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Mining Communities in Directed Networks: A Game Theoretic Approach

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Intelligent Systems Design and Applications (ISDA 2017)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 736))

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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.

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References

  1. Dugué, N., Perez, A.: Directed Louvain: maximizing modularity in directed networks. Ph.D. thesis, Université d’Orléans (2015)

    Google Scholar 

  2. Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)

    Article  MathSciNet  Google Scholar 

  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)

    Article  Google Scholar 

  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)

    Article  Google Scholar 

  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. 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)

    Article  Google Scholar 

  7. Leicht, E.A., Newman, M.E.: Community structure in directed networks. Phys. Rev. Lett. 100(11), 118703 (2008)

    Article  Google Scholar 

  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. Long, H., Li, B.: Overlapping community identification algorithm in directed network. Procedia Comput. Sci. 107, 527–532 (2017)

    Article  Google Scholar 

  10. Malliaros, F.D., Vazirgiannis, M.: Clustering and community detection in directed networks: a survey. Phys. Rep. 533(4), 95–142 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  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)

    Article  Google Scholar 

  12. Myerson, R.B.: Game Theory. Harvard University Press, Cambridge (2013)

    MATH  Google Scholar 

  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)

    Article  Google Scholar 

  14. Ning, X., Liu, Z., Zhang, S.: Local community extraction in directed networks. Phys. A Stat. Mech. Appl. 452, 258–265 (2016)

    Article  Google Scholar 

  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. Santos, C.P., Carvalho, D.M., Nascimento, M.C.: A consensus graph clustering algorithm for directed networks. Expert Syst. Appl. 54, 121–135 (2016)

    Article  Google Scholar 

  17. Shoham, Y., Leyton-Brown, K.: Multiagent Systems: Algorithmic, Game-Theoretic, and Logical Foundations. Cambridge University Press, Cambridge (2008)

    Book  MATH  Google Scholar 

  18. Sun, P.G., Gao, L.: A framework of mapping undirected to directed graphs for community detection. Inf. Sci. 298, 330–343 (2015)

    Article  MATH  Google Scholar 

  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. Wasserman, S., Faust, K.: Social network analysis: methods and applications (1995)

    Google Scholar 

  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)

    Article  Google Scholar 

Download references

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Authors and Affiliations

  1. School of Computer Science and Engineering, VIT University, Vellore, 632014, India

    Annapurna Jonnalagadda & Lakshmanan Kuppusamy

Authors
  1. Annapurna Jonnalagadda
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  2. Lakshmanan Kuppusamy
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Corresponding author

Correspondence to Annapurna Jonnalagadda .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs , Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science, South Asian University, Chanakyapuri, Delhi, India

    Pranab Kr. Muhuri

  3. Faculty of Information and Communication Technology, Universiti Teknikal Malaysia Melaka , Durian Tunggal, Melaka, Malaysia

    Azah Kamilah Muda

  4. Machine Intelligence Research Labs , Auburn, Washington, USA

    Niketa Gandhi

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Jonnalagadda, A., Kuppusamy, L. (2018). Mining Communities in Directed Networks: A Game Theoretic Approach. In: Abraham, A., Muhuri, P., Muda, A., Gandhi, N. (eds) Intelligent Systems Design and Applications. ISDA 2017. Advances in Intelligent Systems and Computing, vol 736. Springer, Cham. https://doi.org/10.1007/978-3-319-76348-4_79

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  • DOI: https://doi.org/10.1007/978-3-319-76348-4_79

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-76347-7

  • Online ISBN: 978-3-319-76348-4

  • eBook Packages: EngineeringEngineering (R0)

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