Semi-Supervised Classification with Graph Convolutional Networks
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. In a number of experiments on citation networks and on a knowledge graph dataset we demonstrate that our approach outperforms related methods by a significant margin.
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Document ClassificationDrug DiscoveryGeneral ClassificationGraph ClassificationGraph RegressionHeterogeneous Node ClassificationMolecular Property PredictionNode ClassificationNode Classification on Non-Homophilic (Heterophilic) GraphsNode Property PredictionRecommendation SystemsSkeleton Based Action RecognitionMethods 이 논문이 사용한 방법론
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