Evolutionary community discovery in dynamic social networks via resistance distance

Weimin Li*, Heng Zhu, Shaohua Li, Hao Wang, Hongning Dai, Can Wang, Qun Jin


研究成果: Article査読

16 被引用数 (Scopus)


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.

ジャーナルExpert Systems with Applications
出版ステータスPublished - 2021 6月 1

ASJC Scopus subject areas

  • 工学一般
  • コンピュータ サイエンスの応用
  • 人工知能


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