Hierarchical Graph Representation Learning with Differentiable Pooling

Recently, graph neural networks (GNNs) have revolutionized the field of graph representation learning through effectively learned node embeddings, and achieved state-of-the-art results in tasks such as node classification and link prediction. However, current GNN methods are inherently flat and do not learn hierarchical representations of graphs---a limitation that is especially problematic for the task of graph classification, where the goal is to predict the label associated with an entire graph. Here we propose DiffPool, a differentiable graph pooling module that can generate hierarchical representations of graphs and can be combined with various graph neural network architectures in an end-to-end fashion. DiffPool learns a differentiable soft cluster assignment for nodes at each layer of a deep GNN, mapping nodes to a set of clusters, which then form the coarsened input for the next GNN layer. Our experimental results show that combining existing GNN methods with DiffPool yields an average improvement of 5-10% accuracy on graph classification benchmarks, compared to all existing pooling approaches, achieving a new state-of-the-art on four out of five benchmark data sets.

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Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Graph Classification COLLAB GNN (DiffPool) Accuracy 75.48% # 21
Graph Classification D&D GNN (DiffPool) Accuracy 80.64% # 11
Graph Classification D&D S2V (with 2 DiffPool) Accuracy 82.07% # 6
Graph Classification ENZYMES GNN (DiffPool) Accuracy 62.53% # 12
Graph Classification ENZYMES S2V (with 2 DiffPool) Accuracy 63.33% # 11
Graph Classification PROTEINS GNN (DiffPool) Accuracy 76.25% # 39
Graph Classification REDDIT-MULTI-12K GNN (DiffPool) Accuracy 47.08 # 1

Methods