Abstract
The tremendous development of community detection in dynamic networks have been witnessed in recent years. In this paper, intimacy evolutionary clustering algorithm is proposed to detect community structure in dynamic networks. Firstly, the time weighted similarity matrix is utilized and calculated to grasp time variation during the community evolution. Secondly, the differential equations are adopted to learn the intimacy evolutionary behaviors. During the interactions, intimacy between two nodes would be updated based on the iteration model. Nodes with higher intimacy would gather into the same cluster and nodes with lower intimacy would get away, then the community structure would be formed in dynamic networks. The extensive experiments are conducted on both real-world and synthetic signed networks to show the efficiency of detection performance. Moreover, the presented method achieves better detection performance compared with several better algorithms in terms of detection accuracy.
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References
Chen J, Hong Z, Wang L, Wu L (2014) Dynamic evolutionary community detection algorithms based on the modularity matrix. Chinese Physica B 23(11):686–691
Chen J, Jiao L, Wu J, Wang X (2010) Projective synchronization with different scale factors in a driven-response complex network and its application in image encryption. Nonlinear Anal Real World Appl 11(4):3045–3058
Chen J, Hua W, Wang L, Liu W (2016) A dynamic evolutionary clustering perspective: community detection in signed networks by reconstructing neighbor sets. Physica A 447:482–492
Cheraghchi HS, Zakerolhosseini A (2017) Toward a novel art inspired incremental community mining algorithm in dynamic social network. Appl Intell 46(2):409–426
Doreian P, Mrvar A (1996) A partitioning approach to structural balance. Soc Netw 18(2):149–168
Fei H, Yau SS, Min G, Yang LT (2014) Detecting $k$-balanced trusted cliques in signed social networks. IEEE Internet Comput 18(2):24–31
Fei H, Park DS, Min G, Jeong YS, Park JH (2016) $k$-cliques mining in dynamic social networks based on triadic formal concept analysis. Neurocomputing 209(C):57–66
Fournet J, Barrat A (2014) Contact patterns among high school students. PLoS One 9(9):e107878
Girvan M, Newman MEJ (2002) Community structure in social and biological networks. Proc Natl Acad Sci USA 99(12):7821–7826
Greene D, Doyle D, Cunningham P (2010) Tracking the evolution of communities in dynamic social networks. In: International conference on advances in social networks analysis mining
Handa HOYS, Ishibuchi H (2015) Proceedings in adaptation learning and optimization. In: Proceedings of the 18th Asia Pacific symposium on intelligent and evolutionary systems, vol 1, p 713
Hu B, Hong W, Yu X, Yuan W, He T (2017) Sparse network embedding for community detection and sign prediction in signed social networks. J Ambient Intell Hum Comput 1:1–12
Jianshe W, Licheng J, Chao J, Fang L, Maoguo G, Ronghua S, Weisheng C (2012) Overlapping community detection via network dynamics. Phys Rev E 85(2):016115
Kernighan BW (2014) An efficient heuristic procedure for partitioning graphs. Bell Syst Tech J 2:291–307
Liu C, Liu J, Jiang Z (2014) A multiobjective evolutionary algorithm based on similarity for community detection from signed social networks. IEEE Trans Cybern 44(12):2274–2287
Lv L, Zhou T (2011) Link prediction in complex networks: a survey. Physica A 390(6):1150–1170
Newman MEJ (2004) Fast algorithm for detecting community structure in networks. Phys Rev E Stat Nonlinear Soft Matter Phys 69(6 Pt 2):066133
Newman ME, Girvan M (2004) Finding and evaluating community structure in networks. Phys Rev E 69(2):026113
Ng AY, Jordan MI, Weiss Y (2001) On spectral clustering: analysis and an algorithm. In: International conference on neural information processing systems: natural and synthetic, pp 849–856
Pizzuti C (2008) Ga-net: a genetic algorithm for community detection in social networks. In: International conference on parallel problem solving from nature: Ppsn X
Pothen A, Simon HD, Liou KP (1990) Partitioning sparse matrices with eigenvectors of graphs. SIAM J Matrix Anal Appl 11(3):430–452
Samie ME, Hamzeh A (2017) Change-aware community detection approach for dynamic social networks. Appl Intell 1:1–19
Sani N, Manthour MFF (2018) A multi-objective ant colony optimization algorithm for community detection in complex networks. J Ambient Intell Hum Comput 62:51–67
Sergio G, Pablo J, Alex A (2009) Analysis of community structure in networks of correlated data. Phys Rev E 80(2):016114
Shao JYQ, Han Z (2015) Community detection via local dynamic interaction. In: Proceedings of the 21th ACM SIGKDD international conference on knowledge discovery and data mining, pp 1075–1084
Silva F, Analide C (2016) Ubiquitous driving and community knowledge. J Ambient Intell Hum Comput 8(2):1–10
Wang Y, Cao J (2013) Cluster synchronization in nonlinearly coupled delayed networks of non-identical dynamic systems. Nonlinear Anal Real World Appl 14(1):842–851
Wang P, Lin G, Ma X (2017) Dynamic community detection based on network structural perturbation and topological similarity. J Stat Mech Theory Exp (1):013401
Wu F, Huberman BA (2004) Finding communities in linear time: a physics approach. Eur Phys J B 38(2):331–338
Wu J, Jiao Y (2014) Clustering dynamics of complex discrete-time networks and its application in community detection. Chaos 24(3):268–4
Wu L, Ying X, Wu X, Lu A, Zhou ZH (2012a) Examining spectral space of complex networks with positive and negative links. Int J Soc Netw Min 15(2):87–97
Wu L, Ying X, Wu X, Lu A, Zhou ZH (2012b) Examining spectral space of complex networks with positive and negative links. Int J Soc Netw Min 1(1):91
Wu Jianshe, Lu Rui, Jiao Licheng, Liu Fang, Yu Xin (2013) Phase transition model for community detection. Physica A 392(6):1287–1301
Wu J, Fang W, Peng X (2016a) Automatic network clustering via density-constrained optimization with grouping operator. Appl Soft Comput 38:606–616
Wu J, Long Z, Yong L, Yang J (2016b) Partition signed social networks via clustering dynamics. Physica A 443:568–582
Wu J, Long Z, Yong L, Yang J (2016c) Partition signed social networks via clustering dynamics. Physica A 443:568–582
Xin W, Song J, Kai L, Wang X (2017) Community detection in attributed networks based on heterogeneous vertex interactions. Appl Intell 47(3):1–12
Yang B, Cheung WK, Liu J (2007) Community mining from signed social networks. IEEE Trans Knowl Data Eng 19(10):1333–1348
Yang T, Chi Y, Zhu S, Gong Y, Jin R (2011) Detecting communities and their evolutions in dynamic social networks–a Bayesian approach. Mach Learn 82(2)
Acknowledgements
This research was supported through National Natural Science Foundation of China (Nos. 61503203, 71561020, 61702317, 61771297); Fundamental Research Funds for the Central Universities (Nos. GK201802013, GK201703059, GK201803062).
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Chen, J., Liu, D., Hao, F. et al. Community detection in dynamic signed network: an intimacy evolutionary clustering algorithm. J Ambient Intell Human Comput 11, 891–900 (2020). https://doi.org/10.1007/s12652-019-01215-3
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DOI: https://doi.org/10.1007/s12652-019-01215-3