Hypergraph Convolution and Hypergraph Attention
This addresses a limitation in graph neural networks for applications with non-pairwise relationships, though it is an incremental extension of existing methods.
The paper tackles the problem of modeling higher-order relationships beyond pairwise connections in graph neural networks by introducing hypergraph convolution and hypergraph attention operators, achieving effective results in semi-supervised node classification tasks.
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 interest. 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.