  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.
