Network partitioning algorithms as cooperative games - Archive ouverte HAL Access content directly
Journal Articles Computational Social Networks Year : 2018

Network partitioning algorithms as cooperative games

(1) , (2, 3) , (2, 4) , (1)
1
2
3
4

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.
Fichier principal
Vignette du fichier
CSON2018_5_11.pdf (3.71 Mo) Télécharger le fichier
Origin : Files produced by the author(s)
Loading...

Dates and versions

hal-01935419 , version 1 (26-11-2018)

Identifiers

Cite

Konstantin Avrachenkov, Aleksei Yu Kondratev, Vladimir V Mazalov, Dmytro Rubanov. Network partitioning algorithms as cooperative games. Computational Social Networks, 2018, 5 (11), ⟨10.1186/s40649-018-0059-5⟩. ⟨hal-01935419⟩
54 View
74 Download

Altmetric

Share

Gmail Facebook Twitter LinkedIn More