Convolutional Networks on Graphs for Learning Molecular Fingerprints

NeurIPS 2015 David DuvenaudDougal MaclaurinJorge Aguilera-IparraguirreRafael Gómez-BombarelliTimothy HirzelAlán Aspuru-GuzikRyan P. Adams

We introduce a convolutional neural network that operates directly on graphs. These networks allow end-to-end learning of prediction pipelines whose inputs are graphs of arbitrary size and shape... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Node Classification CiteSeer (0.5%) GCN-FP Accuracy 43.9% # 10
Node Classification CiteSeer (1%) GCN-FP Accuracy 54.3% # 11
Node Classification CiteSeer with Public Split: fixed 20 nodes per class GCN-FP Accuracy 61.5% # 16
Node Classification Cora (0.5%) GCN-FP Accuracy 50.5% # 9
Node Classification Cora (1%) GCN-FP Accuracy 59.6% # 10
Node Classification Cora (3%) GCN-FP Accuracy 71.7% # 11
Node Classification Cora with Public Split: fixed 20 nodes per class GCN-FP Accuracy 74.6% # 15
Drug Discovery HIV dataset GraphConv AUC 0.822 # 2
Graph Regression Lipophilicity GC RMSE 0.655 # 2
Drug Discovery MUV GraphConv AUC 0.836 # 2
Drug Discovery PCBA GraphConv AUC 0.855 # 2
Node Classification PubMed (0.03%) GCN-FP Accuracy 56.2% # 9
Node Classification PubMed (0.05%) GCN-FP Accuracy 63.2% # 10
Node Classification PubMed (0.1%) GCN-FP Accuracy 70.3% # 10
Node Classification PubMed with Public Split: fixed 20 nodes per class GCN-FP Accuracy 76.0% # 12
Drug Discovery Tox21 GraphConv AUC 0.846 # 4
Drug Discovery ToxCast GraphConv AUC 0.754 # 2