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 Neural Networks (GNNs) with numerical node features and graph structureas inputs have demonstrated superior performance on various supervised learningtasks with graph data. However the numerical node features utilized by GNNs arecommonly extracted from raw data which is of text or tabular(numeric/categorical) type in most real-world applications. The best models forsuch data types in most standard supervised learning settings with IID(non-graph) data are not simple neural network layers and thus are not easilyincorporated into a GNN. Here we propose a robust stacking framework that fusesgraph-aware propagation with arbitrary models intended for IID data, which areensembled and stacked in multiple layers. Our layer-wise framework leveragesbagging and stacking strategies to enjoy strong generalization, in a mannerwhich effectively mitigates label leakage and overfitting. Across a variety ofgraph datasets with tabular/text node features, our method achieves comparableor superior performance relative to both tabular/text and graph neural networkmodels, as well as existing state-of-the-art hybrid strategies that combine thetwo.

A Robust Stacking Framework for Training Deep Graph Models with Multifaceted Node Features