Particle competition and cooperation for uncovering network overlap community structure

Fabricio Breve*, Liang Zhao, Marcos Quiles, Witold Pedrycz, Jiming LIU

*Corresponding author for this work

Research output: Chapter in book/report/conference proceedingConference proceedingpeer-review

4 Citations (Scopus)

Abstract

Identification and classification of overlap nodes in communities is an important topic in data mining. In this paper, a new graph-based (network-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in the network to uncover overlap nodes, i.e., the algorithm can output continuous-valued output (soft labels), which corresponds to the levels of membership from the nodes to each of the communities. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method.

Original languageEnglish
Title of host publicationAdvances in Neural Networks - 8th International Symposium on Neural Networks, ISNN 2011
Pages426-433
Number of pages8
EditionPART 3
DOIs
Publication statusPublished - 2011
Event8th International Symposium on Neural Networks, ISNN 2011 - Guilin, China
Duration: 29 May 20111 Jun 2011

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
NumberPART 3
Volume6677 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Symposium on Neural Networks, ISNN 2011
Country/TerritoryChina
CityGuilin
Period29/05/111/06/11

Scopus Subject Areas

  • Theoretical Computer Science
  • Computer Science(all)

User-Defined Keywords

  • community detection
  • Graph-based method
  • overlap nodes
  • particle competition and cooperation

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