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

Graph-structured data and their related algorithms have attracted significantattention in many fields, such as influenza prediction in public health.However, the variable influenza seasonality, occasional pandemics, and domainknowledge pose great challenges to construct an appropriate graph, which couldimpair the strength of the current popular graph-based algorithms to performdata analysis. In this study, we develop a novel method, Dynamic Virtual GraphSignificance Networks (DVGSN), which can supervisedly and dynamically learnfrom similar "infection situations" in historical timepoints. Representationlearning on the dynamic virtual graph can tackle the varied seasonality andpandemics, and therefore improve the performance. The extensive experiments onreal-world influenza data demonstrate that DVGSN significantly outperforms thecurrent state-of-the-art methods. To the best of our knowledge, this is thefirst attempt to supervisedly learn a dynamic virtual graph for time-seriesprediction tasks. Moreover, the proposed method needs less domain knowledge tobuild a graph in advance and has rich interpretability, which makes the methodmore acceptable in the fields of public health, life sciences, and so on.

Dynamic Virtual Graph Significance Networks for Predicting Influenza