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

Recently, graph neural networks have attracted great attention and achievedprominent performance in various research fields. Most of those algorithms haveassumed pairwise relationships of objects of interest. However, in many realapplications, the relationships between objects are in higher-order, beyond apairwise formulation. To efficiently learn deep embeddings on the high-ordergraph-structured data, we introduce two end-to-end trainable operators to thefamily of graph neural networks, i.e., hypergraph convolution and hypergraphattention. Whilst hypergraph convolution defines the basic formulation ofperforming convolution on a hypergraph, hypergraph attention further enhancesthe capacity of representation learning by leveraging an attention module. Withthe two operators, a graph neural network is readily extended to a moreflexible model and applied to diverse applications where non-pairwiserelationships are observed. Extensive experimental results with semi-supervisednode classification demonstrate the effectiveness of hypergraph convolution andhypergraph attention.

Hypergraph Convolution and Hypergraph Attention