An interpretable approach for social network formation among heterogeneous agents. Issue 1 (December 2018) - Yuan, Yuan; Alabdulkareem, Ahmad; Pentland, Alex
Abstract Understanding the mechanisms of network formation is central in social network analysis. Network formation has been studied in many research fields with their different focuses; for example, network embedding algorithms in machine learning literature consider broad heterogeneity among agents while the social sciences emphasize the interpretability of link formation mechanisms. Here we propose a social network formation model that integrates methods in multiple disciplines and retain both heterogeneity and interpretability. We represent each agent by an "endowment vector" that encapsulates their features and use game-theoretical methods to model the utility of link formation. After applying machine learning methods, we further analyze our model by examining micro- and macro- level properties of social networks as most agent-based models do. Our work contributes to the literature on network formation by combining the methods in game theory, agent-based modeling, machine learning, and computational sociology. Complex networks can be a useful tool to investigate problems in social science. Here the authors use game theory to establish a network model and then use a machine learning approach to characterize the role of nodes within a social network.