Abstract
Membership diversity is a characteristic aspect of social networks in which a person may belong to more than one social group. For this reason, discovering overlapping structures is necessary for realistic social analysis. In this paper, we present a fast algorithm, called SLPA, for overlapping community detection in large-scale networks. SLPA spreads labels according to dynamic interaction rules. It can be applied to both unipartite and bipartite networks. It is also able to uncover overlapping nested hierarchy. The time complexity of SLPA scales linearly with the number of edges in the network. Experiments in both synthetic and real-world networks show that SLPA has an excellent performance in identifying both node and community level overlapping structures.
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References
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466, 761–764 (2010)
Baumes, J., Goldberg, M., Magdon-Ismail, M.: Efficient Identification of Overlapping Communities. In: Kantor, P., Muresan, G., Roberts, F., Zeng, D.D., Wang, F.-Y., Chen, H., Merkle, R.C. (eds.) ISI 2005. LNCS, vol. 3495, pp. 27–36. Springer, Heidelberg (2005)
Collins, L.M., Dent, C.W.: Omega: A general formulation of the rand index of cluster recovery suitable for non-disjoint solutions. MBR 23, 231–242 (1988)
Farkas, I., Ábel, D., Palla, G., Vicsek, T.: Weighted network modules. New Journal of Physics 9(6), 180 (2007)
Gregory, S.: Finding overlapping communities in networks by label propagation. New J. Phys. 12, 103018 (2010)
Kelley, S.: The existence and discovery of overlapping communities in large-scale networks. Ph.D. thesis, Rensselaer Polytechnic Institute, Troy, NY (2009)
Lancichinetti, A., Fortunato, S., Kertesz, J.: Detecting the overlapping and hierarchical community structure in complex networks. New J. Phys., 033015 (2009)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78, 046110 (2008)
Lee, C., Reid, F., McDaid, A., Hurley, N.: Detecting highly overlapping community structure by greedy clique expansion. In: snakdd. pp. 33–42 (2010)
Nicosia, V., Mangioni, G., Carchiolo, V., Malgeri, M.: Extending the definition of modularity to directed graphs with overlapping communities. J. Stat. Mech., 03024 (2009)
Palla, G., Derényi, I., Farkas, I., Vicsek, T.: Uncovering the overlapping community structure of complex networks in nature and society. Nature, 814–818 (2005)
Psorakis, I., Roberts, S., Ebden, M., Sheldon, B.: Overlapping community detection using bayesian non-negative matrix factorization. Phys. Rev. E 83, 066114 (2011)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76, 036106 (2007)
Shen, H., Cheng, X., Cai, K., Hu, M.B.: Detect overlapping and hierarchical community structure. Physica A 388, 1706 (2009)
Xie, J., Szymanski, B.K.: Community detection using a neighborhood strength driven label propagation algorithm. In: IEEE NSW 2011, pp. 188–195 (2011)
Zhang, S., Wang, R.S., Zhang, X.S.: Identification of overlapping community structure in complex networks using fuzzy c-means clustering. Physica A 374, 483–490 (2007)
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Xie, J., Szymanski, B.K. (2012). Towards Linear Time Overlapping Community Detection in Social Networks. In: Tan, PN., Chawla, S., Ho, C.K., Bailey, J. (eds) Advances in Knowledge Discovery and Data Mining. PAKDD 2012. Lecture Notes in Computer Science(), vol 7302. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-30220-6_3
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DOI: https://doi.org/10.1007/978-3-642-30220-6_3
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