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dc.creatorFumanal Idocin, Javieres_ES
dc.creatorCordón, Óscares_ES
dc.creatorMinárová, Maríaes_ES
dc.creatorAlonso Betanzos, Amparoes_ES
dc.creatorBustince Sola, Humbertoes_ES
dc.date.accessioned2022-09-21T08:28:05Z
dc.date.available2022-09-21T08:28:05Z
dc.date.issued2021
dc.identifier.citationFumanal-Idocin; J.; Cordón, O.; Minarova, M.; Alonso, A.; Bustince, H.. (2021). Combinations of affinity functions for different community detection algorithms in social networks. 1 University of Hawaii Press; (p. 2107-2114).en
dc.identifier.urihttps://hdl.handle.net/2454/44085
dc.description.abstractSocial network analysis is a popular discipline among the social and behavioural sciences, in which the relationships between different social entities are modelled as a network. One of the most popular problems in social network analysis is finding communities in its network structure. Usually, a community in a social network is a functional sub-partition of the graph. However, as the definition of community is somewhat imprecise, many algorithms have been proposed to solve this task, each of them focusing on different social characteristics of the actors and the communities. In this work we propose to use novel combinations of affinity functions, which are designed to capture different social mechanics in the network interactions. We use them to extend already existing community detection algorithms in order to combine the capacity of the affinity functions to model different social interactions than those exploited by the original algorithms.en
dc.description.sponsorshipJavier Fumanal Idocin and Humberto Bustince’s re-search has been supported by the project PID2019-108392GBI00 (AEI/10.13039/501100011033). Maria Minarová research has been funded by the project work was supported by the projects APVV-17-0066 andAPVV-18-0052. Oscar Cordon’s research was supported by the Spanish Ministry of Science, Innovation and Universities under grant EXASOCO (PGC2018-101216-B-I00), including, European Regional Development Funds (ERDF).en
dc.format.mimetypeapplication/pdfen
dc.language.isoengen
dc.publisherUniversity of Hawaii Pressen
dc.relation.ispartofBui, T. X. (Ed.): Proceedings of the Hawaii International Conference on System Sciences, HICSS 2021. University of Hawaii Press, 2021, 2107 - 2114,en
dc.rightsCreative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/en
dc.subjectSocial network analysisen
dc.subjectAffinity functionsen
dc.subjectCommunity detectionen
dc.subjectModularityen
dc.subjectAggregation functionsen
dc.titleCombinations of affinity functions for different community detection algorithms in social networksen
dc.typeContribución a congreso / Biltzarrerako ekarpenaes
dc.typeinfo:eu-repo/semantics/conferenceObjecten
dc.date.updated2022-09-21T08:21:07Z
dc.contributor.departmentEstadística, Informática y Matemáticases_ES
dc.contributor.departmentEstatistika, Informatika eta Matematikaeu
dc.rights.accessRightsAcceso abierto / Sarbide irekiaes
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessen
dc.identifier.doi10.24251/HICSS.2022.265
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/en
dc.relation.projectIDinfo:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-101216-B-I00/ES/en
dc.relation.publisherversionhttps://doi.org/10.24251/HICSS.2022.265
dc.type.versionVersión publicada / Argitaratu den bertsioaes
dc.type.versioninfo:eu-repo/semantics/publishedVersionen


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Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
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El Repositorio ha recibido la ayuda de la Fundación Española para la Ciencia y la Tecnología para la realización de actividades en el ámbito del fomento de la investigación científica de excelencia, en la Línea 2. Repositorios institucionales (convocatoria 2020-2021).
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