Detecting the evolving community structure in dynamic social networks

doi:10.1007/s11280-019-00710-z

CSpace > 应用数学研究所

	Detecting the evolving community structure in dynamic social networks
	Liu, Fanzhen 1; Wu, Jia 1; Xue, Shan 1,2; Zhou, Chuan 3; Yang, Jian 1; Sheng, Quanzheng 1
	2019-10-23
发表期刊	WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS
ISSN	1386-145X
页码	19
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.
关键词	Dynamic social networks Community structure Evolutionary clustering Migration operator
DOI	10.1007/s11280-019-00710-z
收录类别	SCI
语种	英语
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
WOS类目	Computer Science, Information Systems ; Computer Science, Software Engineering
WOS记录号	WOS:000492015100001
出版者	SPRINGER
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/50655
专题	应用数学研究所
通讯作者	Zhou, Chuan
作者单位	1.Macquarie Univ, Dept Comp, Sydney, NSW 2109, Australia 2.CSIRO, Data61, Sydney, NSW 2015, Australia 3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
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.