Self-attention Hypergraph Pooling Network

Recently, Graph Convolutional neural Networks (GCNs) have attracted much attention by generalizing convolutional neural networks to graph data, which includes redefining convolution and pooling operations on graphs. Due to the limitation that graph data can only focus on dyadic relations, it cannot perform well in real practice. In contrast, a hypergraph can capture high-order data interaction and is easy to deal with complex data representation using its flexible hyperedges. However, the existing methods for hypergraph convolutional networks are still not mature, and there is no effective operation for hypergraph pooling currently. Therefore, a hypergraph pooling network with a self-attention mechanism is proposed. Using a hypergraph structure for data modeling, this model can learn node hidden features with high-order data information through hypergraph convolution operation which introduces a self-attention mechanism, select important nodes both on structure and content through hypergraph pooling operation, and then obtain more accurate hypergraph representation. Experiments on text classification, dish classification, and protein classification tasks show that the proposed method outperforms recent state-of-the-art methods.