Abstract
Discovering the hidden community structure is a fundamental problem in network and graph analysis. Several approaches have been proposed to solve this challenging problem. Among them, detecting overlapping communities in a network is a usual way towards understanding the features of networks. In this paper, we propose a novel approach to identify overlapping communities in large complex networks. It makes an original use of a new community model, called k-clique-star, to discover densely connected structures in social interactions. We show that such model allows to ensure a minimum density on the discovered communities and overcomes some weaknesses of existing cohesive structures. Experimental results demonstrate the effectiveness and efficiency of our overlapping community model in a variety of real graphs.
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References
Adamcsek, B., Palla, G., Farkas, I.J., Derényi, I., Vicsek, T.: CFinder: locating cliques and overlapping modules in biological networks. Bioinformatics 22(8), 1021–1023 (2006)
Alba, R.D.: A graph-theoretic definition of a sociometric clique 3, 113–126 (1973)
Chakraborty, T., Srinivasan, S., Ganguly, N., Mukherjee, A., Bhowmick, S.: On the permanence of vertices in network communities. In: SIGKDD, pp. 1396–1405 (2014)
Cheng, J., Leng, M., Li, L., Zhou, H., Chen, X.: Active semi-supervised community detection based on must-link and cannot-link constraints. PLoS 9(10), 1–18 (2014)
Cohen, J.: Trusses: cohesive subgraphs for social network analysis. In: Technical report, National Security Agency (2008)
Danisch, M., Balalau, O.D., Sozio, M.: Listing k-cliques in sparse real-world graphs. In: WWW, pp. 589–598 (2018)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99, 7821 (2002)
Huang, X., Cheng, H., Qin, L., Tian, W., Yu, J.X.: Querying k-truss community in large and dynamic graphs. In: SIGMOD, pp. 1311–1322 (2014)
Lancichinetti, A., Fortunato, S., Kertesz, J.: Community detection algorithms: a comparative analysis. New J. Phys. 11 (2009)
Leskovec, J., Krevl, A.: SNAP datasets: stanford large network dataset collection (2014). http://snap.stanford.edu/data
Luce, R.D.: Connectivity and generalized cliques in sociometric group structure. Psychometrika 15(2), 169–190 (1950)
Luce, R.D., Perry, A.D.: A method of matrix analysis of group structure. Psychometrika 14(2), 95–116 (1949)
Lusseau, D., Schneider, K., Boisseau, O., Haase, P., Slooten, E., Dawson, S.: The bottlenose dolphin community of doubtful Sound features a large proportion of long-lasting associations. Behav. Ecol. Sociobiol. 54(4), 396–405 (2003)
McDaid, A.F., Hurley, N.J.: Detecting highly overlapping communities with model-based overlapping seed expansion. In: ASONAM, pp. 112–119 (2010)
Saito, K., Yamada, T., Kazama, K.: Extracting communities from complex networks by the k-dense method. IEICE Trans. 91(A11), 3304–3311 (2008)
Seidman, S.B.: A graph-theoretic generalization of the clique concep. J. Math. Sociol. 5(3), 139–154 (1978)
Seidman, S.B.: Network structure and minimum degree. Soc. Netw. 5(3), 269–287 (1983)
Shao, Y., Chen, L., Cui, B.: Efficient cohesive subgraphs detection in parallel. In: SIGMOD, pp. 613–624 (2014)
Wang, J., Cheng, J.: Truss decomposition in massive networks. PVLDB 5(9), 812–823 (2012)
Wang, N., Zhang, J., Tan, K., Tung, A.K.H.: On triangulation-based dense neighborhood graphs discovery. PVLDB 4(2), 58–68 (2010)
Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge University Press, Cambridge (1994)
Watts, D.J., Strogatz, S.H.: Collective dynamics of small-world networks. Nature 393(6684), 440–442 (1998)
Zachary, W.W.: An information flow model for conflict and fission in small groups. J. Anthropol. Res. 33, 452–473 (1977)
Xie, J., Kelley, S., Szymanski, B.K.: Overlapping community detection in networks: the state-of-the-art and comparative study. ACM Comput. Surv. 45(4), 43:1–43:35 (2013)
Yang, J., Leskovec, J.: Overlapping community detection at scale: a nonnegative matrix factorization approach. In: WSDM, pp. 587–596 (2013)
Yang, J., McAuley, J.J., Leskovec, J.: Community detection in networks with node attributes. CoRR abs/1401.7267 (2014)
Zhang, Y., Parthasarathy, S.: Extracting analyzing and visualizing triangle k-core motifs within networks. In: ICDE, pp. 1049–1060 (2012)
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Jabbour, S., Mhadhbi, N., Raddaoui, B., Sais, L. (2018). Pushing the Envelope in Overlapping Communities Detection. In: Duivesteijn, W., Siebes, A., Ukkonen, A. (eds) Advances in Intelligent Data Analysis XVII. IDA 2018. Lecture Notes in Computer Science(), vol 11191. Springer, Cham. https://doi.org/10.1007/978-3-030-01768-2_13
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