Hypergraph convolution and hypergraph attention

Highlights：

• Apart from hypergraph convolution where the underlying structure used for propagation is pre defined, hypergraph attention further exerts an attention mechanism to learn a dynamic connection of hyperedges. Then, the information propagation and gathering is done in task relevant parts of the graph, thereby generating more discriminative node embeddings.

• Both hypergraph convolution and hypergraph attention are end to end trainable, and can be inserted into most variants of graph neural networks as long as non pairwise relationships are observed. Extensive experimental results on benchmark datasets demonstrate the efficacy of the proposed methods for semi supervised node classification.

摘要

•Hypergraph convolution defines a basic convolutional operator in a hypergraph. It enables an efficient information propagation between vertices by fully exploiting the high order relationship and local clustering structure therein. We mathematically prove that graph convolution is a special case of hypergraph convolution when the non pairwise relationship degenerates to a pairwise one.•Apart from hypergraph convolution where the underlying structure used for propagation is pre defined, hypergraph attention further exerts an attention mechanism to learn a dynamic connection of hyperedges. Then, the information propagation and gathering is done in task relevant parts of the graph, thereby generating more discriminative node embeddings.•Both hypergraph convolution and hypergraph attention are end to end trainable, and can be inserted into most variants of graph neural networks as long as non pairwise relationships are observed. Extensive experimental results on benchmark datasets demonstrate the efficacy of the proposed methods for semi supervised node classification.