【GNN實驗新基準】Benchmarking Graph Neural Networks
Yoshua Bengio等圖神經網絡的新基準Benchmarking Graph Neural Networks(代碼已開源) 轉載 AIGraph 深度學習與圖網絡 該論文作者提出了一個基準框架，以促進圖神經網絡的研究，並解決文獻中的實驗不一致問題。論文確認目前普遍使用的小型TU數據集不適合研究該領域模型，並在框架內引入六個中等規模的數據集。對圖形的多個任務進行的實驗表明：i）隨着轉向更大的
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相關文章
- 1. Graph Neural Networks (GNN)（三）：Spectral-GNN 之 GCN
- 2. Applications of Graph Neural Networks
- 3. GMNN: Graph Markov Neural Networks
- 4. Graph-based Dependency Parsing with Graph Neural Networks
- 5. HOW POWERFUL ARE GRAPH NEURAL NETWORKS?
- 6. How powerful are graph neural networks?
- 7. 筆記《Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels》-NeurIPS 2019
- 8. Overlapping Community Detection with Graph Neural Networks
- 9. Strategies For Pre-Training Graph Neural Networks
- 10. Graph Neural Networks (GNN)（二）：Spectral-GNN 引言和導入