Fuzzy community structure detection by particle competition and cooperation

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dc.contributor.authorBreve, Fabricio [UNESP]
dc.contributor.authorZhao, Liang
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.date.accessioned2014-05-27T11:28:44Z
dc.date.available2014-05-27T11:28:44Z
dc.date.issued2013-04-01
dc.description.abstractIdentification and classification of overlapping nodes in networks are important topics in data mining. In this paper, a network-based (graph-based) semi-supervised learning method is proposed. It is based on competition and cooperation among walking particles in a network to uncover overlapping nodes by generating continuous-valued outputs (soft labels), corresponding to the levels of membership from the nodes to each of the communities. Moreover, the proposed method can be applied to detect overlapping data items in a data set of general form, such as a vector-based data set, once it is transformed to a network. Usually, label propagation involves risks of error amplification. In order to avoid this problem, the proposed method offers a mechanism to identify outliers among the labeled data items, and consequently prevents error propagation from such outliers. Computer simulations carried out for synthetic and real-world data sets provide a numeric quantification of the performance of the method. © 2012 Springer-Verlag.en
dc.description.affiliationDepartment of Computer Science Institute of Mathematics and Computer Science (ICMC), University of São Paulo (USP), Av. Trabalhador São-carlense, 400, 13560-970 São Carlos, SP
dc.description.affiliationDepartment of Statistics, Applied Mathematics and Computation (DEMAC) Institute of Geosciences and Exact Sciences (IGCE), São Paulo State University (UNESP), Avenida 24 A, 1515, 13506-900 Rio Claro, SP
dc.description.affiliationUnespDepartment of Statistics, Applied Mathematics and Computation (DEMAC) Institute of Geosciences and Exact Sciences (IGCE), São Paulo State University (UNESP), Avenida 24 A, 1515, 13506-900 Rio Claro, SP
dc.format.extent659-673
dc.identifierhttp://dx.doi.org/10.1007/s00500-012-0924-3
dc.identifier.citationSoft Computing, v. 17, n. 4, p. 659-673, 2013.
dc.identifier.doi10.1007/s00500-012-0924-3
dc.identifier.issn1432-7643
dc.identifier.issn1433-7479
dc.identifier.lattes5693860025538327
dc.identifier.orcid0000-0002-1123-9784
dc.identifier.scopus2-s2.0-84874948546
dc.identifier.urihttp://hdl.handle.net/11449/74902
dc.identifier.wosWOS:000316334400015
dc.language.isoeng
dc.relation.ispartofSoft Computing
dc.relation.ispartofjcr2.367
dc.relation.ispartofsjr0,593
dc.relation.ispartofsjr0,593
dc.rights.accessRightsAcesso restrito
dc.sourceScopus
dc.subjectCommunity detection
dc.subjectGraph-based method
dc.subjectOutliers
dc.subjectOverlapping nodes
dc.subjectParticle competition and cooperation
dc.subjectGraph-based methods
dc.subjectParticle competition and cooperations
dc.subjectGraphic methods
dc.subjectSupervised learning
dc.subjectVirtual reality
dc.subjectStatistics
dc.titleFuzzy community structure detection by particle competition and cooperationen
dc.typeArtigo
dcterms.licensehttp://www.springer.com/open+access/authors+rights
unesp.author.lattes5693860025538327
unesp.author.orcid0000-0002-1123-9784[1]
unesp.author.orcid0000-0002-1502-6604[2]
unesp.campusUniversidade Estadual Paulista (Unesp), Instituto de Geociências e Ciências Exatas, Rio Claropt

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Artigos - Estatistica, Matemática Aplicada e Computação - IGCE