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dc.contributor.authorDong, Yuchengen
dc.contributor.authorZha, Quanboen
dc.contributor.authorZhang, Hengjieen
dc.contributor.authorKou, Gangen
dc.contributor.authorFujita, Hamidoen
dc.contributor.authorChiclana, Franciscoen
dc.contributor.authorHerrera-Viedma, Enriqueen
dc.date.accessioned2018-08-16T10:32:07Z
dc.date.available2018-08-16T10:32:07Z
dc.date.issued2018-07-06
dc.identifier.citationDong, Y. et al. (2018) Consensus Reaching in Social Network Group Decision Making: Research Paradigms and Challenges. Knowledge-Based Systems. Accepted for publication on 22nd June 2018. doi: 10.1016/j.knosys.2018.06.036en
dc.identifier.urihttp://hdl.handle.net/2086/16478
dc.descriptionThe file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.en
dc.description.abstractIn social network group decision making (SNGDM), the consensus reaching process (CRP) is used to help decision makers with social relationships reach consensus. Many CRP studies have been conducted in SNGDM until now. This paper provides a review of CRPs in SNGDM, and as a result it classifies them into two paradigms: (i) the CRP paradigm based on trust relationships, and (ii) the CRP paradigm based on opinion evolution. Furthermore, identified research challenges are put forward to advance this area of research.en
dc.language.isoenen
dc.publisherElsevieren
dc.subjectGroup decision makingen
dc.subjectSocial networken
dc.subjectConsensusen
dc.subjectOpinion evolutionen
dc.titleConsensus Reaching in Social Network Group Decision Making: Research Paradigms and Challengesen
dc.typeArticleen
dc.identifier.doihttps://dx.doi.org/10.1016/j.knosys.2018.06.036
dc.researchgroupInstitute of Artificial Intelligence (IAI)en
dc.peerreviewedYesen
dc.funderWe would like to acknowledge the financial support of the grants (Nos. 71571124 and 71725001) from NSF of China, the grant (No. sksyl201705) from Sichuan University, and the grant TIN2016-75850-R supported by the Spanish Ministry of Economy and Competitiveness with FEDER funds.en
dc.projectidWe would like to acknowledge the financial support of the grants (Nos. 71571124 and 71725001) from NSF of China, the grant (No. sksyl201705) from Sichuan University, and the grant TIN2016-75850-R supported by the Spanish Ministry of Economy and Competitiveness with FEDER funds.en
dc.cclicenceCC-BY-NC-NDen
dc.date.acceptance2018-06-22en
dc.researchinstituteInstitute of Artificial Intelligence (IAI)en


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