Semi-Supervised Classification with Graph Convolutional Networks

9 Sep 2016Thomas N. Kipf • Max Welling

We present a scalable approach for semi-supervised learning on graph-structured data that is based on an efficient variant of convolutional neural networks which operate directly on graphs. We motivate the choice of our convolutional architecture via a localized first-order approximation of spectral graph convolutions. Our model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes.

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Evaluation


Task Dataset Model Metric name Metric value Global rank Compare
Node Classification Citeseer GCN Accuracy 70.3% # 6
Node Classification Cora GCN Accuracy 81.5% # 4
Node Classification NELL GCN Accuracy 66.0% # 1
Node Classification Pubmed GCN Accuracy 79.0% # 5