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/

Zendy Plus

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

Zendy Tools

Zendy Open

Identifying the most influential spreaders that maximize information flow isa central question in network theory. Recently, a scalable method called"Collective Influence (CI)" has been put forward through collective influencemaximization. In contrast to heuristic methods evaluating nodes' significanceseparately, CI method inspects the collective influence of multiple spreaders.Despite that CI applies to the influence maximization problem in percolationmodel, it is still important to examine its efficacy in realistic informationspreading. Here, we examine real-world information flow in various social andscientific platforms including American Physical Society, Facebook, Twitter andLiveJournal. Since empirical data cannot be directly mapped to idealmulti-source spreading, we leverage the behavioral patterns of users extractedfrom data to construct "virtual" information spreading processes. Our resultsdemonstrate that the set of spreaders selected by CI can induce larger scale ofinformation propagation. Moreover, local measures as the number of connectionsor citations are not necessarily the deterministic factors of nodes' importancein realistic information spreading. This result has significance for rankingsscientists in scientific networks like the APS, where the commonly used numberof citations can be a poor indicator of the collective influence of authors inthe community.

Collective Influence of Multiple Spreaders Evaluated by Tracing Real Information Flow in Large-Scale Social Networks