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Comparing Game-Theoretic and Maximum Likelihood Approaches for Network Partitioning

  • Vladimir V. MazalovEmail author
Chapter
Part of the Lecture Notes in Computer Science book series (LNCS, 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.

Keywords

Network partitioning Community detection Cooperative games Hedonic games Tuning of the parameter Maximum likelihood method 

Notes

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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Copyright information

© Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Applied Mathematical ResearchKarelian Research Center, Russian Academy of SciencesPetrozavodskRussia
  2. 2.School of Mathematcis and Statistics and Institute of Applied MathematcisQingdao UniversityQingdaoPeople’s Republic of China

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