  Graph Neural Networks (GNNs), which generalize deep neural networks to
graph-structured data, have drawn considerable attention and achieved
state-of-the-art performance in numerous graph related tasks. However, existing
GNN models mainly focus on designing graph convolution operations. The graph
pooling (or downsampling) operations, that play an important role in learning
hierarchical representations, are usually overlooked. In this paper, we propose
a novel graph pooling operator, called Hierarchical Graph Pooling with
Structure Learning (HGP-SL), which can be integrated into various graph neural
network architectures. HGP-SL incorporates graph pooling and structure learning
into a unified module to generate hierarchical representations of graphs. More
specifically, the graph pooling operation adaptively selects a subset of nodes
to form an induced subgraph for the subsequent layers. To preserve the
integrity of graph's topological information, we further introduce a structure
learning mechanism to learn a refined graph structure for the pooled graph at
each layer. By combining HGP-SL operator with graph neural networks, we perform
graph level representation learning with focus on graph classification task.
Experimental results on six widely used benchmarks demonstrate the
effectiveness of our proposed model.
