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Centrality measure in social networks based on linear threshold model
dc.contributor.author | Riquelme Csori, Fabián |
dc.contributor.author | Gonzalez Cantergiani, Pablo |
dc.contributor.author | Molinero Albareda, Xavier |
dc.contributor.author | Serna Iglesias, María José |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Matemàtiques |
dc.contributor.other | Universitat Politècnica de Catalunya. Departament de Ciències de la Computació |
dc.date.accessioned | 2017-12-11T16:58:45Z |
dc.date.available | 2020-02-01T01:26:23Z |
dc.date.issued | 2018-01-15 |
dc.identifier.citation | Riquelme, F., Gonzalez, P., Molinero, X., Serna, M. Centrality measure in social networks based on linear threshold model. "Knowledge-based systems", 15 Gener 2018, vol. 140, p. 92-102. |
dc.identifier.issn | 0950-7051 |
dc.identifier.uri | http://hdl.handle.net/2117/111727 |
dc.description.abstract | Centrality and influence spread are two of the most studied concepts in social network analysis. In recent years, centrality measures have attracted the attention of many researchers, generating a large and varied number of new studies about social network analysis and its applications. However, as far as we know, traditional models of influence spread have not yet been exhaustively used to define centrality measures according to the influence criteria. Most of the considered work in this topic is based on the independent cascade model. In this paper we explore the possibilities of the linear threshold model for the definition of centrality measures to be used on weighted and labeled social networks. We propose a new centrality measure to rank the users of the network, the Linear Threshold Rank (LTR), and a centralization measure to determine to what extent the entire network has a centralized structure, the Linear Threshold Centralization (LTC). We appraise the viability of the approach through several case studies. We consider four different social networks to compare our new measures with two centrality measures based on relevance criteria and another centrality measure based on the independent cascade model. Our results show that our measures are useful for ranking actors and networks in a distinguishable way. |
dc.format.extent | 11 p. |
dc.language.iso | eng |
dc.rights | Attribution-NonCommercial-NoDerivs 3.0 Spain |
dc.rights.uri | http://creativecommons.org/licenses/by-nc-nd/3.0/es/ |
dc.subject | Àrees temàtiques de la UPC::Matemàtiques i estadística::Investigació operativa::Teoria de jocs |
dc.subject.lcsh | Centrality (Graph theory) |
dc.subject.lcsh | Online social networks |
dc.subject.lcsh | Social influence |
dc.subject.other | Centrality |
dc.subject.other | Independent cascade model |
dc.subject.other | Linear threshold model |
dc.subject.other | Social network |
dc.subject.other | Spread of influence |
dc.title | Centrality measure in social networks based on linear threshold model |
dc.type | Article |
dc.subject.lemac | Grafs, Teoria de |
dc.subject.lemac | Xarxes socials en línia |
dc.subject.lemac | Influència social |
dc.contributor.group | Universitat Politècnica de Catalunya. GRTJ - Grup de Recerca en Teoria de Jocs |
dc.contributor.group | Universitat Politècnica de Catalunya. ALBCOM - Algorismia, Bioinformàtica, Complexitat i Mètodes Formals |
dc.identifier.doi | 10.1016/j.knosys.2017.10.029 |
dc.description.peerreviewed | Peer Reviewed |
dc.subject.ams | Classificació AMS::05 Combinatorics::05C Graph theory |
dc.subject.ams | Classificació AMS::68 Computer science::68R Discrete mathematics in relation to computer science |
dc.subject.ams | Classificació AMS::91 Game theory, economics, social and behavioral sciences::91D Mathematical sociology |
dc.relation.publisherversion | http://www.sciencedirect.com/science/article/pii/S0950705117304975?via%3Dihub |
dc.rights.access | Open Access |
local.identifier.drac | 21605597 |
dc.description.version | Postprint (author's final draft) |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//MTM2015-66818-P/ES/ASPECTOS MATEMATICOS, COMPUTACIONALES Y SOCIALES EN CONTEXTOS DE VOTACION Y DE COOPERACION./ |
dc.relation.projectid | info:eu-repo/grantAgreement/MINECO//TIN2013-46181-C2-1-R/ES/MODELOS Y METODOS COMPUTACIONALES PARA DATOS MASIVOS ESTRUCTURADOS/ |
dc.relation.projectid | info:eu-repo/grantAgreement/AGAUR/2014SGR1034 |
local.citation.author | Riquelme, F.; Gonzalez, P.; Molinero, X.; Serna, M. |
local.citation.publicationName | Knowledge-based systems |
local.citation.volume | 140 |
local.citation.startingPage | 92 |
local.citation.endingPage | 102 |
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