Graph Regression On Zinc 100K
Metrics
MAE
Results
Performance results of various models on this benchmark
Model Name | MAE | Paper Title | Repository |
|---|---|---|---|
| GatedGCN | 0.363 | Benchmarking Graph Neural Networks | - |
| MPNN | 0.288 | Neural Message Passing for Quantum Chemistry | - |
| MoNet | 0.407 | Geometric deep learning on graphs and manifolds using mixture model CNNs | - |
| CIN-small | 0.094 | Weisfeiler and Lehman Go Cellular: CW Networks | - |
| EGT | 0.143 | Global Self-Attention as a Replacement for Graph Convolution | - |
| GAT | 0.463 | Graph Attention Networks | - |
| ARGNP | 0.136 | Automatic Relation-aware Graph Network Proliferation | - |
| GSN | 0.115 | Improving Graph Neural Network Expressivity via Subgraph Isomorphism Counting | - |
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