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Combinations of affinity functions for different community detection algorithms in social networks
dc.creator | Fumanal Idocin, Javier | es_ES |
dc.creator | Cordón, Óscar | es_ES |
dc.creator | Minárová, María | es_ES |
dc.creator | Alonso Betanzos, Amparo | es_ES |
dc.creator | Bustince Sola, Humberto | es_ES |
dc.date.accessioned | 2022-09-21T08:28:05Z | |
dc.date.available | 2022-09-21T08:28:05Z | |
dc.date.issued | 2021 | |
dc.identifier.citation | Fumanal-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.uri | https://hdl.handle.net/2454/44085 | |
dc.description.abstract | Social 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.sponsorship | Javier 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.mimetype | application/pdf | en |
dc.language.iso | eng | en |
dc.publisher | University of Hawaii Press | en |
dc.relation.ispartof | Bui, T. X. (Ed.): Proceedings of the Hawaii International Conference on System Sciences, HICSS 2021. University of Hawaii Press, 2021, 2107 - 2114, | en |
dc.rights | Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) | |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/4.0/ | en |
dc.subject | Social network analysis | en |
dc.subject | Affinity functions | en |
dc.subject | Community detection | en |
dc.subject | Modularity | en |
dc.subject | Aggregation functions | en |
dc.title | Combinations of affinity functions for different community detection algorithms in social networks | en |
dc.type | Contribución a congreso / Biltzarrerako ekarpena | es |
dc.type | info:eu-repo/semantics/conferenceObject | en |
dc.date.updated | 2022-09-21T08:21:07Z | |
dc.contributor.department | Estadística, Informática y Matemáticas | es_ES |
dc.contributor.department | Estatistika, Informatika eta Matematika | eu |
dc.rights.accessRights | Acceso abierto / Sarbide irekia | es |
dc.rights.accessRights | info:eu-repo/semantics/openAccess | en |
dc.identifier.doi | 10.24251/HICSS.2022.265 | |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2019-108392GB-I00/ES/ | en |
dc.relation.projectID | info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PGC2018-101216-B-I00/ES/ | en |
dc.relation.publisherversion | https://doi.org/10.24251/HICSS.2022.265 | |
dc.type.version | Versión publicada / Argitaratu den bertsioa | es |
dc.type.version | info:eu-repo/semantics/publishedVersion | en |