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Detecting the evolving community structure in dynamic social networks

文献类型：期刊论文

作者Liu, Fanzhen3; Wu, Jia3; Xue, Shan2,3; Zhou, Chuan1; Yang, Jian3; Sheng, Quanzheng3
刊名WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
出版日期2019-10-23
页码19
ISSN号1386-145X
关键词Dynamic social networks Community structure Evolutionary clustering Migration operator
DOI10.1007/s11280-019-00710-z
英文摘要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.
资助项目National Key Research and Development Program of China[2016YFB0801003] ; MQNS[9201701203] ; MQEPS[9201701455] ; MQRSG[95109718] ; National Natural Science Foundation of China[61702355] ; National Natural Science Foundation of China[61872360] ; Youth Innovation Promotion Association CAS[2017210] ; Macquarie University ; CSIRO's Data61
WOS研究方向Computer Science
语种英语
出版者SPRINGER
WOS记录号WOS:000492015100001
源URL[http://ir.amss.ac.cn/handle/2S8OKBNM/50654]  
专题应用数学研究所
通讯作者Zhou, Chuan
作者单位1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
2.CSIRO, Data61, Sydney, NSW 2015, Australia
3.Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia
推荐引用方式
GB/T 7714		 Liu, Fanzhen,Wu, Jia,Xue, Shan,et al. Detecting the evolving community structure in dynamic social networks[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2019:19.
APA Liu, Fanzhen,Wu, Jia,Xue, Shan,Zhou, Chuan,Yang, Jian,&Sheng, Quanzheng.(2019).Detecting the evolving community structure in dynamic social networks.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,19.
MLA Liu, Fanzhen,et al."Detecting the evolving community structure in dynamic social networks".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2019):19.

入库方式： OAI收割

来源：数学与系统科学研究院

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