  Graph neural networks have emerged as a powerful model for graph
representation learning to undertake graph-level prediction tasks. Various
graph pooling methods have been developed to coarsen an input graph into a
succinct graph-level representation through aggregating node embeddings
obtained via graph convolution. However, most graph pooling methods are heavily
node-centric and are unable to fully leverage the crucial information contained
in global graph structure. This paper presents a cross-view graph pooling
(Co-Pooling) method to better exploit crucial graph structure information. The
proposed Co-Pooling fuses pooled representations learnt from both node view and
edge view. Through cross-view interaction, edge-view pooling and node-view
pooling seamlessly reinforce each other to learn more informative graph-level
representations. Co-Pooling has the advantage of handling various graphs with
different types of node attributes. Extensive experiments on a total of 15
graph benchmark datasets validate the effectiveness of our proposed method,
demonstrating its superior performance over state-of-the-art pooling methods on
both graph classification and graph regression tasks.
