Social networks

Social networks affect individual behavior as well as social phenomena. Conversely, when actors can choose with whom to interact, social networks are also themselves affected by individual behavior. This chapter provides an overview of two main classes of formal theoretical models for the analysis of network effects and network formation, namely, game-theoretic models and agent-based simulation models. We first discuss models in which networks are assumed to be exogenous and focus on network effects. More specifically, we focus on models predicting effects of social networks on behavior in social dilemmas. Second, we summarize main approaches to network formation and the dynamics of networks. Third, we review models on the co-evolution of networks and behavior that provide an integrated analysis of network formation and network effects, again focusing on social dilemma problems. The chapter ends with an evaluation of the state of the art of theoretical models for social networks, including open problems and suggestions for future research

Predicting Empathy From Resting State Brain Connectivity: A Multivariate Approach.

Recent task fMRI studies suggest that individual differences in trait empathy and empathic concern are mediated by patterns of connectivity between self-other resonance and top-down control networks that are stable across task demands. An untested implication of this hypothesis is that these stable patterns of connectivity should be visible even in the absence of empathy tasks. Using machine learning, we demonstrate that patterns of resting state fMRI connectivity (i.e. the degree of synchronous BOLD activity across multiple cortical areas in the absence of explicit task demands) of resonance and control networks predict trait empathic concern (n = 58). Empathic concern was also predicted by connectivity patterns within the somatomotor network. These findings further support the role of resonance-control network interactions and of somatomotor function in our vicariously driven concern for others. Furthermore, a practical implication of these results is that it is possible to assess empathic predispositions in individuals without needing to perform conventional empathy assessments

Predicting Scientific Success Based on Coauthorship Networks

We address the question to what extent the success of scientific articles is due to social influence. Analyzing a data set of over 100000 publications from the field of Computer Science, we study how centrality in the coauthorship network differs between authors who have highly cited papers and those who do not. We further show that a machine learning classifier, based only on coauthorship network centrality measures at time of publication, is able to predict with high precision whether an article will be highly cited five years after publication. By this we provide quantitative insight into the social dimension of scientific publishing - challenging the perception of citations as an objective, socially unbiased measure of scientific success.Comment: 21 pages, 2 figures, incl. Supplementary Materia