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.
Highlights
Understanding the mechanisms of network formation is central in social network analysis
In this paper, inspired by the network embedding techniques, we develop a social network formation model using representation learning methods for heterogeneous agents; to retain the interpretability, we maintain the inter-agent micro-structure characteristics of most agent-based models and the macro-level structures that are the focus of sociology
Since we limit the dimensionality of endowment vectors, similar to network embedding algorithms, each dimension does not necessarily have a specific meaning, but may be a combination of many attributes of an individual
Summary
Understanding the mechanisms of network formation is central in social network analysis. Previous work on modeling social network formation has typically employed game theory or agent-based modeling[14,15,16,17,18,19,20] These studies typically propose simple and tractable micro-level rules for link formation mechanisms and show that these rules have implications for known macro-level properties. Probabilistic membership models typically do not seek to uncover economic and sociological mechanisms and the dynamics of network formation We extend these previous works to the estimation of agent characteristics and network link formation using observed network data. We want to incorporate a more complex but interpretable inter-agent exchange utility function, by modeling both exchange benefits and coordination costs arising from the differences among agents
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