Effective algorithm for detecting community structure in complex networks based on GA and clustering

Xin Liu*, Deyi Li, Shuliang Wang, Zhiwei Tao

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

59 Citations (Scopus)

Abstract

The study of networked systems has experienced a particular surge of interest in the last decade. One issue that has received a considerable amount of attention is the detection and characterization of community structure in networks, meaning the appearance of densely connected groups of vertices, with only sparser connections between groups. In this paper, we present an approach for the problem of community detection using genetic algorithm (GA) in conjunction with the method of clustering. We demonstrate that our algorithms are highly effective at discovering community structure in both computer-generated and real-world network data, and show how they can be used to shed light on the sometimes daunting complex real-world systems of scale-free network structure.

Original languageEnglish
Title of host publicationComputational Science - ICCS 2007 - 7th International Conference, Proceedings
PublisherSpringer Verlag
Pages657-664
Number of pages8
EditionPART 2
ISBN (Print)9783540725855
DOIs
Publication statusPublished - 2007
Externally publishedYes
Event7th International Conference on Computational Science, ICCS 2007 - Beijing, China
Duration: 27 May 200730 May 2007

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 2
Volume4488 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference7th International Conference on Computational Science, ICCS 2007
Country/TerritoryChina
CityBeijing
Period27/05/0730/05/07

Keywords

  • Clustering
  • Community structure
  • Complex network
  • Modularity

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