  While graph neural networks (GNNs) have been successful for node
classification tasks and link prediction tasks in graph, learning graph-level
representations 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 pooling
methods have been developed for this goal. However, most of the existing
pooling methods utilize k-hop neighborhood without considering explicit
structural information in a graph. In this paper, we propose Structure
Prototype Guided Pooling (SPGP) that utilizes prior graph structures to
overcome the limitation. SPGP formulates graph structures as learnable
prototype vectors and computes the affinity between nodes and prototype
vectors. This leads to a novel node scoring scheme that prioritizes informative
nodes while encapsulating the useful structures of the graph. Our experimental
results show that SPGP outperforms state-of-the-art graph pooling methods on
graph classification benchmark datasets in both accuracy and scalability.
