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 other competing measures in the literature. By applying the proposed WPR to the real network data generated from World Input-Output Tables, we have seen the results that are consistent with the global economic trends, which renders it a preferred measure in the analysis.
翻译:PageRank(PageRank)是评估节点在网络中的相对重要性的基本工具。 在本文中,我们提出了一个从传统PR扩展为加权的加权PageRank(WPR)的措施,即加权PageRank(WPR),用于使用依赖或独立于网络结构的可能非统一节点特定信息的加权定向网络。引入了调制参数,以节点和强度为杠杆,根据R程序开发了一种高效算法,用于在大型网络中计算WPPR。我们用广泛使用的模拟网络模型测试了拟议的WPR,发现它优于文献中的其他竞争措施。我们通过将拟议的WPR应用于从世界输入输出表中产生的真实网络数据,我们看到了与全球经济趋势相一致的结果,从而使它成为了分析中的首选措施。