Mining Stable Communities in Temporal Networks by Density-based Clustering

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

Research output: Contribution to journalArticlepeer-review

Abstract

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 paper, 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
JournalIEEE Transactions on Big Data
DOIs
Publication statusAccepted/In press - 2020

Scopus Subject Areas

  • Information Systems
  • Information Systems and Management

User-Defined Keywords

  • Big Data
  • Clustering algorithms
  • Collaboration
  • Community Detection
  • Computer science
  • Data mining
  • Density-based Clustering
  • Detection algorithms
  • Image edge detection
  • Stable Community
  • Structural Graph Clustering
  • Temporal Networks

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