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

被引：16

作者：

Curado, Manuel ^{[1
]}

Tortosa, Leandro ^{[2
]}

Vicent, Jose F. ^{[2
]}

Yeghikyan, Gevorg ^{[3
]}

机构：

[1] Catholic Univ Murcia, Polytech Sch, Campus Los Jeronimos S-N, E-30107 Murcia, Spain

[2] Univ Alicante, Dept Comp Sci & Artificial Intelligence, Campus San Vicente,Ap Correos 99, E-03080 Alicante, Spain

[3] Scuola Normale Super Pisa, Fac Sci, Piazza Cavalieri 7, I-56126 Pisa, PI, Italy

来源：

JOURNAL OF COMPUTATIONAL SCIENCE | 2020年 / 43卷 / 43期

关键词：

Centrality measures; Urban networks; Vertex importance; Eigenvector centrality; ALGORITHM; RANKING; STREETS; NODES;

D O I：

10.1016/j.jocs.2020.101127

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

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). (C) 2020 Elsevier B.V. All rights reserved.

引用

页数：11

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