Abstract
Identifying the evolving community structure of social networks has recently drawn increasing attention. Evolutionary clustering, previously proposed to detect the evolution of clusters over time, presents a temporal smoothness framework to simultaneously maximize clustering accuracy and minimize the clustering drift between two successive time steps. Under this framework, evolving patterns of communities in dynamic networks were detected by finding the best trade-off between the clustering accuracy and temporal smoothness. However, two main drawbacks in previous methods limit the effectiveness of dynamic community detection. One is that the classic operators implemented by existing methods cannot avoid that a node is often inter-connected to most of its neighbors. The other is that those methods take it for granted that an inter-connection cannot exist between nodes clustered into the same community, which results in a limited search space. In this paper, we propose a novel multi-objective evolutionary clustering algorithm called DECS, to detect the evolving community structure in dynamic social networks. Specifically, we develop a migration operator cooperating with efficient operators to ensure that nodes and their most neighbors are grouped together, and use a genome matrix encoding the structure information of networks to expand the search space. DECS calculates the modularity based on the genome matrix as one of objectives to optimize. Experimental results on synthetic networks and real-world social networks demonstrate that DECS outperforms in both clustering accuracy and smoothness, contrasted with other state-of-the-art methods.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Attea, B.A., Hariz, W.A., Abdulhalim, M.F.: Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks. Swarm Evol. Comput. 26, 137–156 (2016)
Bagrow, J.P., Bollt, E.M.: Local method for detecting communities. Phys. Rev. E 72, 046108 (2005)
Chakrabarti, D., Kumar, R., Tomkins, A.: Evolutionary clustering. In: Proceedings of the 12th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 554–560 (2006)
Chen, J., Yuan, B.: Detecting functional modules in the yeast protein–protein interaction network. Bioinformatics 22(18), 2283–2290 (2006)
Chi, Y., Song, X., Zhou, D., Hino, K., Tseng, B.L.: Evolutionary spectral clustering by incorporating temporal smoothness. In: Proceedings of the 13th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 153–162 (2007)
Danon, L., Díaz-Guilera, A., Duch, J., Arenas, A.: Comparing community structure identification. J. Stat. Mech: Theory Exp. 2005(09), P09008 (2005)
Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)
Falkenauer, E.: Genetic Algorithms and Grouping Problems. Wiley, New York (1998)
Folino, F., Pizzuti, C.: An evolutionary multiobjective approach for community discovery in dynamic networks. IEEE Trans. Knowl. Data Eng. 26(8), 1838–1852 (2014)
Girvan, M., Newman, M.E.J.: Community structure in social and biological networks. Proc. Natl. Acad. Sci. 99(12), 7821–7826 (2002)
Gong, M., Cai, Q., Li, Y., Ma, J.: An improved memetic algorithm for community detection in complex networks. In: 2012 IEEE Congress on Evolutionary Computation, pp 1–8 (2012)
Gong, M.G., Zhang, L.J., Ma, J.J., Jiao, L.C.: Community detection in dynamic social networks based on multiobjective immune algorithm. J. Comput. Sci. Technol. 27(3), 455–467 (2012)
Greene, D., Doyle, D., Cunningham, P.: Tracking the evolution of communities in dynamic social networks. In: 2010 International Conference on Advances in Social Networks Analysis and Mining, pp 176–183 (2010)
Hruschka, E.R., Campello, R.J.G.B., Freitas, A.A., de Carvalho, A.C.P.L.F.: A survey of evolutionary algorithms for clustering. IEEE Trans. Syst. Man Cybern. Part C Appl. Rev. 39(2), 133–155 (2009)
Kim, M.S., Han, J.: A particle-and-density based evolutionary clustering method for dynamic networks. Proc. VLDB Endow. 2(1), 622–633 (2009)
Li, X., Gao, C., Pu, R.: A community clustering algorithm based on genetic algorithm with novel coding scheme. In: 2014 10Th International Conference on Natural Computation, pp 486–491 (2014)
Lin, Y.R., Chi, Y., Zhu, S., Sundaram, H., Tseng, B.L.: Analyzing communities and their evolutions in dynamic social networks. ACM Trans. Knowl. Discov. Data 3(2) (2009)
Liu, F., Chen, Z., Cui, Y., Liu, C., Li, X., Gao, C.: A hybrid evolutionary algorithm for community detection. In: Proceedings of the International Conference on Web Intelligence, pp. 469–475 (2017)
Liu, F., Wu, J., Zhou, C., Yang, J.: Evolutionary community detection in dynamic social networks. In: 2019 International Joint Conference on Neural Networks, pp. 1–7 (2019)
Lu, Y., Liang, M., Gao, C., Liu, Y., Li, X.: A bio-inspired genetic algorithm for community mining. In: 2016 12Th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, pp 673–679 (2016)
Ma, J., Liu, J., Ma, W., Gong, M., Jiao, L.: Decomposition-based multiobjective evolutionary algorithm for community detection in dynamic social networks. Sci. World J. 2014(3), 402345 (2014)
Newman, M.E.J.: The structure of scientific collaboration networks. Proc. Natl. Acad. Sci. 98(2), 404–409 (2001)
Newman, M.E.J.: Modularity and community structure in networks. Proc. Natl. Acad. Sci. 103(23), 8577–8582 (2006)
Tasgin, M., Herdagdelen, A., Bingol, H.: Community detection in complex networks using genetic algorithms. arXiv:0711.0491 (2007)
Wu, B., Ye, Q., Yang, S., Wang, B.: Group CRM: a new telecom CRM framework from social network perspective. In: Proceedings of the 1st ACM International Workshop on Complex Networks Meet Information & Knowledge Management, pp. 3–10 (2009)
Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)
Zheng, K., Zheng, Y., Yuan, N.J., Shang, S., Zhou, X.: Online discovery of gathering patterns over trajectories. IEEE Trans. Knowl. Data Eng. 26(8), 1974–1988 (2014)
Acknowledgments
This work was supported by the National Key Research and Development Program of China (Grant No. 2016YFB0801003), MQNS (Grant No. 9201701203), MQEPS (Grant No. 9201701455), MQRSG (Grant No. 95109718), the National Natural Science Foundation of China (Grant No. 61702355 and No. 61872360), the Youth Innovation Promotion Association CAS (Grant No. 2017210), and the 2018 Collaborative Research Project (Macquarie University and CSIRO’s Data61).
Author information
Authors and Affiliations
Corresponding author
Additional information
Publisher’s note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
This article belongs to the Topical Collection: Special Issue on Graph Data Management in Online Social Networks
Guest Editors: Kai Zheng, Guanfeng Liu, Mehmet A. Orgun, and Junping Du
Rights and permissions
About this article
Cite this article
Liu, F., Wu, J., Xue, S. et al. Detecting the evolving community structure in dynamic social networks. World Wide Web 23, 715–733 (2020). https://doi.org/10.1007/s11280-019-00710-z
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s11280-019-00710-z