Abstract
Community detection is a very important task in social network analysis. Most existing community detection algorithms are designed for single-relational networks. However, in the real world, social networks are mostly multi-relational. In this paper, we propose a coalition formation game theory-based approach to detect communities in multi-relational social networks. We define the multi-relational communities as the shared communities over multiple single-relational graphs, and model community detection as a coalition formation game process in which actors in a social network are modeled as rational players trying to improve group’s utilities by cooperating with other players to form coalitions. Each player is allowed to join multiple coalitions and coalitions with fewer players can merge into a larger coalition as long as the merge operation could improve the utilities of coalitions merged. We then use a greedy agglomerative manner to identify communities. Experimental results and performance studies verify the effectiveness of our approach.
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References
Francesco, F., Clara, P.: An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Transactions on Knowledge and Data Engineering 26(8), 1838–1852 (2014)
Zhou, L., Lü, K.: Detecting communities with different sizes for social network analysis. The Computer Journal (2014). doi:10.1093/comjnl/bxu087
Xin, Y., Yang, J., Xie, Z.Q.: A semantic overlapping community detection algorithm based on field sampling. Expert Systems with Applications 42, 366–375 (2015)
Yuan, W., Guan, D., Lee, Y.-K., Lee, S., Hur, S.J.: Improved trust-aware recommender system using small-worldness of trust networks. Knowledge-Based Systems 23(3), 232–238 (2010)
Wu, P., Li, S.K.: Social network analysis layout algorithm under ontology model. Journal of Software 6(7), 1321–1328 (2011)
Wang, D., Lin, Y.-R., Bagrow, J.P.: Social networks in emergency response. In: Alhajj, R., Rokne, J. (eds.) Encyclopedia of Social Network Analysis and Mining, vol. 1, pp. 1904–1914 (2014)
Li, G.P., Pan, Z.S., Xiao, B., Huang, L.W.: Community discovery and importance analysis in social network. Intelligent Data Analysis 18(3), 495–510 (2014)
Ströele, V., Zimbrão, G., Souza, J.M.: Group and link analysis of multi-relational scientific social networks. Journal of Systems and Software 86(7), 1819–1830 (2013)
Cai, D., Shao, Z., He, X., Yan, X., Han, J.: Community mining from multi-relational networks. In: Jorge, A.M., Torgo, L., Brazdil, P.B., Camacho, R., Gama, J. (eds.) PKDD 2005. LNCS (LNAI), vol. 3721, pp. 445–452. Springer, Heidelberg (2005)
Rodriguez, M., Shinavier, J.: Exposing multi-relational networks to single relational network analysis algorithms. Journal of Informetrics 4(1), 29–41 (2010)
Szell, M., Lambiotte, R., Thurner, S.: Multirelational organization of large-scale social networks in an online world. Proceedings of the National Academy of Sciences of the United States of America 107(31), 13636–13641 (2010)
Saad, W., Han, Z., Debbah, M., Hjørungnes, A., Basar, T.: Coalitional game theory for communication networks: a tutorial. IEEE Signal Processing Magazine 26(5), 77–97 (2009)
Zacharias, G.L., MacMillan, J., Hemel, S.B.V. (eds.): Behavioral modeling and simulation: from individuals to societies. The National Academies Press, Washington, DC (2008)
Sarason, S.B.: The Psychological Sense of Community: Prospects for a Community Psychology. Jossey-Bass, San Francisco (1974)
Chen, W., Liu, Z., Sun, X., Wang, Y.: A Game-theoretic framework to identify overlapping communities in social networks. Data Mining and Knowledge Discovery 21(2), 224–240 (2010)
Fortunato, S.: Community detection in graphs. Physics Reports 486, 75–174 (2010)
