Bi-Objective Community Detection (BOCD) in Networks Using Genetic Algorithm

  • Rohan Agrawal
Part of the Communications in Computer and Information Science book series (CCIS, volume 168)


A lot of research effort has been put into community detection from all corners of academic interest such as physics, mathematics and computer science. In this paper I have proposed a Bi-Objective Genetic Algorithm for community detection which maximizes modularity and community score. Then the results obtained for both benchmark and real life data sets are compared with other algorithms using the modularity and MNI performance metrics. The results show that the BOCD algorithm is capable of successfully detecting community structure in both real life and synthetic datasets, as well as improving upon the performance of previous techniques.


Community Structure Community detection Genetic Algorithm Multi-objective Genetic Algorithm Multi-objective optimization modularity Normalized Mutual Information Bi-objective Genetic Algorithm 


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  1. 1.
    Krishnamurthy, B., Wang, J.: On network-aware clustering of web clients. SIGCOMM Comput. Commun. Rev. 30, 97–110 (2000)CrossRefGoogle Scholar
  2. 2.
    Reddy, P.K., Kitsuregawa, M., Sreekanth, P., Rao, S.S.: A graph based approach to extract a neighborhood customer community for collaborative filtering. In: Bhalla, S. (ed.) DNIS 2002. LNCS, vol. 2544, pp. 188–200. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  3. 3.
    Agrawal, R., Jagadish, H.V.: Algorithms for searching massive graphs. IEEE Trans. on Knowl. and Data Eng. 6, 225–238 (1994)CrossRefGoogle Scholar
  4. 4.
    Wu, A.Y., Garland, M., Han, J.: Mining scale-free networks using geodesic clustering. In: Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 719–724. ACM Press, New York (2004)Google Scholar
  5. 5.
    Perkins, C.E.: Ad Hoc Networking. Addison-Wesley Professional, Reading (2000)Google Scholar
  6. 6.
    Steenstrup, M.: Cluster-Based Networks. In: Perkins, C.E. (ed.) Ad Hoc Networking, pp. 75–138. Addison-Wesley, Reading (2001)Google Scholar
  7. 7.
    Weiss, R.S., Jacobson, E.: A method for the analysis of the structure of complex organizations. American Sociological Review 20, 661–668 (1955)CrossRefGoogle Scholar
  8. 8.
    Rice, S.A.: The Identification of Blocs in Small Political Bodies. The American Political Science Review 21, 619–627 (1927)CrossRefGoogle Scholar
  9. 9.
    Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences of the United States of America 99, 7821–7826 (2002)MathSciNetCrossRefMATHGoogle Scholar
  10. 10.
    Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)Google Scholar
  11. 11.
    Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms,
  12. 12.
    Liu, X., Li, D., Wang, S., Tao, Z.: Effective algorithm for detecting community structure in complex networks based on GA and clustering. In: Shi, Y., van Albada, G.D., Dongarra, J., Sloot, P.M.A. (eds.) ICCS 2007. LNCS, vol. 4488, pp. 657–664. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  13. 13.
    Pizzuti, C.: A Multi-objective Genetic Algorithm for Community Detection in Networks. In: Proceedings of the 2009 21st IEEE International Conference on Tools with Artificial Intelligence, pp. 379–386. IEEE Computer Society, Washington (2009)CrossRefGoogle Scholar
  14. 14.
    Pizzuti, C.: GA-net: A genetic algorithm for community detection in social networks. In: Rudolph, G., Jansen, T., Lucas, S., Poloni, C., Beume, N. (eds.) PPSN 2008. LNCS, vol. 5199, pp. 1081–1090. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  15. 15.
    Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 26113 (2004)CrossRefGoogle Scholar
  16. 16.
    Park, Y.J., Song, M.S.: A genetic algorithm for clustering problems. In: Proceedings of the 3rd Annual Conf. Genetic Programming, pp. 568–575. Morgan Kauffman, San Francisco (1998)Google Scholar
  17. 17.
    Handle, J., Knowles, J.: An evolutionary approach to Multiobjective clustering. IEEE Transactions on Evolutionary Computation 11, 56–76 (2007)CrossRefGoogle Scholar
  18. 18.
    Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)CrossRefGoogle Scholar
  19. 19.
    Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 8577–8582 (2006)CrossRefGoogle Scholar
  20. 20.
    Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69, 66133 (2004)CrossRefGoogle Scholar
  21. 21.
    Lancichinetti, A., Fortunato, S., Kertesz, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11, 33015 (2009)CrossRefGoogle Scholar
  22. 22.
    Danon, L., Duch, J., Diaz-Guilera, A., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 9008 (2005)Google Scholar
  23. 23.
    Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Physical Review E (Statistical, Nonlinear, and Soft Matter Physics) 78, 46110 (2008)CrossRefGoogle Scholar
  24. 24.
    Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2, 221–248 (1994)CrossRefGoogle Scholar
  25. 25.
  26. 26.
    Lusseau, D., Schneider, K., Boisseau, O.J., Haase, P., Slooten, E., Dawson, S.M.: The bottlenose dolphin community of doubtful sound features a large proportion of long-lasting associations. Behavioral Ecology and Sociobiology 54, 396–405 (2003)CrossRefGoogle Scholar

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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Rohan Agrawal
    • 1
  1. 1.Computer Science DepartmentJaypee Institute of Information TechnologyNoidaIndia

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