Detecting the evolving community structure in dynamic social networks

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.

[1]  Alex Alves Freitas,et al.  A Survey of Evolutionary Algorithms for Clustering , 2009, IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews).

[2]  Maoguo Gong,et al.  Community Detection in Dynamic Social Networks Based on Multiobjective Immune Algorithm , 2012, Journal of Computer Science and Technology.

[3]  Maoguo Gong,et al.  Decomposition-Based Multiobjective Evolutionary Algorithm for Community Detection in Dynamic Social Networks , 2014, TheScientificWorldJournal.

[4]  Chao Gao,et al.  A community clustering algorithm based on genetic algorithm with novel coding scheme , 2014, 2014 10th International Conference on Natural Computation (ICNC).

[5]  Qingfu Zhang,et al.  MOEA/D: A Multiobjective Evolutionary Algorithm Based on Decomposition , 2007, IEEE Transactions on Evolutionary Computation.

[6]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[7]  Yangyang Li,et al.  An improved memetic algorithm for community detection in complex networks , 2012, 2012 IEEE Congress on Evolutionary Computation.

[8]  Bara'a Ali Attea,et al.  Improving the performance of evolutionary multi-objective co-clustering models for community detection in complex social networks , 2016, Swarm Evol. Comput..

[9]  M E J Newman,et al.  Community structure in social and biological networks , 2001, Proceedings of the National Academy of Sciences of the United States of America.

[10]  Chen Liu,et al.  A hybrid evolutionary algorithm for community detection , 2017, WI.

[11]  Erik M Bollt,et al.  Local method for detecting communities. , 2005, Physical review. E, Statistical, nonlinear, and soft matter physics.

[12]  Yun Chi,et al.  Evolutionary spectral clustering by incorporating temporal smoothness , 2007, KDD '07.

[13]  Lynda L. McGhie,et al.  World Wide Web , 2011, Encyclopedia of Information Assurance.

[14]  Derek Greene,et al.  Tracking the Evolution of Communities in Dynamic Social Networks , 2010, 2010 International Conference on Advances in Social Networks Analysis and Mining.

[15]  Jiawei Han,et al.  A Particle-and-Density Based Evolutionary Clustering Method for Dynamic Networks , 2009, Proc. VLDB Endow..

[16]  M E J Newman,et al.  Modularity and community structure in networks. , 2006, Proceedings of the National Academy of Sciences of the United States of America.

[17]  Jingchun Chen,et al.  Detecting functional modules in the yeast protein-protein interaction network , 2006, Bioinform..

[18]  Francesco Folino,et al.  An Evolutionary Multiobjective Approach for Community Discovery in Dynamic Networks , 2014, IEEE Transactions on Knowledge and Data Engineering.

[19]  Hans-Peter Kriegel,et al.  Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering , 2009, TKDD.

[20]  Mingxin Liang,et al.  A bio-inspired genetic algorithm for community mining , 2016, 2016 12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD).

[21]  Nicholas Jing Yuan,et al.  Online Discovery of Gathering Patterns over Trajectories , 2014, IEEE Transactions on Knowledge and Data Engineering.

[22]  Emanuel Falkenauer,et al.  Genetic Algorithms and Grouping Problems , 1998 .

[23]  Haluk Bingol,et al.  Community Detection in Complex Networks Using Genetic Algorithms , 2006, 0711.0491.

[24]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Deepayan Chakrabarti,et al.  Evolutionary clustering , 2006, KDD '06.

[26]  Yun Chi,et al.  Analyzing communities and their evolutions in dynamic social networks , 2009, TKDD.

[27]  Bin Wu,et al.  Group CRM: a new telecom CRM framework from social network perspective , 2009, CIKM-CNIKM.

[28]  Jian Yang,et al.  Evolutionary Community Detection in Dynamic Social Networks , 2019, 2019 International Joint Conference on Neural Networks (IJCNN).

[29]  Leon Danon,et al.  Comparing community structure identification , 2005, cond-mat/0505245.