Hierarchical Graph Representation Learning with Differentiable Pooling

Code

• github.com/AaltoPML/Rethinking-pooling-in-GNNs
  pytorch★ 62
• github.com/basiralab/RG-Select
  pytorch★ 9
• github.com/basiralab/reproduciblefedgnn
  pytorch★ 9
• github.com/dmlc/dgl/tree/master/examples/pytorch/diffpool
  pytorch★ 0
• github.com/RexYing/graph-pooling
  pytorch★ 0
• github.com/gospodima/extended-simgnn
  pytorch★ 0
• github.com/Tioz90/DiffPool
  tf★ 0
• github.com/Tioz90/GCN
  tf★ 0
• github.com/VoVAllen/diffpool
  pytorch★ 0
• github.com/PasqualeAuriemma/GCN-DIFFPOOL
  tf★ 0

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.

Tasks

Benchmark Results

Reproductions