Abstract
Most real-world social networks are usually dynamic (evolve over time), thus communities are constantly changing in memberships. In this paper, an incremental approach based on the coalition formation game theory to identify communities in dynamic social networks is proposed, where the community evolution is modeled as the problem of transformations of stable coalition structures. The proposed approach adaptively update communities from the previous known structures and the changes of topological structure of a network, rather than re-computing in the snapshots of the network at different time steps, such that the computational cost and processing time can be significantly reduced. Experiments have been conducted to evaluate the effectiveness of the proposed approach.
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Alvari, H., Hajibagheri, A., Sukthankar, G.: Community detection in dynamic social networks: a game-theoretic approach. In: ASONAM, China, 17–20 August 2014, pp. 101–107 (2014)
Apt, K.R., Witzel, A.: A generic approach to coalition formation. Int. Game Theory Rev. 11(03), 347–367 (2009)
Chen, W., Liu, Z., Sun, X., Wang, Y.: A game-theoretic framework to identify overlapping communities in social networks. Data Mining Knowl. Discov. 21(2), 224–240 (2010)
Chong, W.H., Teow, L.N.: An incremental batch technique for community detection. In: FUSION, Istanbul, Turkey, 9 July–12 July 2013, pp. 750–757 (2013)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech: Theory Exp. 2008, P10008 (2008)
Danon, L., Díaz-Guilera, D.A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech: Theory Exp. 2005(09), P09008 (2005)
Du, N., Jia, X., Gao, J., Gopalakrishnan, V., Zhang, A.: Tracking temporal community strength in dynamic networks. IEEE Trans. Knowl. Data Eng. 27(11), 3125–3137 (2015)
Dinh, T.N., Nguyen, N.P., Alim, M.A., Thai, M.T.: A near-optimal adaptive algorithm for maximizing modularity in dynamic scale-free networks. J. Comb. Optim. 30(3), 747–767 (2015)
Ferreira, L.N., Zhao, L.: Time series clustering via community detection in networks. Inf. Sci. 326(1), 227–242 (2016)
Fortunato, S.: Community detection in graphs. Phys. Rep. 486, 75–174 (2010)
Hecking, T., Steinert, L., Gohnert, T., Hoppe, H.U.: Incremental clustering of dynamic bipartite networks. In: ENIC, Wroclaw, Poland, 29–30 September 2014, pp. 9–16 (2014)
Lee, M., Choi, S., Chung, C.W.: Efficient algorithms for updating betweenness centrality in fully dynamic graphs. Inf. Sci. 326(2016), 278–296 (2016)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Phys. Rev. E 69, 026113 (2004)
Nguyen, N.P., Dinh, T.N., Xuan, Y., Thai, M.T.: Adaptive algorithms for detecting community structure in dynamic social networks. In: INFOCOM, Shanghai, China, 10–15 April 2011, pp. 2282–2290 (2011)
Saad, W., Han, Z., Debbah, M., Hjørungnes, A., Basar, T.: Coalitional game theory for communication networks: a tutorial. IEEE Sig. Process. Mag. 26(5), 77–97 (2009)
Sarason, S.B.: The Psychological Sense of Community: Prospects for a Community Psychology. Jossey-Bass, San Francisco (1974)
Shang, J., Liu, L., Li, X., Xie, F., Wu, C.: Targeted revision: a learning-based approach for incremental community detection in dynamic networks. Phys. A: Stat. Mech. Appl. 443(2016), 70–85 (2016)
Sun, J., Faloutsos, C., Papadimitriou, S., Yu, P.S.: Graphscope: parameter-free mining of large time-evolving graphs. In: SIGKDD, San Jose, USA, 11–14 August 2007, pp. 687–696 (2007)
Wu, P., Li, S.K.: Social network analysis layout algorithm under ontology model. J. Softw. 6(7), 1321–1328 (2011)
Zacharias, G.L., MacMillan, J., Hemel, S.B.V. (eds.): Behavioral Modeling and Simulation: From Individuals to Societies. National Academies Press, Washington, DC (2008)
Zhou, L., Lü, K., Yang, P., Wang, L., Kong, B.: An approach for overlapping and hierarchical community detection in social networks based on coalition formation game theory. Expert Syst. Appl. 42(24), 9634–9646 (2015)
Acknowledgement
This research was supported by the National Natural Science Foundation of China (61762090, 61262069, 61472346, and 61662086), The Natural Science Foundation of Yunnan Province (2016FA026, 2015FB114), the Project of Innovative Research Team of Yunnan Province, and Program for Innovation Research Team (in Science and Technology) in University of Yunnan Province (IRTSTYN).
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Xiao, Q., Yang, P., Zhou, L., Wang, L. (2018). An Incremental Approach Based on the Coalition Formation Game Theory for Identifying Communities in Dynamic Social Networks. In: Liu, W., Giunchiglia, F., Yang, B. (eds) Knowledge Science, Engineering and Management. KSEM 2018. Lecture Notes in Computer Science(), vol 11061. Springer, Cham. https://doi.org/10.1007/978-3-319-99365-2_35
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DOI: https://doi.org/10.1007/978-3-319-99365-2_35
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