Title: Hierarchical Graph Representation Learning with Differentiable Pooling,
Abstract: 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.