Community detection and social network analysis based on the Italian wars of the 15th century

dc.contributor.authorFumanal Idocin, Javier
dc.contributor.authorAlonso Betanzos, Amparo
dc.contributor.authorCordón, Óscar
dc.contributor.authorBustince Sola, Humberto
dc.contributor.authorMinárová, María
dc.contributor.departmentInstitute of Smart Cities - ISCen
dc.date.accessioned2021-01-27T12:17:49Z
dc.date.available2021-12-01T00:00:15Z
dc.date.issued2020
dc.description.abstractIn this contribution we study social network modelling by using human interaction as a basis. To do so, we propose a new set of functions, affinities, designed to capture the nature of the local interactions among each pair of actors in a network. By using these functions, we develop a new community detection algorithm, the Borgia Clustering, where communities naturally arise from the multi-agent interaction in the network. We also discuss the effects of size and scale for communities regarding this case, as well as how we cope with the additional complexity present when big communities arise. Finally, we compare our community detection solution with other representative algorithms, finding favourable results.en
dc.description.sponsorshipJavier Fumanal Idocin’s and Humberto Bustince’s research has been supported by the project TIN2016-77356-P (AEI/FEDER,UE).Oscar Cordón’s research was supported by the Spanish Ministry of Science, Innovation and Universities under grant EX-ASOCO (PGC2018-101216-B-I00), including, European Regional Development Funds (ERDF). Amparo Alonso-Betanzos’ research has been financially supported in part by the Spanish Ministerio de Economía y Competitividad (research project TIN2015-65069-C2-1-R), by European Union FEDER funds and by the Consellería de Industria of the Xunta de Galicia, Spain (research project GRC2014 /035). M. Minárová’s research has been funded by the project work was supported by the project APVV-17-0066.en
dc.embargo.lift2021-12-01
dc.embargo.terms2021-12-01
dc.format.extent33 p.
dc.format.mimetypeapplication/pdfen
dc.identifier.doi10.1016/j.future.2020.06.030
dc.identifier.issn0167-739X
dc.identifier.urihttps://academica-e.unavarra.es/handle/2454/39070
dc.language.isoengen
dc.publisherElsevieren
dc.relation.ispartofFuture Generation Computer Systems, 2020, 113, 25-40en
dc.relation.projectIDinfo:eu-repo/grantAgreement/ES/1PE/TIN2016-77356-P/
dc.relation.projectIDinfo:eu-repo/grantAgreement/MINECO//TIN2015-65069-C2-1-R/ES/
dc.relation.publisherversionhttps://doi.org/10.1016/j.future.2020.06.030
dc.rights© 2020 Elsevier B.V. This manuscript version is made available under the CC-BY-NC-ND 4.0.en
dc.rights.accessRightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectSocial networken
dc.subjectCommunity detectionen
dc.subjectHuman social behaviouren
dc.subjectSimulationen
dc.subjectMulti-agent systemsen
dc.titleCommunity detection and social network analysis based on the Italian wars of the 15th centuryen
dc.typeinfo:eu-repo/semantics/article
dc.type.versioninfo:eu-repo/semantics/acceptedVersion
dspace.entity.typePublication
relation.isAuthorOfPublication5193d488-fd4e-4556-88ca-ba5116412a36
relation.isAuthorOfPublication1bdd7a0e-704f-48e5-8d27-4486444f82c9
relation.isAuthorOfPublication.latestForDiscovery5193d488-fd4e-4556-88ca-ba5116412a36

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