PageRank centrality and algorithms for weighted, directed networks with applications to World Input-Output Tables

TL;DR

This paper introduces a weighted PageRank measure for directed networks, including an efficient R algorithm, and demonstrates its effectiveness on simulated data and real-world economic networks from World Input-Output Tables.

Contribution

It extends classical PageRank to weighted, directed networks with a tunable parameter and provides an efficient R implementation, outperforming existing measures.

Findings

WPR outperforms other measures on simulated networks.

WPR results align with global economic trends.

Efficient R algorithm enables large-scale network analysis.

Abstract

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.