Emergence of communities and diversity in social networks

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.This work was supported by the National Nature Science Foundation of China under Grants 61573064, 71631002, 71401037, and 11301032; the Fundamental Research Funds for the Central Universities and Beijing Nova Programme; and the Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant). The Boston University work was supported by NSF Grants PHY-1505000, CMMI-1125290, and CHE- 1213217, and by Defense Threat Reduction Agency Grant HDTRA1-14-1-0017, and Department of Energy Contract DE-AC07-05Id14517. (61573064 - National Nature Science Foundation of China; 71631002 - National Nature Science Foundation of China; 71401037 - National Nature Science Foundation of China; 11301032 - National Nature Science Foundation of China; Fundamental Research Funds for the Central Universities and Beijing Nova Programme; Natural Sciences and Engineering Research Council of Canada (Individual Discovery Grant); PHY-1505000 - NSF; CMMI-1125290 - NSF; CHE-1213217 - NSF; HDTRA1-14-1-0017 - Defense Threat Reduction Agency; DE-AC07-05Id14517 - Department of Energy)Published versio

New Complexity Results and Algorithms for the Minimum Tollbooth Problem

The inefficiency of the Wardrop equilibrium of nonatomic routing games can be eliminated by placing tolls on the edges of a network so that the socially optimal flow is induced as an equilibrium flow. A solution where the minimum number of edges are tolled may be preferable over others due to its ease of implementation in real networks. In this paper we consider the minimum tollbooth (MINTB) problem, which seeks social optimum inducing tolls with minimum support. We prove for single commodity networks with linear latencies that the problem is NP-hard to approximate within a factor of $1.1377$ through a reduction from the minimum vertex cover problem. Insights from network design motivate us to formulate a new variation of the problem where, in addition to placing tolls, it is allowed to remove unused edges by the social optimum. We prove that this new problem remains NP-hard even for single commodity networks with linear latencies, using a reduction from the partition problem. On the positive side, we give the first exact polynomial solution to the MINTB problem in an important class of graphs---series-parallel graphs. Our algorithm solves MINTB by first tabulating the candidate solutions for subgraphs of the series-parallel network and then combining them optimally

Towards Scalable Network Delay Minimization

Reduction of end-to-end network delays is an optimization task with applications in multiple domains. Low delays enable improved information flow in social networks, quick spread of ideas in collaboration networks, low travel times for vehicles on road networks and increased rate of packets in the case of communication networks. Delay reduction can be achieved by both improving the propagation capabilities of individual nodes and adding additional edges in the network. One of the main challenges in such design problems is that the effects of local changes are not independent, and as a consequence, there is a combinatorial search-space of possible improvements. Thus, minimizing the cumulative propagation delay requires novel scalable and data-driven approaches. In this paper, we consider the problem of network delay minimization via node upgrades. Although the problem is NP-hard, we show that probabilistic approximation for a restricted version can be obtained. We design scalable and high-quality techniques for the general setting based on sampling and targeted to different models of delay distribution. Our methods scale almost linearly with the graph size and consistently outperform competitors in quality

Social network profiles as information sources for adolescents' offline relations

This paper presents the results of a study concerning the use of online profile pages by adolescents to know more about “offline” friends and acquaintances. Previous research has indicated that social networking sites (SNSs) are used to gather information on new online contacts. However, several studies have demonstrated a substantial overlap between offline and online social networks. Hence, we question whether online connections are meaningful in gathering information on offline friends and acquaintances. First, the results indicate that a combination of passive uncertainty reduction (monitoring a target’s profile) and interactive uncertainty reduction (communication through the target’s profile) explains a considerable amount of variance in the level of uncertainty about both friends and acquaintances. More specifically, adolescents generally get to know much more about their acquaintances. Second, the results of online uncertainty reduction positively affect the degree of self-disclosure, which is imperative in building a solid friend relation. Furthermore, we find that uncertainty reduction strategies positively mediate the effect of social anxiety on the level of certainty about friends. This implies that socially anxious teenagers benefit from SNSs by getting the conditions right to build a more solid relation with their friends. Hence, we conclude that SNSs play a substantial role in today’s adolescents’ everyday interpersonal communication

Determinants of Depressive Symptoms Among Women on Public Assistance in Louisiana

Depression can be a significant barrier in the welfare-to-work transition of poor women. Fortunately, support from social networks can lessen symptoms and facilitate entry into the workplace. Inconsistency in the literature concerning the effects of social networks on the poor suggests further research is needed. Thus, we examine the level and determinants of depressive symptoms among participants in the Temporary Assistance to Needy Families program. Having a good job, being in good health, married, and black, and living in rural areas inhibit symptoms of depression. Remaining on TANF and having several children increases symptom levels. Those who report that they frequently have people to help them show lower levels of depression. The larger the social network, and the higher the percent of the network that is made up of neighbors, the higher the level of depression. While some of our findings suggest the success of 1996 welfare reform legislation others suggest important policy considerations. Good physical health (including access to health care), reduction of economic hardships, and effective social supports are ongoing issues to be addressed among low-income populations

See you on Facebook: the effect of social networking on human interaction

This paper proposes an evolutionary framework to explore the dynamics of social interaction in an environment characterized by online networking and increasing pressure on time. The model shows how time pressure encourages the choice to develop social interactions also through online networking instead of relying exclusively on face to face encounters. Our findings suggest that the joint influence exerted by the reduction in leisure time and the new opportunities of participation offered by web-mediated communication may progressively lead a growing share of the population to adopt networking sites as an indispensable environment for the development of interpersonal relationships.internet, computer-mediated communication, social networking, online networks, Facebook, human interaction, social capital

Exploring internal child sex trafficking networks using social network analysis

This article explores the potential of social network analysis as a tool in supporting the investigation of internal child sex trafficking in the UK. In doing so, it uses only data, software, and training already available to UK police. Data from two major operations are analysed using in-built centrality metrics, designed to measure a network’s overarching structural properties and identify particularly powerful individuals. This work addresses victim networks alongside offender networks. The insights generated by SNA inform ideas for targeted interventions based on the principles of Situational Crime Prevention. These harm-reduction initiatives go beyond traditional enforcement to cover prevention, disruption, prosecution, etc. This article ends by discussing how SNA can be applied and further developed by frontline policing, strategic policing, prosecution, and policy and research

Topological data analysis of contagion maps for examining spreading processes on networks

Social and biological contagions are influenced by the spatial embeddedness of networks. Historically, many epidemics spread as a wave across part of the Earth's surface; however, in modern contagions long-range edges -- for example, due to airline transportation or communication media -- allow clusters of a contagion to appear in distant locations. Here we study the spread of contagions on networks through a methodology grounded in topological data analysis and nonlinear dimension reduction. We construct "contagion maps" that use multiple contagions on a network to map the nodes as a point cloud. By analyzing the topology, geometry, and dimensionality of manifold structure in such point clouds, we reveal insights to aid in the modeling, forecast, and control of spreading processes. Our approach highlights contagion maps also as a viable tool for inferring low-dimensional structure in networks.Comment: Main Text and Supplementary Informatio