A joint optimization framework for better community detection based on link prediction in social networks

Shu Kai Zhang, Cheng Te Li, Shou De Lin

Research output: Contribution to journalArticlepeer-review

Abstract

Real-world network data can be incomplete (e.g., the social connections are partially observed) due to reasons such as graph sampling and privacy concerns. Consequently, communities detected based on such incomplete network information could be not as reliable as the ones identified based on the fully observed network. While existing studies first predict missing links and then detect communities, in this paper, a joint optimization framework, Communities detected on Predicted Edges, is proposed. Our goal to improve the quality of community detection through learning the probability of unseen links and the probability of community affiliation of nodes simultaneously. Link prediction and community detection are mutually reinforced to generate better results of both tasks. Experiments conducted on a number of well-known network data show that the proposed COPE stably outperforms several state-of-the-art community detection algorithms.

Original languageEnglish
Pages (from-to)4277-4296
Number of pages20
JournalKnowledge and Information Systems
Volume62
Issue number11
DOIs
Publication statusPublished - 2020 Nov 1

All Science Journal Classification (ASJC) codes

  • Software
  • Information Systems
  • Human-Computer Interaction
  • Hardware and Architecture
  • Artificial Intelligence

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