An interpretable approach for social network formation among heterogeneous agents.

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 propertie

SUBMITTER: Yuan Y 

PROVIDER: S-EPMC6224571 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature