論文筆記：SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
前言 本文提出了一種可擴展的基於圖數據結構的半監督學習方法，該方法基於一個有效的卷積神經網絡變形，這種變形能夠直接對圖進行操作（卷積層變爲了圖卷積層）。圖結構的數據在現實應用中非常常見，比如: Social Networks, Citation Netwoks, Knowledge Graphs等。 圖半監督學習的setting是，在給定的圖結構的數據中，只有少部分節點是有標記的，大部分節點是未標
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相關文章
- 1. 論文筆記：Semi-Supervised Classification with Graph Convolutional Networks
- 2. 【GCN】論文筆記：SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
- 3. 論文筆記之：Semi-supervised Classification with Graph Convolutional Networks
- 4. Semi-Supervised Classification With Graph Convolutional Networks
- 5. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
- 6. Semi-Supervised Classification with Graph Convolutional Networks
- 7. 論文筆記：LCML 2019 Simplifying Graph Convolutional Networks
- 8. 論文閱讀筆記：GAS---spam review detection with graph convolutional networks
- 9. 論文筆記：AAAI 2019 Graph Convolutional Networks for Text Classification
- 10. 【論文閱讀筆記】Graph Convolutional Networks for Text Classification