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Mining Communities in Directed Networks: A Game Theoretic Approach

  • Annapurna Jonnalagadda
  • Lakshmanan Kuppusamy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

Detecting the communities in directed networks is a challenging task. Many of the existing community detection algorithm are designed to disclose the community structure for undirected networks. These algorithms can be applied to directed networks by transforming the directed networks to undirected. However, ignoring the direction of the links loses the information concealed along the link and end-up with imprecise community structure. In this paper, we retain the direction of the graph and propose a cooperative game in order to capture the interactions among the nodes of the network. We develop a greedy community detection algorithm to disclose the overlapping communities of the given directed network. Experimental evaluation on synthetic networks illustrates that the algorithm is able to disclose the correct number of communities with good community structure.

Keywords

Cooperative game Directed networks Game theory Utility of node 

References

  1. 1.
    Dugué, N., Perez, A.: Directed Louvain: maximizing modularity in directed networks. Ph.D. thesis, Université d’Orléans (2015)Google Scholar
  2. 2.
    Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Havens, T.C., Bezdek, J.C., Leckie, C., Ramamohanarao, K., Palaniswami, M.: A soft modularity function for detecting fuzzy communities in social networks. IEEE Trans. Fuzzy Syst. 21(6), 1170–1175 (2013)CrossRefGoogle Scholar
  4. 4.
    Jonnalagadda, A., Kuppusamy, L.: A survey on game theoretic models for community detection in social networks. Soc. Netw. Anal. Min. 6(1), 83 (2016)CrossRefGoogle Scholar
  5. 5.
    Jonnalagadda, A., Kuppusamy, L.: A cooperative game framework for detecting overlapping communities in social networks. Phys. A Stat. Mech. Appl. (2017)Google Scholar
  6. 6.
    Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Phys. Rev. E 80(1), 016118 (2009)CrossRefGoogle Scholar
  7. 7.
    Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118703 (2008)CrossRefGoogle Scholar
  8. 8.
    Levorato, V., Petermann, C.: Detection of communities in directed networks based on strongly p-connected components. In: 2011 International Conference on Computational Aspects of Social Networks (CASoN), pp. 211–216. IEEE (2011)Google Scholar
  9. 9.
    Long, H., Li, B.: Overlapping community identification algorithm in directed network. Procedia Comput. Sci. 107, 527–532 (2017)CrossRefGoogle Scholar
  10. 10.
    Malliaros, F.D., Vazirgiannis, M.: Clustering and community detection in directed networks: a survey. Phys. Rep. 533(4), 95–142 (2013)MathSciNetCrossRefzbMATHGoogle Scholar
  11. 11.
    Mathias, S.B., Rosset, V., Nascimento, M.C.: Community detection by consensus genetic-based algorithm for directed networks. Procedia Comput. Sci. 96, 90–99 (2016)CrossRefGoogle Scholar
  12. 12.
    Myerson, R.B.: Game Theory. Harvard University Press, Cambridge (2013)zbMATHGoogle Scholar
  13. 13.
    Nicosia, V., Mangioni, G., Carchiolo, V., Malgeri, M.: Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech. Theory Exp. 2009(03), P03024 (2009)CrossRefGoogle Scholar
  14. 14.
    Ning, X., Liu, Z., Zhang, S.: Local community extraction in directed networks. Phys. A Stat. Mech. Appl. 452, 258–265 (2016)CrossRefGoogle Scholar
  15. 15.
    Palla, G., Ábel, D., Farkas, I.J., Pollner, P., Derényi, I., Vicsek, T.: \(k\)-clique percolation and clustering. In: Handbook of Large-Scale Random Networks, pp. 369–408. Springer (2008)Google Scholar
  16. 16.
    Santos, C.P., Carvalho, D.M., Nascimento, M.C.: A consensus graph clustering algorithm for directed networks. Expert Syst. Appl. 54, 121–135 (2016)CrossRefGoogle Scholar
  17. 17.
    Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, Cambridge (2008)CrossRefzbMATHGoogle Scholar
  18. 18.
    Sun, P.G., Gao, L.: A framework of mapping undirected to directed graphs for community detection. Inf. Sci. 298, 330–343 (2015)CrossRefzbMATHGoogle Scholar
  19. 19.
    Wang, Q., Fleury, E.: Overlapping community structure and modular overlaps in complex networks. In: Mining Social Networks and Security Informatics, pp. 15–40. Springer (2013)Google Scholar
  20. 20.
    Wasserman, S., Faust, K.: Social network analysis: methods and applications (1995)Google Scholar
  21. 21.
    Zhou, L., Lü, K., Yang, P., Wang, L., Kong, B.: An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory. Expert Syst. Appl. 42(24), 9634–9646 (2015)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.School of Computer Science and EngineeringVIT UniversityVelloreIndia

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