  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.
