Please use this identifier to cite or link to this item: http://hdl.handle.net/1942/32437
Full metadata record
DC FieldValueLanguage
dc.contributor.authorFUENTES HERRERA, Ivett-
dc.contributor.authorPina, Arian-
dc.contributor.authorNAPOLES RUIZ, Gonzalo-
dc.contributor.authorArco, Leticia-
dc.contributor.authorVANHOOF, Koen-
dc.date.accessioned2020-10-13T07:33:20Z-
dc.date.available2020-10-13T07:33:20Z-
dc.date.issued2020-
dc.date.submitted2020-10-01T21:51:09Z-
dc.identifier.citationRough Sets, p. 401 -415-
dc.identifier.isbn978-3-030-52704-4-
dc.identifier.isbn978-3-030-52705-1-
dc.identifier.issn0302-9743-
dc.identifier.issn1611-3349-
dc.identifier.urihttp://hdl.handle.net/1942/32437-
dc.description.abstractRough set theory has many interesting applications in circumstances which are characterized by vagueness. In this paper, the applications of rough set theory in community detection analysis is discussed based on the Rough Net definition. We will focus the application of Rough Net concept in community detection validity in both monoplex and multiplex complex networks. Also, the topological evolution estimation between adjacent layers in dynamic networks is discussed and a new visualization schema combining both complex network representation and Rough Net definition is adopted contributing to the understanding of the community structure. We provide some examples demonstrating how the Rough Net definition can be used to analyze the properties of the community structure in real-world networks, including dynamic networks.-
dc.language.isoen-
dc.publisherSPRINGER INTERNATIONAL PUBLISHING AG-
dc.relation.ispartofseriesLecture Notes in Computer Science-
dc.rightsSpringer Nature Switzerland AG 2020-
dc.subject.otherExtended Rough Set Theory-
dc.subject.otherCommunity Detection Anal- ysis-
dc.subject.otherMonoplex Complex Networks-
dc.subject.otherMultiplex Complex Networks-
dc.titleRough Net Approach for Community Detection Analysis in Complex Networks-
dc.typeProceedings Paper-
local.bibliographicCitation.conferencedate29 june - 6 July 2020-
local.bibliographicCitation.conferencenameInternational Joint Conference on Rough Sets : IJCRS 2020-
local.bibliographicCitation.conferenceplaceHavana, Cuba (online)-
dc.identifier.epage415-
dc.identifier.spage401-
dc.identifier.volume12179-
local.bibliographicCitation.jcatC1-
local.publisher.placeGEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND-
local.type.refereedRefereed-
local.type.specifiedProceedings Paper-
local.relation.ispartofseriesnr12179-
dc.identifier.doi10.1007/978-3-030-52705-1_30-
dc.identifier.isi000713415600030-
local.provider.typeCrossRef-
local.bibliographicCitation.btitleRough Sets-
local.uhasselt.internationalyes-
item.fullcitationFUENTES HERRERA, Ivett; Pina, Arian; NAPOLES RUIZ, Gonzalo; Arco, Leticia & VANHOOF, Koen (2020) Rough Net Approach for Community Detection Analysis in Complex Networks. In: Rough Sets, p. 401 -415.-
item.contributorFUENTES HERRERA, Ivett-
item.contributorPina, Arian-
item.contributorNAPOLES RUIZ, Gonzalo-
item.contributorArco, Leticia-
item.contributorVANHOOF, Koen-
item.validationecoom 2022-
item.validationvabb 2022-
item.accessRightsOpen Access-
item.fulltextWith Fulltext-
Appears in Collections:Research publications
Files in This Item:
File Description SizeFormat 
manuscript.pdfPeer-reviewed author version1.46 MBAdobe PDFView/Open
Fuentes2020_Chapter_RoughNetApproachForCommunityDe.pdf
  Restricted Access		Published version2.76 MBAdobe PDFView/Open    Request a copy
Show simple item record

WEB OF SCIENCETM
Citations

2
checked on May 1, 2024

Page view(s)

92
checked on Jun 9, 2022

Download(s)

26
checked on Jun 9, 2022

Google ScholarTM

Check

Altmetric


Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.