SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
Abstract 本文提出了一种基于卷积神经网络的可扩展半监督学习方法，该方法可以直接对图进行学习。我们通过图卷积的局部一阶近似来进行卷积结构的选择。我们的模型在图边缘数量上线性扩展，并且学习了编码局部图结构和节点特征的隐层表示。在引用的网络和知识图数据集上的大量实验表明，我们的方法比相关方法有显著的优势。 1、Introduction 我们考虑在图(如引用网络)中
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相关文章
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- 2. Semi-Supervised Classification with Graph Convolutional Networks
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- 9. 《ImageNet Classification with Deep Convolutional Neural Network》
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