Newman, M.E.J., Girvan, M.: Finding and evaluating community structure in networks. Physical Review E 69, 026113 (2004)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. Journal of Statistical Mechanics: Theory and Experiment 10, P10008 (2008)
Aynaud, T., Guillaume J.-L.: Multi-step community detection and hierarchical time segmentation in evolving networks. In: Proceedings of the fifth SNA-KDD Workshop on Social Network Mining and Analysis, in conjunction with the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2011), San Diego, CA, pp. 21–24, August 2011
Wu, Z., Yin, W., Cao, J., Xu, G., Cuzzocrea, A.: Community detection in multi-relational social networks. In: Lin, X., Manolopoulos, Y., Srivastava, D., Huang, G. (eds.) WISE 2013, Part II. LNCS, vol. 8181, pp. 43–56. Springer, Heidelberg (2013)
Tang, L., Wang, X., Liu, H.: Community detection via heterogeneous interaction analysis. Data Mining Knowledge Discovery 25(1), 1–33 (2012)
Lin, Y.-R., Choudhury, M.D., Sundaram, H., Kelliher, A.: Discovering Multi-Relational Structure in Social Media Streams. ACM Transactions on Multimedia Computing, Communications and Applications 8(1), 1–28 (2012)
Zhang, Z., Li, Q., Zeng, D., Gao, H.: User community discovery from multi-relational networks. Decision Support Systems 54(2), 870–879 (2013)
Li, X.T., Ng, M.K., Ye, Y.M.: MultiComm: finding community structure in multi-dimensional networks. IEEE Transactions on Knowledge and Data Engineering 26(4), 929–941 (2014)
Nash, J.F.: Non-cooperative games. Annals of Mathematics 54(2), 286–295 (1951)
Zlotkin, G., Rosenschein J.: Coalition cryptography and stability mechanisms for coalition formation in task oriented domains. In: Proceedings of The Twelfth National Conference on Artificial Intelligence, Seattle, Washington, August 1–4, pp. 432–437. The AAAI Press, Menlo Park (1994)
Alvari, H., Hashemi, S., Hamzeh, A.: Detecting overlapping communities in social networks by game theory and structural equivalence concept. In: Deng, H., Miao, D., Lei, J., Wang, F.L. (eds.) AICI 2011, Part II. LNCS, vol. 7003, pp. 620–630. Springer, Heidelberg (2011)
Lung, R.I., Gog, A., Chira, C.: A game theoretic approach to community detection in social networks. In: Pelta, D.A., Krasnogor, N., Dumitrescu, D., Chira, C., Lung, R. (eds.) NICSO 2011. SCI, vol. 387, pp. 121–131. Springer, Heidelberg (2011)
Hajibagheri, A., Alvari, H., Hamzeh, A., Hashemi, A.: Social networks community detection using the shapley value. In: 16th CSI International Symposium on Artificial Intelligence and Signal Processing (AISwww.lw20.comP), Shiraz, Iran, May 2–3, pp. 222–227 (2012)
Zhou, L., Cheng, C., Lü, K., Chen, H.: Using coalitional games to detect communities in social networks. In: Wang, J., Xiong, H., Ishikawa, Y., Xu, J., Zhou, J. (eds.) WAIM 2013. LNCS, vol. 7923, pp. 326–331. Springer, Heidelberg (2013)
Danon, L.: Danony, Díaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. Journal of Statistical Mechanics: Theory and Experiment 9, P09008 (2005)
Lancichinetti, A., Fortunato, S.: Benchmarks for testing community detection algorithms on directed and weighted graphs with overlapping communities. Physical Review E 80(1), 16118 (2009)
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Zhou, L., Yang, P., Lü, K., Zhang, Z., Chen, H. (2015). A Coalition Formation Game Theory-Based Approach for Detecting Communities in Multi-relational Networks. In: Dong, X., Yu, X., Li, J., Sun, Y. (eds) Web-Age Information Management. WAIM 2015. Lecture Notes in Computer Science(), vol 9098. Springer, Cham. https://doi.org/10.1007/978-3-319-21042-1_3
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DOI: https://doi.org/10.1007/978-3-319-21042-1_3
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