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While graph neural networks (GNNs) have been successful for nodeclassification tasks and link prediction tasks in graph, learning graph-levelrepresentations still remains a challenge. For the graph-level representation,it is important to learn both representation of neighboring nodes, i.e.,aggregation, and graph structural information. A number of graph poolingmethods have been developed for this goal. However, most of the existingpooling methods utilize k-hop neighborhood without considering explicitstructural information in a graph. In this paper, we propose StructurePrototype Guided Pooling (SPGP) that utilizes prior graph structures toovercome the limitation. SPGP formulates graph structures as learnableprototype vectors and computes the affinity between nodes and prototypevectors. This leads to a novel node scoring scheme that prioritizes informativenodes while encapsulating the useful structures of the graph. Our experimentalresults show that SPGP outperforms state-of-the-art graph pooling methods ongraph classification benchmark datasets in both accuracy and scalability.

SPGP: Structure Prototype Guided Graph Pooling