Cooperative Game Theory Approaches for Network Partitioning

TLDR

Community detection in networks traditionally relies on dense subgraph selection, but this paper focuses on game‑theoretic methods, viewing modularity‑based approaches as special cases of hedonic games. The study proposes using cooperative game theory to highlight both link density and cluster formation mechanisms in community detection. The authors employ two cooperative game theory approaches: a Myerson value‑based method and a hedonic game‑based method. Both methods detect clusters at multiple resolutions, with the hedonic game approach offering particularly intuitive resolution tuning.

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.