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Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning

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Transactions on Computational Collective Intelligence XXXI

Part of the book series: Lecture Notes in Computer Science ((TCCI,volume 11290))

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.

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Acknowledgements

This research is supported by the Russian Fund for Basic Research (projects 16-51-55006, 16-01-00183) and Shandong Province “Double-Hundred Talent Plan (No. WST2017009)”.

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Authors and Affiliations

  1. Institute of Applied Mathematical Research, Karelian Research Center, Russian Academy of Sciences, 11, Pushkinskaya st., Petrozavodsk, 185910, Russia

    Vladimir V. Mazalov

  2. School of Mathematcis and Statistics and Institute of Applied Mathematcis, Qingdao University, Qingdao, 266071, People’s Republic of China

    Vladimir V. Mazalov

Authors
  1. Vladimir V. Mazalov
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Correspondence to Vladimir V. Mazalov .

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Editors and Affiliations

  1. Department of Information Systems, Wrocław University of Technology, Wrocław, Poland

    Ngoc Thanh Nguyen

  2. Swinburne University of Technology, Hawthorn, VIC, Australia

    Richard Kowalczyk

  3. WSB University Wroclaw, Wroclaw, Poland

    Jacek Mercik

  4. WSB University Wroclaw, Wroclaw, Poland

    Anna Motylska-Kuźma

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Mazalov, V.V. (2018). Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning. In: Nguyen, N., Kowalczyk, R., Mercik, J., Motylska-Kuźma, A. (eds) Transactions on Computational Collective Intelligence XXXI. Lecture Notes in Computer Science(), vol 11290. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58464-4_4

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  • DOI: https://doi.org/10.1007/978-3-662-58464-4_4

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-662-58463-7

  • Online ISBN: 978-3-662-58464-4

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