GraphSAINT: Graph Sampling Based Inductive Learning Method
GraphSAINT: Graph Sampling Based Inductive Learning Method 1、背景 Layer Sampling： Graph Sampling: 2、GraphSAINT 2.1 算法流程 2.1.1 normalization techniques to eliminate biases 2.1.2 minibatch loss 2.1.3 VARI
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相關文章
- 1. Inductive Matrix Completion Based on Graph Neural Networks
- 2. Inductive Representation Learning on Large Graphs
- 3. Relational inductive biases, deep learning, and graph networks
- 4. Paper Notes: Inductive Representation Learning on Large Graphs
- 5. sampling method
- 6. Learning about Graph Network
- 7. 論文筆記：LCLR 2018 FastGCN: Fast Learning With Graph Convolutional Networks Via Important Sampling
- 8. GNN新作《Relational inductive biases，deep learning，and graph networks》讀書筆記
- 9. 《Relational inductive biases, deep learning, and graph networks》論文解讀（轉載）
- 10. 論文理解—— Disentangle-based Continual Graph Representation Learning