Centrality measure in social networks based on linear threshold model
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10.1016/j.knosys.2017.10.029
Inclou dades d'ús des de 2022
Cita com:
hdl:2117/111727
Tipus de documentArticle
Data publicació2018-01-15
Condicions d'accésAccés obert
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteASPECTOS MATEMATICOS, COMPUTACIONALES Y SOCIALES EN CONTEXTOS DE VOTACION Y DE COOPERACION. (MINECO-MTM2015-66818-P)
MODELOS Y METODOS COMPUTACIONALES PARA DATOS MASIVOS ESTRUCTURADOS (MINECO-TIN2013-46181-C2-1-R)
MODELOS Y METODOS COMPUTACIONALES PARA DATOS MASIVOS ESTRUCTURADOS (MINECO-TIN2013-46181-C2-1-R)
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.
CitacióRiquelme, 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.
ISSN0950-7051
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