Mining Stable Communities in Temporal Networks by Density-based Clustering

Hongchao Qin, Rong Hua Li, Guoren Wang*, Xin Huang, Ye Yuan, Jeffrey Xu Yu

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

9 Citations (Scopus)


Community detection is a fundamental task in graph data mining. Most existing studies in contact networks, collaboration networks, and social networks do not utilize the temporal information associated with edges for community detection. In this article, we study a problem of finding stable communities in a temporal network, where each edge is associated with a timestamp. Our goal is to identify the communities in a temporal network that are stable over time. To efficiently find the stable communities, we develop a new community detection algorithm based on the density-based graph clustering framework. We also propose several carefully-designed pruning techniques to significantly speed up the proposed algorithm. We conduct extensive experiments on four real-life temporal networks to evaluate our algorithm. The results demonstrate the effectiveness and efficiency of the proposed algorithm.

Original languageEnglish
Pages (from-to)671-684
Number of pages14
JournalIEEE Transactions on Big Data
Issue number3
Early online date18 Feb 2020
Publication statusPublished - 1 Jun 2022

Scopus Subject Areas

  • Information Systems and Management
  • Information Systems

User-Defined Keywords

  • Community detection
  • temporal networks
  • stable community
  • structural graph clustering
  • density-based clustering


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