  Graph Neural Network (GNN) research has concentrated on improving
convolutional layers, with little attention paid to developing graph pooling
layers. Yet pooling layers can enable GNNs to reason over abstracted groups of
nodes instead of single nodes. To close this gap, we propose a graph pooling
layer relying on the notion of edge contraction: EdgePool learns a localized
and sparse hard pooling transform. We show that EdgePool outperforms
alternative pooling methods, can be easily integrated into most GNN models, and
improves performance on both node and graph classification.
