Cooperative Game Theory Approaches for Network Partitioning

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.
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Communication dans un congrès
CSoNet 2017 - The 6th International Conference on Computational Social Networks, Aug 2017, Hong Kong, Hong Kong SAR China. Springer, 2017, The 6th International Conference on Computational Social Networks (CSoNet 2017). 〈http://spacl.kennesaw.edu/csonet2017/〉. 〈10.1086/jar.33.4.3629752〉
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https://hal.inria.fr/hal-01560682
Contributeur : Konstantin Avrachenkov <>
Soumis le : jeudi 27 juillet 2017 - 09:46:55
Dernière modification le : jeudi 11 janvier 2018 - 16:48:44

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Konstantin Avrachenkov, Aleksei Kondratev, Vladimir Mazalov. Cooperative Game Theory Approaches for Network Partitioning. CSoNet 2017 - The 6th International Conference on Computational Social Networks, Aug 2017, Hong Kong, Hong Kong SAR China. Springer, 2017, The 6th International Conference on Computational Social Networks (CSoNet 2017). 〈http://spacl.kennesaw.edu/csonet2017/〉. 〈10.1086/jar.33.4.3629752〉. 〈hal-01560682v2〉

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