Analysis and comparison of centrality measures applied to urban networks with data

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dc.contributorAnálisis y Visualización de Datos en Redes (ANVIDA)es_ES
dc.contributor.authorCurado, Manuel-
dc.contributor.authorTortosa, Leandro-
dc.contributor.authorVicent, Jose F.-
dc.contributor.authorYeghikyan, Gevorg-
dc.contributor.otherUniversidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificiales_ES
dc.identifier.citationJournal of Computational Science. 2020, 43: 101127. doi:10.1016/j.jocs.2020.101127es_ES
dc.identifier.issn1877-7503 (Print)-
dc.identifier.issn1877-7511 (Online)-
dc.description.abstractFor a considerable time, researchers have focused on defining different measures capable to characterizing the importance of vertices in networks. One type of these networks, the cities, are complex systems that generate large quantity of information. These data are an important part of the characteristics of the urban network itself. Because of this, it is crucial to have a classification system, for the vertices of a network, considering the data we can find in the city itself. To address this question, this paper studies and compares several measures of centrality specifically applied to urban networks. These centralities are based on the calculation of the eigenvectors of a matrix and are very suitable for urban networks with data. With the aim of expanding the range covered by these measures, a new centrality measure is presented. Finally we compare three centralities by means of a real network and real data on the city of Rome (Italy).es_ES
dc.rights© 2020 Elsevier B.V.es_ES
dc.subjectCentrality measureses_ES
dc.subjectUrban networkses_ES
dc.subjectVertex importancees_ES
dc.subjectEigenvector centralityes_ES
dc.subject.otherCiencia de la Computación e Inteligencia Artificiales_ES
dc.titleAnalysis and comparison of centrality measures applied to urban networks with dataes_ES
Appears in Collections:INV - ANVIDA - Artículos de Revistas

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