CR-Graph: Community Reinforcement for Accurate Community Detection

Yoonsuk Kang, Jun Seok Lee, Won Yong Shin, Sang Wook Kim

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

Abstract

In this paper, we present CR-Graph (community reinforcement on graphs), a novel method that helps existing algorithms to perform more-accurate community detection (CD). Toward this end, CR-Graph strengthens the community structure of a given original graph by adding non-existent predicted intra-community edges and deleting existing predicted inter-community edges. To design CR-Graph, we propose the following two strategies: (1) predicting intra-community and inter-community edges (i.e., the type of edges) and (2) determining the amount of edges to be added/deleted. To show the effectiveness of CR-Graph, we conduct extensive experiments with various CD algorithms on 7 synthetic and 4 real-world graphs. The results demonstrate that CR-Graph improves the accuracy of all underlying CD algorithms universally and consistently.

Original languageEnglish
Title of host publicationCIKM 2020 - Proceedings of the 29th ACM International Conference on Information and Knowledge Management
PublisherAssociation for Computing Machinery
Pages2077-2080
Number of pages4
ISBN (Electronic)9781450368599
DOIs
Publication statusPublished - 2020 Oct 19
Event29th ACM International Conference on Information and Knowledge Management, CIKM 2020 - Virtual, Online, Ireland
Duration: 2020 Oct 192020 Oct 23

Publication series

NameInternational Conference on Information and Knowledge Management, Proceedings

Conference

Conference29th ACM International Conference on Information and Knowledge Management, CIKM 2020
CountryIreland
CityVirtual, Online
Period20/10/1920/10/23

Bibliographical note

Funding Information:
This research was supported by (1) the National Research Foundation of Korea grant funded by the Korea government (NRF-2020R1A2B5B03001960), (2) the National Research Foundation of Korea grant funded by the Korea government (2018R1A5A7059549), and (3) the Next-Generation Information Computing Development Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT (NRF-2017M3C4A7069440).

Publisher Copyright:
© 2020 ACM.

All Science Journal Classification (ASJC) codes

  • Business, Management and Accounting(all)
  • Decision Sciences(all)

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