicate among themselves as well as with patients should strengthen collaboration and treatment as reported by Bacigalupe (Fam Syst Heal 29(1):1-14, 2011). This paper is a case study in one organization's development of an internally designed and developed social technology solution termed "Unite." The Unite system combines social technologies' features including push notifications, messaging, community groups, and user lists with clinical workflow and applications to construct dynamic provider networks, simplify communications, and facilitate clinical workflow optimization. Modeling Unite as a social technology may ease adoption barriers. Developing a social network that is integrated with healthcare information systems in the clinical space opens the doors to capturing and studying the way in which providers communicate. The Unite system appears to have the potential to breaking down existing communication paradigms. With Unite, a rich set of usage data tied to clinical events may unravel alternative networks that can be leveraged to advance patient care.

a Weighted Local-World Evolving Network Model Based on the Edge Weights Preferential Selection

NASA Astrophysics Data System (ADS)

Li, Ping; Zhao, Qingzhen; Wang, Haitang

2013-05-01

In this paper, we use the edge weights preferential attachment mechanism to build a new local-world evolutionary model for weighted networks. It is different from previous papers that the local-world of our model consists of edges instead of nodes. Each time step, we connect a new node to two existing nodes in the local-world through the edge weights preferential selection. Theoretical analysis and numerical simulations show that the scale of the local-world affect on the weight distribution, the strength distribution and the degree distribution. We give the simulations about the clustering coefficient and the dynamics of infectious diseases spreading. The weight dynamics of our network model can portray the structure of realistic networks such as neural network of the nematode C. elegans and Online Social Network.

Dynamics and nature of support in the personal networks of people with type 2 diabetes living in Europe: qualitative analysis of network properties.

PubMed

Kennedy, Anne; Rogers, Anne; Vassilev, Ivaylo; Todorova, Elka; Roukova, Poli; Foss, Christina; Knutsen, Ingrid; Portillo, Mari Carmen; Mujika, Agurtzane; Serrano-Gil, Manuel; Lionis, Christos; Angelaki, Agapi; Ratsika, Nikoleta; Koetsenruijter, Jan; Wensing, Michel

2015-12-01

Living with and self-managing a long-term condition implicates a diversity of networked relationships. This qualitative study examines the personal communities of support of people with type 2 diabetes. We conducted 170 biographical interviews in six European countries (Bulgaria, Greece, the Netherlands, Norway, Spain and UK) to explore social support and networks. Analysis was framed with reference to three predetermined social support mechanisms: the negotiation of support enabling engagement with healthy practices, navigation to sources of support and collective efficacy. Each interview was summarized to describe navigation and negotiation of participants' networks and the degree of collective efficacy. Analysis highlighted the similarities and differences between countries and provided insights into capacities of networks to support self-management. The network support mechanisms were identified in all interviews, and losses and gains in networks impacted on diabetes management. There were contextual differences between countries, most notably the impact of financial austerity on network dynamics. Four types of network are suggested: generative, diverse and beneficial to individuals; proxy, network members undertook diabetes management work; avoidant, support not engaged with; and struggling, diabetes management a struggle or not prioritized. It is possible to differentiate types of network input to living with and managing diabetes. Recognizing the nature of active, generative aspects of networks support is likely to have relevance for self-management support interventions either through encouraging continuing development and maintenance of these contacts or intervening to address struggling networks through introducing the means to connect people to additional sources of support. © 2014 John Wiley & Sons Ltd.

The many faces of graph dynamics

NASA Astrophysics Data System (ADS)

Pignolet, Yvonne Anne; Roy, Matthieu; Schmid, Stefan; Tredan, Gilles

2017-06-01

The topological structure of complex networks has fascinated researchers for several decades, resulting in the discovery of many universal properties and reoccurring characteristics of different kinds of networks. However, much less is known today about the network dynamics: indeed, complex networks in reality are not static, but rather dynamically evolve over time. Our paper is motivated by the empirical observation that network evolution patterns seem far from random, but exhibit structure. Moreover, the specific patterns appear to depend on the network type, contradicting the existence of a ‘one fits it all’ model. However, we still lack observables to quantify these intuitions, as well as metrics to compare graph evolutions. Such observables and metrics are needed for extrapolating or predicting evolutions, as well as for interpolating graph evolutions. To explore the many faces of graph dynamics and to quantify temporal changes, this paper suggests to build upon the concept of centrality, a measure of node importance in a network. In particular, we introduce the notion of centrality distance, a natural similarity measure for two graphs which depends on a given centrality, characterizing the graph type. Intuitively, centrality distances reflect the extent to which (non-anonymous) node roles are different or, in case of dynamic graphs, have changed over time, between two graphs. We evaluate the centrality distance approach for five evolutionary models and seven real-world social and physical networks. Our results empirically show the usefulness of centrality distances for characterizing graph dynamics compared to a null-model of random evolution, and highlight the differences between the considered scenarios. Interestingly, our approach allows us to compare the dynamics of very different networks, in terms of scale and evolution speed.

Bias, belief, and consensus: Collective opinion formation on fluctuating networks

NASA Astrophysics Data System (ADS)

Ngampruetikorn, Vudtiwat; Stephens, Greg J.

2016-11-01

With the advent of online networks, societies have become substantially more interconnected with individual members able to easily both maintain and modify their own social links. Here, we show that active network maintenance exposes agents to confirmation bias, the tendency to confirm one's beliefs, and we explore how this bias affects collective opinion formation. We introduce a model of binary opinion dynamics on a complex, fluctuating network with stochastic rewiring and we analyze these dynamics in the mean-field limit of large networks and fast link rewiring. We show that confirmation bias induces a segregation of individuals with different opinions and stabilizes the consensus state. We further show that bias can have an unusual, nonmonotonic effect on the time to consensus and this suggests a novel avenue for large-scale opinion manipulation.

Bias, belief, and consensus: Collective opinion formation on fluctuating networks.

PubMed

Ngampruetikorn, Vudtiwat; Stephens, Greg J

2016-11-01

With the advent of online networks, societies have become substantially more interconnected with individual members able to easily both maintain and modify their own social links. Here, we show that active network maintenance exposes agents to confirmation bias, the tendency to confirm one's beliefs, and we explore how this bias affects collective opinion formation. We introduce a model of binary opinion dynamics on a complex, fluctuating network with stochastic rewiring and we analyze these dynamics in the mean-field limit of large networks and fast link rewiring. We show that confirmation bias induces a segregation of individuals with different opinions and stabilizes the consensus state. We further show that bias can have an unusual, nonmonotonic effect on the time to consensus and this suggests a novel avenue for large-scale opinion manipulation.