论文笔记：SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
前言 本文提出了一种可扩展的基于图数据结构的半监督学习方法，该方法基于一个有效的卷积神经网络变形，这种变形能够直接对图进行操作（卷积层变为了图卷积层）。图结构的数据在现实应用中非常常见，比如: Social Networks, Citation Netwoks, Knowledge Graphs等。 图半监督学习的setting是，在给定的图结构的数据中，只有少部分节点是有标记的，大部分节点是未标
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相关文章
- 1. 论文笔记：Semi-Supervised Classification with Graph Convolutional Networks
- 2. 【GCN】论文笔记：SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
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- 4. Semi-Supervised Classification With Graph Convolutional Networks
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- 7. 论文笔记：LCML 2019 Simplifying Graph Convolutional Networks
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- 9. 论文笔记：AAAI 2019 Graph Convolutional Networks for Text Classification
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