Network partitioning algorithms as cooperative games

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 dens...

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Bibliographic Details
Published in:Computational social networks 2018-12, Vol.5 (1), p.11-28, Article 11
Main Authors: Avrachenkov, Konstantin E., Kondratev, Aleksei Y., Mazalov, Vladimir V., Rubanov, Dmytro G.
Format: Article
Language:English
Subjects:
Article Collection of the 6th International Conference on Complex Networks and Their Applications
Communities
Computer Science
Data-driven Science
Database Management
Game theory
Graph theory
Industrial and Production Engineering
Industrial Organization
Machine Learning
Mathematical Models of Cognitive Processes and Neural Networks
Mathematics
Mathematics and Statistics
Media Sociology
Modeling and Theory Building
Modularity
Social and Information Networks
Statistics
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Summary: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.
ISSN:2197-4314
2197-4314
DOI:10.1186/s40649-018-0059-5