The paper is devoted to game-theoretic methods for community detection in networks. The traditional methods for detecting community structure are based on selecting dense 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 resolutions. However, the tuning of the resolution parameter in the hedonic games approach is particularly intuitive. Furthermore, the modularity-based approach and its generalizations as well as ratio cut and normalized cut methods can be viewed as particular cases of the hedonic games. Finally, for approaches based on potential hedonic games we suggest a very efficient computational scheme using Gibbs sampling.

中文翻译：

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网络分区算法作为合作游戏。

本文致力于网络社区检测的博弈论方法。检测社区结构的传统方法是基于选择网络内部的密集子图。在这里，我们建议使用合作博弈论的方法，该方法不仅强调链接密度，而且强调集群形成的机制。具体而言，我们从合作博弈论中建议了两种方法：第一种方法基于Myerson值，而第二种方法则基于享乐游戏。两种方法都可以检测具有各种分辨率的群集。但是，享乐游戏方法中分辨率参数的调整特别直观。此外，基于模块化的方法及其推广以及比率削减和归一化削减方法可以视为享乐游戏的特殊情况。最后，对于基于潜在享乐游戏的方法，我们建议使用Gibbs采样的非常有效的计算方案。