Abstract
Recent developments and extensive usage of social networking applications have facilitated enormous amounts of essential data. It can be examined for numerous reasons by companies, governments, nonprofit organizations. The problem in social networks analysis mainly emerged of its vast scale and complicated relations in the networks. These networks can be analyzed and visualized using community structure properties. This paper introduces an agglomerative hierarchical community detection approach, Enhanced Louvain method (ELM), to identify communities in complex networks. We proposed a modularity and similarity measure-based approach that does not need the information of the communities as input and can find community structure in a rapid way. Experimental results on real world network datasets demonstrate the performance of the proposed ELM method over its counterparts to show that good results can be generated. The performance of methods evaluated in terms of communities, modularity value and quality of community obtained in the network.
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References
Fortunato, S.: Community detection in graphs. Phys. Rep. 486(3), 75–174 (2010)
Chen, L., et al.: Fast community detection based on distance dynamics. Tsinghua Sci. Technol. 22(6), 564–585 (2017)
Blondel, V.D., et al.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008(10), 10008 (2008)
Odent, J., Saint-Guillain, M.: Automatic detection of community structure in networks (2012)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Radicchi, F., et al.: Defining and identifying communities in networks. Proc. Natl. Acad. Sci. U.S.A. 101(9), 2658–2663 (2004)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 74(3), 036104 (2006)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks. Phys. Rev. E 70(6), 066111 (2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69(2), 026113 (2004)
Jaccard, P.: Distribution de la flore alpine dans le bassin des Dranses et dans quelques régions voisines. Bulletin de la Société Vaudoise des Sciences Naturelles 37, 241–272 (1901)
Singhal, A.: Modern information retrieval: a brief overview. Bull. IEEE Comput. Soc. Tech. Comm. Data Eng. 24(4), 35–43 (2001)
Krebs, V.: A network of books about US politics (2004)
Leskovec, J., Mcauley, J.J.: Learning to discover social circles in ego networks. In: Advances in Neural Information Processing Systems (2012)
Richardson, M., Agrawal, R., Domingos, P.: Trust management for the semantic web. In: Fensel, D., Sycara, K., Mylopoulos, J. (eds.) ISWC 2003. LNCS, vol. 2870, pp. 351–368. Springer, Heidelberg (2003). https://doi.org/10.1007/978-3-540-39718-2_23
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: friendship and mobility: user movement in location-based social networks. In: ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD) (2011)
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Chaudhary, L., Singh, B. (2019). Community Detection Using an Enhanced Louvain Method in Complex Networks. In: Fahrnberger, G., Gopinathan, S., Parida, L. (eds) Distributed Computing and Internet Technology. ICDCIT 2019. Lecture Notes in Computer Science(), vol 11319. Springer, Cham. https://doi.org/10.1007/978-3-030-05366-6_20
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DOI: https://doi.org/10.1007/978-3-030-05366-6_20
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