An interpretable approach for social network formation among heterogeneous agents

Authored by Yuan Yuan, Ahmad Alabdulkareem, Alex `Sandy' Pentland

Date Published: 2018

DOI: 10.1038/s41467-018-07089-x

Sponsors: No sponsors listed

Platforms: Python

Model Documentation: Other Narrative Mathematical description

Model Code URLs: https://github.com/yuany94/endowment

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.

Tags

Evolution Cooperation Model Power Exchange networks