Network partitioning algorithms as cooperative games
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 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.
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