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

Measuring the influence of users in social networks is key for numerousapplications. A recently proposed influence metric, coined as $\psi$-score,allows to go beyond traditional centrality metrics, which only assessstructural graph importance, by further incorporating the rich informationprovided by the posting and re-posting activity of users. The $\psi$-score isshown in fact to generalize PageRank for non-homogeneous node activity. Despiteits significance, it scales poorly to large datasets; for a network of $N$users, it requires to solve $N$ linear systems of equations of size $N$. Toaddress this problem, this work introduces a novel scalable algorithm for thefast approximation of $\psi$-score, named \textit{Power}-$\psi$. The proposedalgorithm is based on a novel equation indicating that it suffices to solve onesystem of equations of size $N$ to compute the $\psi$-score. Then, ouralgorithm exploits the fact that such a system can be recursively anddistributedly approximated to any desired error. This permits the $\psi$-score,summarizing both structural and behavioral information for the nodes, to run asfast as PageRank. We validate the effectiveness of the proposed algorithm,which we release as an open source Python library, on several real-worlddatasets.

A Fast Algorithm for Ranking Users by their Influence in Online Social Platforms