Standard

Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning. / Mazalov, Vladimir V.

Transactions on Computational Collective Intelligence XXXI. ed. / Ngoc Thanh Nguyen; Richard Kowalczyk; Jacek Mercik; Anna Motylska-Kuźma. Berlin, Heidelberg : Springer Nature, 2018. p. 37-46.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

Harvard

Mazalov, VV 2018, Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning. in NT Nguyen, R Kowalczyk, J Mercik & A Motylska-Kuźma (eds), Transactions on Computational Collective Intelligence XXXI. Springer Nature, Berlin, Heidelberg, pp. 37-46. https://doi.org/10.1007/978-3-662-58464-4_4

APA

Mazalov, V. V. (2018). Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning. In N. T. Nguyen, R. Kowalczyk, J. Mercik, & A. Motylska-Kuźma (Eds.), Transactions on Computational Collective Intelligence XXXI (pp. 37-46). Springer Nature. https://doi.org/10.1007/978-3-662-58464-4_4

Vancouver

Mazalov VV. Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning. In Nguyen NT, Kowalczyk R, Mercik J, Motylska-Kuźma A, editors, Transactions on Computational Collective Intelligence XXXI. Berlin, Heidelberg: Springer Nature. 2018. p. 37-46 https://doi.org/10.1007/978-3-662-58464-4_4

Author

Mazalov, Vladimir V. / Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning. Transactions on Computational Collective Intelligence XXXI. editor / Ngoc Thanh Nguyen ; Richard Kowalczyk ; Jacek Mercik ; Anna Motylska-Kuźma. Berlin, Heidelberg : Springer Nature, 2018. pp. 37-46

BibTeX

@inbook{7634dfce9c024782984eb3bc68f6b38f,
title = "Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning",
abstract = "The purpose of this article is to show the relationship between the game-theoretic approach and the maximum likelihood method in the problem of community detection in networks. We formulate a cooperative game related with network structure where the nodes are players in a hedonic game and then we find the stable partition of the network into coalitions. This approach corresponds to the problem of maximizing a potential function and allows to detect clusters with various resolution. We propose here the maximum likelihood method for the tuning of the resolution parameter in the hedonic game. We illustrate this approach by some numerical examples.",
author = "Mazalov, {Vladimir V.}",
year = "2018",
doi = "10.1007/978-3-662-58464-4_4",
language = "русский",
isbn = "978-3-662-58464-4",
pages = "37--46",
editor = "Nguyen, {Ngoc Thanh} and Richard Kowalczyk and Jacek Mercik and Anna Motylska-Ku{\'z}ma",
booktitle = "Transactions on Computational Collective Intelligence XXXI",
publisher = "Springer Nature",
address = "Германия",

}

RIS

TY - CHAP

T1 - Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning

AU - Mazalov, Vladimir V.

PY - 2018

Y1 - 2018

N2 - The purpose of this article is to show the relationship between the game-theoretic approach and the maximum likelihood method in the problem of community detection in networks. We formulate a cooperative game related with network structure where the nodes are players in a hedonic game and then we find the stable partition of the network into coalitions. This approach corresponds to the problem of maximizing a potential function and allows to detect clusters with various resolution. We propose here the maximum likelihood method for the tuning of the resolution parameter in the hedonic game. We illustrate this approach by some numerical examples.

AB - The purpose of this article is to show the relationship between the game-theoretic approach and the maximum likelihood method in the problem of community detection in networks. We formulate a cooperative game related with network structure where the nodes are players in a hedonic game and then we find the stable partition of the network into coalitions. This approach corresponds to the problem of maximizing a potential function and allows to detect clusters with various resolution. We propose here the maximum likelihood method for the tuning of the resolution parameter in the hedonic game. We illustrate this approach by some numerical examples.

U2 - 10.1007/978-3-662-58464-4_4

DO - 10.1007/978-3-662-58464-4_4

M3 - глава/раздел

SN - 978-3-662-58464-4

SP - 37

EP - 46

BT - Transactions on Computational Collective Intelligence XXXI

A2 - Nguyen, Ngoc Thanh

A2 - Kowalczyk, Richard

A2 - Mercik, Jacek

A2 - Motylska-Kuźma, Anna

PB - Springer Nature

CY - Berlin, Heidelberg

ER -

ID: 133691253