論文筆記：LCML 2020 Simple and Deep Graph Convolutional Networks
前言 圖卷積網絡(GCNs)是一種針對圖結構數據的強大的深度學習方法。最近，GCNs和後續的變體在實際數據集中的各種應用領域顯示了優越的性能。大多數目前的GCN模型是淺層結構的，由於過度平滑的問題。本文研究了深度圖卷積網絡的設計與分析問題，提出了GCNII，它是普通GCN模型的擴展，具有兩種簡單而有效的技術:初始殘差和恆等映射。這兩種技術有效地緩解了過度平滑的問題。實驗表明，深層GCNII模型在各
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相關文章
- 1. 論文筆記：LCML 2019 Simplifying Graph Convolutional Networks
- 2. 《Simple and Deep Graph Convolutional Networks》--論文閱讀筆記
- 3. 【論文閱讀筆記】Simple and Deep Graph Convolutional Networks
- 4. 論文筆記：DROPEDGE TOWARDS DEEP GRAPH CONVOLUTIONAL NETWORKS ON NODE CLASSIFICATION
- 5. [論文筆記]Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- 6. [論文筆記] [2014] Spectral Networks and Deep Locally Connected Networks on Graphs
- 7. 論文筆記Visualizing and understanding convolutional networks
- 8. 論文筆記：Visualizing and Understanding Convolutional Networks
- 9. 【論文筆記】Graph Transformer Networks
- 10. 論文筆記：Spectral Networks and Deep Locally Connected Networks on Graphs