HyperGCN: A New Method of Training Graph Convolutional Networks on Hypergraphs--論文筆記
論文題目–《超圖卷積網絡–一種新的方法用於訓練超圖的圖卷積網絡》 摘要 在許多現實世界的網絡中，其實有很多關係都不僅僅是成對的關係，比如co-authorship, co-citation網絡，其中的關係非常的複雜。超圖提供了一種靈活且自然的工具去模擬諸如以上網絡的複雜關係。以上存在的複雜的關係刺激了使用超圖學習的問題。目前一種流行的方法是基於超圖的半監督學習，其目標是分配標籤給超圖中沒有標籤的節
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相關文章
- 1. Self-supervised Training of Graph Convolutional Networks
- 2. 論文筆記：LCML 2019 Simplifying Graph Convolutional Networks
- 3. Modification of Graph Convolutional Networks in PyTorch
- 4. HyperGCN
- 5. 論文筆記：DROPEDGE TOWARDS DEEP GRAPH CONVOLUTIONAL NETWORKS ON NODE CLASSIFICATION
- 6. [論文筆記]Cluster-GCN: An Efficient Algorithm for Training Deep and Large Graph Convolutional Networks
- 7. A New Hand Segmentation Method Based on Fully Convolutional Network
- 8. 論文筆記之：Semi-supervised Classification with Graph Convolutional Networks
- 9. 【論文筆記】Deep Learning on Graphs: A Survey
- 10. 【論文閱讀筆記】Ristretto： Hardware-Oriented Approximation of Convolutional Neural Networks