A game theoretic framework for community detection

Patrick J. McSweeney, Kishan Mehrotra, Jae C. Oh

Research output: Chapter in Book/Report/Conference proceedingConference contribution

16 Scopus citations

Abstract

The mainstream approach for community detection focuses on the optimization of a metric that measures the quality of a partition over a given network. Optimizing a global metric is akin to community assignment by a centralized decision maker. In liu of global optimization, we treat each node as a player in a hedonic game and focus on their ability to form fair and stable community structures. Application on real-world networks and a well-known benchmark demonstrates that our approach produces better results than modularity optimization.

Original languageEnglish (US)
Title of host publicationProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
Pages227-234
Number of pages8
DOIs
StatePublished - Dec 1 2012
Event2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 - Istanbul, Turkey
Duration: Aug 26 2012Aug 29 2012

Publication series

NameProceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012

Other

Other2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012
CountryTurkey
CityIstanbul
Period8/26/128/29/12

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Software

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    McSweeney, P. J., Mehrotra, K., & Oh, J. C. (2012). A game theoretic framework for community detection. In Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012 (pp. 227-234). [6425758] (Proceedings of the 2012 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2012). https://doi.org/10.1109/ASONAM.2012.47