  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.
