Evolutionary community discovery in dynamic social networks via resistance distance

Weimin LI*, Heng ZHU, Shaohua LI, Hao WANG, Hongning DAI, Can WANG, Qun JIN

*Corresponding author for this work

Research output: Journal PublicationsJournal Article (refereed)peer-review

Abstract

Traditional 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.

Original languageEnglish
Article number114536
JournalExpert Systems with Applications
Volume171
Early online date29 Dec 2020
DOIs
Publication statusPublished - 1 Jun 2021
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Key R&D Program for China (No. 2017YFE0117500 ).

Publisher Copyright:
© 2020 Elsevier Ltd

Keywords

  • Community discovery
  • Community evolution
  • Dynamic social networks
  • Resistance distance

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