An Information Flow Model for Conflict and Fission in Small Groups

TLDR

The paper focuses on game‑theoretic approaches to community detection, contrasting traditional density‑based methods with modularity‑based techniques that can be seen as special cases of hedonic games. The study proposes using cooperative game‑theoretic methods to detect communities by emphasizing both link density and cluster‑formation mechanisms. The authors employ two cooperative game‑theoretic approaches: a Myerson‑value based method and a hedonic‑games based method. Both methods detect clusters at multiple resolutions, with the hedonic‑games approach offering especially 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.