Abstract
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.
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References
Krishnamurthy, B., Wang, J.: On network-aware clustering of web clients. SIGCOMM Comput. Commun. Rev. 30, 97–110 (2000)
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)
Agrawal, R., Jagadish, H.V.: Algorithms for searching massive graphs. IEEE Trans. on Knowl. and Data Eng. 6, 225–238 (1994)
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)
Perkins, C.E.: Ad Hoc Networking. Addison-Wesley Professional, Reading (2000)
Steenstrup, M.: Cluster-Based Networks. In: Perkins, C.E. (ed.) Ad Hoc Networking, pp. 75–138. Addison-Wesley, Reading (2001)
Weiss, R.S., Jacobson, E.: A method for the analysis of the structure of complex organizations. American Sociological Review 20, 661–668 (1955)
Rice, S.A.: The Identification of Blocs in Small Political Bodies. The American Political Science Review 21, 619–627 (1927)
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)
Holland, J.H.: Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence. MIT Press, Cambridge (1992)
Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms, http://arxiv.org/abs/0711.0491
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)
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)
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)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 26113 (2004)
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)
Handle, J., Knowles, J.: An evolutionary approach to Multiobjective clustering. IEEE Transactions on Evolutionary Computation 11, 56–76 (2007)
Zachary, W.W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)
Newman, M.E.J.: Modularity and community structure in networks. Proceedings of the National Academy of Sciences 103, 8577–8582 (2006)
Newman, M.E.J.: Fast algorithm for detecting community structure in networks. Physical Review E 69, 66133 (2004)
Lancichinetti, A., Fortunato, S., Kertesz, J.: Detecting the overlapping and hierarchical community structure in complex networks. New Journal of Physics 11, 33015 (2009)
Danon, L., Duch, J., Diaz-Guilera, A., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment, 9008 (2005)
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)
Srinivas, N., Deb, K.: Multiobjective optimization using nondominated sorting in genetic algorithms. Evolutionary Computation 2, 221–248 (1994)
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)
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Agrawal, R. (2011). Bi-Objective Community Detection (BOCD) in Networks Using Genetic Algorithm. In: Aluru, S., et al. Contemporary Computing. IC3 2011. Communications in Computer and Information Science, vol 168. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-22606-9_5
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DOI: https://doi.org/10.1007/978-3-642-22606-9_5
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