MuchGCN：Multi-Channel Graph Convolutional Networks

時間 2020-12-24

標籤圖神經網絡深度學習简体版

原文原文鏈接

這篇文章在DiffPool的基礎上引入了多通道（Multi-channel）的概念，提出了multi channel graph convolutional networks (MuchGCN)，通過inter-channel和intra-channel兩種方式聚合通道上的信息。本文被IJCAI2020接收，在寫這篇博客時我看的是2019年12月掛到arXiv上的版本，整體感覺有點粗糙，被會議接

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1. Adaptive Graph Convolutional Neural Networks
2. Semi-Supervised Classification With Graph Convolutional Networks
3. Geom-GCN: Geometric Graph Convolutional Networks
4. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
5. Knowledge Graph Convolutional Networks for Recommender Systems
6. Graph Convolutional Networks for Text Classification
7. Emotion Recognition Using Graph Convolutional Networks
8. 《Large-Scale Learnable Graph Convolutional Networks》
9. Semi-Supervised Classification with Graph Convolutional Networks
10. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS
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1. Adaptive Graph Convolutional Neural Networks
2. Semi-Supervised Classification With Graph Convolutional Networks
3. Geom-GCN: Geometric Graph Convolutional Networks
4. SEMI-SUPERVISED CLASSIFICATION WITH GRAPH CONVOLUTIONAL NETWORKS
5. Knowledge Graph Convolutional Networks for Recommender Systems
6. Graph Convolutional Networks for Text Classification
7. Emotion Recognition Using Graph Convolutional Networks
8. 《Large-Scale Learnable Graph Convolutional Networks》
9. Semi-Supervised Classification with Graph Convolutional Networks
10. IMAGE DENOISING WITH GRAPH-CONVOLUTIONAL NEURAL NETWORKS

>>更多相關文章<<