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dc.contributor.authorLi, Weimin
dc.contributor.authorZhu, Heng
dc.contributor.authorLi, Shaohua
dc.contributor.authorWang, Hao
dc.contributor.authorDai, Hongning
dc.contributor.authorWang, Can
dc.contributor.authorJin, Qun
dc.date.accessioned2021-04-29T03:19:03Z
dc.date.available2021-04-29T03:19:03Z
dc.date.issued2021
dc.identifier.issn0957-4174en_US
dc.identifier.doi10.1016/j.eswa.2020.114536en_US
dc.identifier.urihttp://hdl.handle.net/10072/404014
dc.description.abstractTraditional social community discovery methods concentrate mainly on static social networks, but the analysis of dynamic networks is a prerequisite for real-time and personalized social services. Through the study of community changes, the community structure in a dynamic network can be tracked over time, which helps in the mining of dynamic network information. In this paper, we propose a method of tracking dynamic community evolution that is based on resistance distance. Specifically, we model the time-varying features of dynamic networks using the convergence of a resistance-based distance. In our model, the heterogeneity of neighboring nodes can be obtained in the local topology of nodes by analyzing the resistance distance between nodes. We design a community discovery algorithm that essentially discovers community structures on dynamic networks by identifying the so-called core node. During the process of community evolution analysis, both the dynamic contribution of ordinary nodes and core nodes in each community are considered. In addition, to avoid the inclusion of spurious communities in the community structure, we define the notion of noise community and account for it in our algorithm. Experimental results show that the method proposed in this paper can yield better accuracy than other existing methods.en_US
dc.description.peerreviewedYesen_US
dc.languageEnglishen_US
dc.publisherElsevieren_US
dc.relation.ispartofpagefrom114536en_US
dc.relation.ispartofjournalExpert Systems with Applicationsen_US
dc.relation.ispartofvolume171en_US
dc.subject.fieldofresearchMathematical Sciencesen_US
dc.subject.fieldofresearchInformation and Computing Sciencesen_US
dc.subject.fieldofresearchEngineeringen_US
dc.subject.fieldofresearchcode01en_US
dc.subject.fieldofresearchcode08en_US
dc.subject.fieldofresearchcode09en_US
dc.subject.keywordsScience & Technologyen_US
dc.subject.keywordsEngineering, Electrical & Electronicen_US
dc.subject.keywordsOperations Research & Management Scienceen_US
dc.subject.keywordsArtificial Intelligenceen_US
dc.titleEvolutionary community discovery in dynamic social networks via resistance distanceen_US
dc.typeJournal articleen_US
dc.type.descriptionC1 - Articlesen_US
dcterms.bibliographicCitationLi, W; Zhu, H; Li, S; Wang, H; Dai, H; Wang, C; Jin, Q, Evolutionary community discovery in dynamic social networks via resistance distance, Expert Systems with Applications, 2021, 171, pp. 114536en_US
dc.date.updated2021-04-29T02:15:15Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Can


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