Detecting Communities in Networks by Merging Cliques

Yan Bowen, Steve Gregory

Research output: Chapter in Book/Report/Conference proceedingConference Contribution (Conference Proceeding)

23 Citations (Scopus)

Abstract

Many algorithms have been proposed for detecting disjoint communities (relatively densely connected subgraphs) in networks. One popular technique is to optimize modularity, a measure of the quality of a partition in terms of the number of intracommunity and intercommunity edges. Greedy approximate algorithms for maximizing modularity can be very fast and effective. We propose a new algorithm that starts by detecting disjoint cliques and then merges these to optimize modularity. We show that this performs better than other similar algorithms in terms of both modularity and execution speed.
Translated title of the contributionDetecting Communities in Networks by Merging Cliques
Original languageEnglish
Title of host publication2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009)
PublisherInstitute of Electrical and Electronics Engineers (IEEE)
Pages832 - 836
Number of pages5
ISBN (Print)9781424447541
DOIs
Publication statusPublished - 2009

Bibliographical note

Conference Proceedings/Title of Journal: 2009 IEEE International Conference on Intelligent Computing and Intelligent Systems (ICIS 2009)

Fingerprint Dive into the research topics of 'Detecting Communities in Networks by Merging Cliques'. Together they form a unique fingerprint.

Cite this