Cooperative Game Theory Approaches for Network Partitioning

TL;DR

This paper introduces cooperative game theory methods, including Myerson value and hedonic games, for community detection in networks, providing flexible resolution detection and unifying existing modularity-based approaches.

Contribution

It proposes novel cooperative game theory-based algorithms for network partitioning, offering intuitive resolution tuning and connecting to existing modularity methods.

Findings

Effective detection of communities with various resolutions

Hedonic games approach offers intuitive resolution tuning

Modularity-based methods are special cases of the proposed approaches

Abstract

The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting denser subgraphs inside the network. Here we propose to use the methods of cooperative game theory that highlight not only the link density but also the mechanisms of cluster formation. Specifically, we suggest two approaches from cooperative game theory: the first approach is based on the Myerson value, whereas the second approach is based on hedonic games. Both approaches allow to detect clusters with various resolution. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity based approach and its generalizations can be viewed as particular cases of the hedonic games.