Local Aggregated Differential Evolution Algorithm for Community Detection in Complex Networks

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1 Citation (Scopus)

Abstract

As one of the cornerstones in complex networks, researches on community structure upholds many advances in scientific fields like social and biological networks. A number of recent studies have concentrated on the community detection problem, which is equilibrium to the optimization of the fitness function called modularity over possible partition schemes. In this paper we propose a lightweight differential evolution algorithm with an additional local aggregation operator, which contributes to the improvement in precision, to search for the optimal division of the network. The competitive accuracy and scalability of the introduced algorithm have been demonstrated on computer generated networks and real world data sets in comparison with other famous counterparts.

Original languageEnglish
Title of host publicationProceedings of the 37th Chinese Control Conference, CCC 2018
EditorsXin Chen, Qianchuan Zhao
PublisherIEEE Computer Society
Pages2384-2389
Number of pages6
ISBN (Electronic)9789881563941
DOIs
Publication statusPublished - 5 Oct 2018
Event37th Chinese Control Conference, CCC 2018 - Wuhan, China
Duration: 25 Jul 201827 Jul 2018

Publication series

NameChinese Control Conference, CCC
Volume2018-July
ISSN (Print)1934-1768
ISSN (Electronic)2161-2927

Conference

Conference37th Chinese Control Conference, CCC 2018
Country/TerritoryChina
CityWuhan
Period25/07/1827/07/18

Keywords

  • Community detection
  • Complex networks
  • Differential evolution
  • Optimization

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