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

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Title: Analysis and comparison of centrality measures applied to urban networks with data
Authors: Curado, Manuel | Tortosa, Leandro | Vicent, Jose F. | Yeghikyan, Gevorg
Research Group/s: Análisis y Visualización de Datos en Redes (ANVIDA)
Center, Department or Service: Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Centrality measures | Urban networks | Vertex importance | Eigenvector centrality
Knowledge Area: Ciencia de la Computación e Inteligencia Artificial
Issue Date: May-2020
Publisher: Elsevier
Citation: Journal of Computational Science. 2020, 43: 101127. doi:10.1016/j.jocs.2020.101127
Abstract: For 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).
ISSN: 1877-7503 (Print) | 1877-7511 (Online)
DOI: 10.1016/j.jocs.2020.101127
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2020 Elsevier B.V.
Peer Review: si
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Appears in Collections:INV - ANVIDA - Artículos de Revistas

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