Noise-enhanced community detection

Reyhaneh Abdolazimi, Shengmin Jin, Reza Zafarani

Research output: Chapter in Book/Report/Conference proceedingConference contribution

1 Scopus citations

Abstract

Community structure plays a significant role in uncovering the structure of a network. While many community detection algorithms have been introduced, improving the quality of detected communities is still an open problem. In many areas of science, adding noise improves system performance and algorithm efficiency, motivating us to also explore the possibility of adding noise to improve community detection algorithms. We propose a noise-enhanced community detection framework that improves communities detected by existing community detection methods. The framework introduces three noise methods to help detect communities better. Theoretical justification and extensive experiments on synthetic and real-world datasets show that our framework helps community detection methods find better communities.

Original languageEnglish (US)
Title of host publicationProceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020
PublisherAssociation for Computing Machinery, Inc
Pages271-280
Number of pages10
ISBN (Electronic)9781450370981
DOIs
StatePublished - Jul 13 2020
Event31st ACM Conference on Hypertext and Social Media, HT 2020 - Virtual, Online, United States
Duration: Jul 13 2020Jul 15 2020

Publication series

NameProceedings of the 31st ACM Conference on Hypertext and Social Media, HT 2020

Conference

Conference31st ACM Conference on Hypertext and Social Media, HT 2020
Country/TerritoryUnited States
CityVirtual, Online
Period7/13/207/15/20

Keywords

  • Community detection
  • Graph mining
  • Noise-enhanced methods

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Graphics and Computer-Aided Design
  • Human-Computer Interaction
  • Software

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