Show simple item record

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.issn0957-4174
dc.identifier.doi10.1016/j.eswa.2020.114536
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.
dc.description.peerreviewedYes
dc.languageEnglish
dc.publisherElsevier
dc.relation.ispartofpagefrom114536
dc.relation.ispartofjournalExpert Systems with Applications
dc.relation.ispartofvolume171
dc.subject.fieldofresearchMathematical sciences
dc.subject.fieldofresearchEngineering
dc.subject.fieldofresearchcode49
dc.subject.fieldofresearchcode40
dc.subject.keywordsScience & Technology
dc.subject.keywordsEngineering, Electrical & Electronic
dc.subject.keywordsOperations Research & Management Science
dc.subject.keywordsArtificial Intelligence
dc.titleEvolutionary community discovery in dynamic social networks via resistance distance
dc.typeJournal article
dc.type.descriptionC1 - Articles
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. 114536
dc.date.updated2021-04-29T02:15:15Z
gro.hasfulltextNo Full Text
gro.griffith.authorWang, Can


Files in this item

FilesSizeFormatView

There are no files associated with this item.

This item appears in the following Collection(s)

  • Journal articles
    Contains articles published by Griffith authors in scholarly journals.

Show simple item record