An interpretable approach for social network formation among heterogeneous agents
Author(s)
Alabdulkareem, Ahmad; Yuan, Yuan; Pentland, Alex Paul
Downloads41467-018-07089-x.pdf (722.4Kb)
PUBLISHER_CC
Publisher with Creative Commons License
Creative Commons Attribution
Terms of use
Metadata
Show full item recordAbstract
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.
Date issued
2018-11Department
Massachusetts Institute of Technology. Institute for Data, Systems, and SocietyJournal
Nature Communications
Publisher
Nature Publishing Group
Citation
Yuan, Yuan, Ahmad Alabdulkareem, and Alex “Sandy” Pentland. “An Interpretable Approach for Social Network Formation Among Heterogeneous Agents.” Nature Communications 9, no. 1 (November 8, 2018). © 2018 The Authors
Version: Final published version
ISSN
2041-1723