A new betweenness centrality measure based on an algorithm for ranking the nodes of a network

Please use this identifier to cite or link to this item: http://hdl.handle.net/10045/44390
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dc.contributorAnálisis y Visualización de Datos en Redes (ANVIDA)es
dc.contributor.authorAgryzkov, Taras-
dc.contributor.authorOliver, Jose-Luis-
dc.contributor.authorTortosa, Leandro-
dc.contributor.authorVicent, Jose F.-
dc.contributor.otherUniversidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificiales
dc.identifier.citationApplied Mathematics and Computation. 2014, 244: 467-478. doi:10.1016/j.amc.2014.07.026es
dc.identifier.issn0096-3003 (Print)-
dc.identifier.issn1873-5649 (Online)-
dc.description.abstractWe propose and discuss a new centrality index for urban street patterns represented as networks in geographical space. This centrality measure, that we call ranking-betweenness centrality, combines the idea behind the random-walk betweenness centrality measure and the idea of ranking the nodes of a network produced by an adapted PageRank algorithm. We initially use a PageRank algorithm in which we are able to transform some information of the network that we want to analyze into numerical values. Numerical values summarizing the information are associated to each of the nodes by means of a data matrix. After running the adapted PageRank algorithm, a ranking of the nodes is obtained, according to their importance in the network. This classification is the starting point for applying an algorithm based on the random-walk betweenness centrality. A detailed example of a real urban street network is discussed in order to understand the process to evaluate the ranking-betweenness centrality proposed, performing some comparisons with other classical centrality measures.es
dc.description.sponsorshipThis work was partially supported by Generalitat Valenciana Grant GV2012-111.es
dc.subjectStreet network algorithmses
dc.subjectPageRank algorithmses
dc.subjectCentrality measureses
dc.subjectRandom-walk betweennesses
dc.subjectEigenvector centralityes
dc.subject.otherCiencia de la Computación e Inteligencia Artificiales
dc.titleA new betweenness centrality measure based on an algorithm for ranking the nodes of a networkes
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