Semi-Supervised Classification with Graph Convolutional Networks

9 Sep 2016Thomas N. KipfMax 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... (read more)

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Evaluation results from the paper


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