期刊
PHYSICA A-STATISTICAL MECHANICS AND ITS APPLICATIONS
卷 586, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.physa.2021.126438
关键词
Node centrality; Weighted directed networks; Weighted PageRank; World Input-Output Tables
In this paper, a measure called Weighted PageRank (WPR) is proposed for weighted, directed networks, with a tuning parameter leveraging node degree and strength, which shows superior performance compared to the classical PR in both simulated network models and real network data. The efficient algorithm developed using R program allows for computation of WPR in large-scale networks.
PageRank (PR) is a fundamental tool for assessing the relative importance of the nodes in a network. In this paper, we propose a measure, weighted PageRank (WPR), extended from the classical PR for weighted, directed networks with possible non-uniform node-specific information that is dependent or independent of network structure. A tuning parameter leveraging node degree and strength is introduced. An efficient algorithm based on R program has been developed for computing WPR in large-scale networks. We have tested the proposed WPR on widely used simulated network models, and found it outperformed the classical PR. Additionally, we apply the proposed WPR to the real network data generated from World Input-Output Tables as an example, and have seen the results that are consistent with the global economic trends, which renders it a preferred measure in the analysis. (C) 2021 Elsevier B.V. All rights reserved.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据