Abstract
Discovering meaningful communities based on the interactions of different people in online social networks (OSNs) is an active research topic in recent years. However, existing interaction based community detection techniques either rely on the content analysis or only consider underlying structure of the social network graph, while identifying communities in OSNs. As a result, these approaches fail to identify active communities, i.e., communities based on actual interactions rather than mere friendship. To alleviate the limitations of existing approaches, we propose a novel solution of community detection in OSNs. The key idea of our approach comes from the following observations: (i) the degree of interaction between each pair of users can widely vary, which we term as the strength of ties, and (ii) for each pair of users, the interactions with mutual friends, which we term the group behavior, play an important role to determine their belongingness to the same community. Based on these two observations, we propose an efficient solution to detect communities in OSNs. The detailed experimental study shows that our proposed algorithm significantly outperforms state-of-the-art techniques for both real and synthetic datasets
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
Aggarwal, C.C., Wang, H. (eds.): Managing and Mining Graph Data. Advances in Database Systems, vol. 40. Springer (2010)
Abello, J., Resende, M.G.C., Sudarsky, S.: Massive quasi-clique detection. In: Rajsbaum, S. (ed.) LATIN 2002. LNCS, vol. 2286, pp. 598–612. Springer, Heidelberg (2002)
Newman, M.E.J.: Detecting community structure in networks. The European Physical Journal B - Condensed Matter and Complex Systems 38 (2004)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69 (2004)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proceedings of the National Academy of Sciences 99(12), 7821–7826 (2002)
Zhou, Y., Cheng, H., Yu, J.X.: Graph clustering based on structural/attribute similarities. Proc. VLDB Endow. 2(1), 718–729 (2009)
Zhou, W., Jin, H., Liu, Y.: Community discovery and profiling with social messages. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 388–396 (2012)
Sachan, M., Contractor, D., Faruquie, T.A., Subramaniam, L.V.: Using content and interactions for discovering communities in social networks. In: Proceedings of the 21st International Conference on World Wide Web, pp. 331–340 (2012)
Qi, G.J., Aggarwal, C.C., Huang, T.S.: Community detection with edge content in social media networks. In: ICDE, pp. 534–545 (2012)
Pujol, J.M., Erramilli, V., Siganos, G., Yang, X., Laoutaris, N., Chhabra, P., Rodriguez, P.: The little engine(s) that could: Scaling online social networks. IEEE/ACM Trans. Netw. 20(4), 1162–1175 (2012)
Rothschild, J.: High performance at massive scale - lessons learned at facebook, http://cns.ucsd.edu/lecturearchive09.shtml
Zeng, Z., Wang, J., Zhou, L., Karypis, G.: Out-of-core coherent closed quasi-clique mining from large dense graph databases. ACM Trans. Database Syst. 32(2) (June 2007)
Pei, J., Jiang, D., Zhang, A.: On mining cross-graph quasi-cliques. In: Proceedings of the 11th ACM SIGKDD International Conference on Knowledge Discovery in Data Mining (2005)
Xu, X., Yuruk, N., Feng, Z., Schweiger, T.A.J.: Scan: A structural clustering algorithm for networks. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 824–833 (2007)
Cohen, J.: Trusses: Cohesive subgraphs for social network analysis (2008)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks (September 2007)
Clauset, A., Newman, M.E.J., Moore, C.: Finding community structure in very large networks 70(6) (December 2004)
Meo, P.D., Ferrara, E., Fiumara, G., Provetti, A.: Generalized louvain method for community detection in large networks. In: ISDA, pp. 88–93. IEEE (2011)
Yang, T., Jin, R., Chi, Y., Zhu, S.: Combining link and content for community detection: A discriminative approach. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 927–936 (2009)
Mllner, D.: http://math.stanford.edu/muellner/fastcluster.html
Pons, P., Latapy, M.: Computing communities in large networks using random walks. J. of Graph Alg. and App. bf 10, 284–293 (2004)
Newman, M.E.J.: Finding community structure in networks using the eigenvectors of matrices. Phys. Rev. E 036104
Rosvall, M., Bergstrom, C.T.: Maps of random walks on complex networks reveal community structure. Proc. Natl. Acad. Sci., 1118 (2008)
Blondel, V., Guillaume, J., Lambiotte, R., Mech, E.: Fast unfolding of communities in large networks. J. Stat. Mech., P10008 (2008)
Opsahl, T.: Triadic closure in two-mode networks: Redefining the global and local clustering coefficients. Social Networks (August 2011)
SNAP: Stanford network analysis project
Zachary, W.: An information flow model for conflict and fission in small groups. Journal of Anthropological Research 33, 452–473 (1977)
Lusseau, D.: The emergent properties of a dolphin social network. Proceedings of the Royal Society of London. Series B: Biological Sciences 270(suppl. 2) (November 2003)
Gleiser, P.M., Danon, L.: Community structure in jazz. Advances in Complex Systems 6(4), 565–574 (2003)
Lancichinetti, A., Fortunato, S., Radicchi, F.: Benchmark graphs for testing community detection algorithms. Phys. Rev. E 78(4), 46110 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Dev, H., Ali, M.E., Hashem, T. (2014). User Interaction Based Community Detection in Online Social Networks. In: Bhowmick, S.S., Dyreson, C.E., Jensen, C.S., Lee, M.L., Muliantara, A., Thalheim, B. (eds) Database Systems for Advanced Applications. DASFAA 2014. Lecture Notes in Computer Science, vol 8422. Springer, Cham. https://doi.org/10.1007/978-3-319-05813-9_20
Download citation
DOI: https://doi.org/10.1007/978-3-319-05813-9_20
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-05812-2
Online ISBN: 978-3-319-05813-9
eBook Packages: Computer ScienceComputer Science (R0)