Hypergraph convolution and hypergraph attention

Recently, graph neural networks have attracted great attention and achieved prominent performance in various research fields. Most of those algorithms have assumed pairwise relationships of objects of in-terest. However, in many real applications, the relationships between objects are in higher-order, beyond a pairwise formulation. To efficiently learn deep embeddings on the high-order graph-structured data, we introduce two end-to-end trainable operators to the family of graph neural networks, i.e., hypergraph convolution and hypergraph attention. Whilst hypergraph convolution defines the basic formulation of performing convolution on a hypergraph, hypergraph attention further enhances the capacity of representation learning by leveraging an attention module. With the two operators, a graph neural network is readily extended to a more flexible model and applied to diverse applications where non-pairwise relationships are observed. Extensive experimental results with semi-supervised node classification demonstrate the effectiveness of hypergraph convolution and hypergraph attention. (c) 2020 Elsevier Ltd. All rights reserved.

Automated summary

Graph neural networks have recently gained significant attention and performance, with the introduction of hypergraph convolution and hypergraph attention operators for efficient learning on high-order graph-structured data. These operators enhance representation learning capacity and enable the extension of graph neural networks to more flexible models for diverse applications with non-pairwise relationships.