Abstract
Community detection partitions users in social networks into sub-groups according to structural or behavioral similarities, which had been widely adopted by a lot of applications such as friend recommendation, precision marketing, etc. In this paper, we propose a location-interest-aware community detection approach for mobile social networks. Specifically, we develop a spatial-temporal topic model to describe users’ location interest, and introduce an auto encoder mechanism to represent users’ location features and social network features as low-dimensional vectors, based on which a community detection algorithm is applied to divide users into sub-graphs. We conduct extensive experiments based on a real-world mobile social network dataset, which demonstrate that the proposed community detection approach outperforms the baseline algorithms in a variety of performance metrics.
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References
Ahn, Y.Y., Bagrow, J.P., Lehmann, S.: Link communities reveal multiscale complexity in networks. Nature 466(7307), 761 (2010)
Blondel, V.D., Guillaume, J.L., Lambiotte, R., Lefebvre, E.: Fast unfolding of communities in large networks. J. Stat. Mech. Theory Exp. 2008(10), P10008 (2008)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1082–1090. ACM (2011)
Darling, W.M.: A theoretical and practical implementation tutorial on topic modeling and GIBBs sampling. In: Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies, pp. 642–647 (2011)
Duan, L., Xu, L., Guo, F., Lee, J., Yan, B.: A local-density based spatial clustering algorithm with noise. Inf. Syst. 32(7), 978–986 (2007)
He, D., Yang, X., Feng, Z., Chen, S., Fogelman-Soulié, F.: A network embedding-enhanced approach for generalized community detection. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11062, pp. 383–395. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99247-1_34
Jain, A.K.: Data clustering: 50 years beyond k-means. Pattern Recogn. Lett. 31(8), 651–666 (2010)
Jin, D., Liu, Z., He, D., Gabrys, B., Musial, K.: Robust detection of communities with multi-semantics in large attributed networks. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11061, pp. 362–376. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99365-2_32
Ma, H., Xie, M., Wei, J., He, T.: An overlapping microblog community detection method using new partition criterion. In: Liu, W., Giunchiglia, F., Yang, B. (eds.) KSEM 2018. LNCS (LNAI), vol. 11062, pp. 313–323. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-99247-1_28
Chen, M., Li, W., Qian, L., Lu, S., Chen, D.: Interest-aware next POI recommendation for mobile social networks. In: Chellappan, S., Cheng, W., Li, W. (eds.) WASA 2018. LNCS, vol. 10874, pp. 27–39. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-94268-1_3
Newman, M.E.: Fast algorithm for detecting community structure in networks. Phys. Rev. E 69(6), 066133 (2004)
Newman, M.E.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Ng, A.Y., Jordan, M.I., Weiss, Y.: On spectral clustering: analysis and an algorithm. In: Advances in Neural Information Processing Systems, pp. 849–856 (2002)
Raghavan, U.N., Albert, R., Kumara, S.: Near linear time algorithm to detect community structures in large-scale networks. Phys. Rev. E 76(3), 036106 (2007)
Acknowledgment
This work was partially supported by the National Key R&D Program of China (Grant No. 2018YFB1004704), the National Natural Science Foundation of China (Grant Nos. 61672278, 61832008, 61832005), the Key R&D Program of Jiangsu Province, China (Grant No. BE2018116), the science and technology project from State Grid Corporation of China (Contract No. SGSNXT00YJJS1800031), the Collaborative Innovation Center of Novel Software Technology and Industrialization, and the Sino-German Institutes of Social Computing.
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Chen, M., Li, W., Lu, S., Chen, D. (2019). Location-Interest-Aware Community Detection for Mobile Social Networks Based on Auto Encoder. In: Douligeris, C., Karagiannis, D., Apostolou, D. (eds) Knowledge Science, Engineering and Management. KSEM 2019. Lecture Notes in Computer Science(), vol 11775. Springer, Cham. https://doi.org/10.1007/978-3-030-29551-6_16
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DOI: https://doi.org/10.1007/978-3-030-29551-6_16
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