Social Network Analysis Using Coordination Games

TL;DR

This paper introduces a novel game-theoretic approach using coordination games to detect overlapping communities in social networks, demonstrating significant improvements over existing methods on benchmark datasets.

Contribution

It presents a scalable two-phase algorithm for community detection based on coordination games, advancing the understanding of social network topology and information spread.

Findings

Improved accuracy in community detection over existing methods

Effective handling of overlapping communities

Validated on real and synthetic benchmark networks

Abstract

Communities typically capture homophily as people of the same community share many common features. This paper is motivated by the problem of community detection in social networks, as it can help improve our understanding of the network topology and the spread of information. Given the selfish nature of humans to align with like-minded people, we employ game theoretic models and algorithms to detect communities in this paper. Specifically, we employ coordination games to represent interactions between individuals in a social network. We represent the problem of community detection as a graph coordination game. We provide a novel and scalable two phased approach to compute an accurate overlapping community structure in the given network. We evaluate our algorithm against the best existing methods for community detection and show that our algorithm improves significantly on benchmark networks (real and synthetic) with respect to standard Normalised Mutual Information measure.