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

机译:数据应用于城市网络与数据的分析与比较

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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.
机译:对于相当长的时间,研究人员专注于定义不同措施,能够表征网络中顶点的重要性。其中一种类型的这些网络,城市是一种复杂的系统,可以产生大量信息。这些数据是城市网络本身特征的重要组成部分。因此,考虑我们在城市本身可以找到的数据,拥有一个分类系统是至关重要的。为了解决这个问题,本文研究并比较了几个专门应用于城市网络的中心措施。这些中心基于计算矩阵的特征向量,非常适合与数据的城市网络。旨在扩大这些措施所涵盖的范围,提出了一种新的中心度量。最后,我们通过关于罗马市(意大利)的真实网络和真实数据比较三个集合。 (c)2020 Elsevier B.v.保留所有权利。

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