SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
Abstract 本文提出了一種基於卷積神經網絡的可擴展半監督學習方法，該方法可以直接對圖進行學習。我們通過圖卷積的局部一階近似來進行卷積結構的選擇。我們的模型在圖邊緣數量上線性擴展，並且學習了編碼局部圖結構和節點特徵的隱層表示。在引用的網絡和知識圖數據集上的大量實驗表明，我們的方法比相關方法有顯著的優勢。 1、Introduction 我們考慮在圖(如引用網絡)中
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相關文章
- 1. Semi-Supervised Classification With Graph Convolutional Networks
- 2. Semi-Supervised Classification with Graph Convolutional Networks
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- 5. Graph Convolutional Networks for Text Classification
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- 7. Structural Image Classification with Graph Neural Networks
- 8. ImageNet Classification with Deep Convolutional Neural Networks
- 9. 《ImageNet Classification with Deep Convolutional Neural Network》
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