English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

PageRank (PR) is a fundamental tool for assessing the relative importance ofthe nodes in a network. In this paper, we propose a measure, weighted PageRank(WPR), extended from the classical PR for weighted, directed networks withpossible non-uniform node-specific information that is dependent or independentof network structure. A tuning parameter leveraging node degree and strength isintroduced. An efficient algorithm based on R program has been developed forcomputing WPR in large-scale networks. We have tested the proposed WPR onwidely used simulated network models, and found it outperformed other competingmeasures in the literature. By applying the proposed WPR to the real networkdata generated from World Input-Output Tables, we have seen the results thatare consistent with the global economic trends, which renders it a preferredmeasure in the analysis.

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