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data data May 1, 2018
gen data fixes May 12, 2018
.gitignore fix dataloader for -1/+1 graph label datasets Feb 8, 2019
LICENSE Initial commit Mar 12, 2018 Create Feb 8, 2019 graphsage Mar 13, 2018 val script Oct 15, 2018 modulelist May 31, 2019 Update Mar 14, 2019 data generation Mar 13, 2018 val script Oct 15, 2018 train Mar 14, 2018 fix dataloader for -1/+1 graph label datasets Feb 8, 2019 max nodes Apr 30, 2018 set2set base May 14, 2018 modulelist May 31, 2019 visualize performance May 3, 2018


This is the repo for Hierarchical Graph Representation Learning with Differentiable Pooling (NeurIPS 2018)

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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