Network partitioning algorithms as cooperative games

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Network partitioning algorithms as cooperative games. / Avrachenkov, Konstantin E.; Kondratev, Aleksei Y.; Mazalov, Vladimir V.; Rubanov, Dmytro G.

In: Computational Social Networks, Vol. 5, No. 1, 2018.

Research output: Contribution to journal › Article › peer-review

Author

Avrachenkov, Konstantin E. ; Kondratev, Aleksei Y. ; Mazalov, Vladimir V. ; Rubanov, Dmytro G. / Network partitioning algorithms as cooperative games. In: Computational Social Networks. 2018 ; Vol. 5, No. 1.

BibTeX

@article{dda409bb38ab48d8be6eb7595bfa05e8,

title = "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.",

author = "Avrachenkov, {Konstantin E.} and Kondratev, {Aleksei Y.} and Mazalov, {Vladimir V.} and Rubanov, {Dmytro G.}",

year = "2018",

doi = "10.1186/s40649-018-0059-5",

language = "English",

volume = "5",

journal = "Journal of Combinatorial Optimization",

issn = "1382-6905",

publisher = "Springer Nature",

number = "1",

}

RIS

TY - JOUR

T1 - Network partitioning algorithms as cooperative games

AU - Avrachenkov, Konstantin E.

AU - Kondratev, Aleksei Y.

AU - Mazalov, Vladimir V.

AU - Rubanov, Dmytro G.

PY - 2018

Y1 - 2018

N2 - 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.

AB - 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.

U2 - 10.1186/s40649-018-0059-5

DO - 10.1186/s40649-018-0059-5

M3 - Article

VL - 5

JO - Journal of Combinatorial Optimization

JF - Journal of Combinatorial Optimization

SN - 1382-6905

IS - 1

ER -

ID: 128971518