Graph Convolutional Network (GCN)

A Graph Convolutional Network, or GCN, is an approach for semi-supervised learning on graph-structured data. It is based on an efficient variant of convolutional neural networks which operate directly on graphs. The choice of convolutional architecture is motivated via a localized first-order approximation of spectral graph convolutions. The model scales linearly in the number of graph edges and learns hidden layer representations that encode both local graph structure and features of nodes. Introduced by Kipf et al. in Semi-Supervised Classification with Graph Convolutional Networks
相关学科: Graph Convolutional NetworkNode ClassificationSkeleton Based Action RecognitionGATGraphSAGEGraph LearningGraph ClassificationGraph AttentionLink PredictionGraph Representation Learning









Rob Knight

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Albert-László Barabási

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Jian Sun

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Paul M. Thompson

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Ramnik J. Xavier

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Jiawei Han

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Myrna M. Weissman

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Jürgen Schmidhuber

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Jian Yang

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Neil Gehrels

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