Graph Regression On F2
Métriques
R2
RMSE
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | R2 | RMSE | Paper Title | Repository |
|---|---|---|---|---|
| TokenGT | 0.872±0.000 | 0.363±0.872 | Pure Transformers are Powerful Graph Learners | - |
| Graphormer | OOM | OOM | Do Transformers Really Perform Bad for Graph Representation? | - |
| GCN | 0.878±0.000 | 0.355±0.878 | Semi-Supervised Classification with Graph Convolutional Networks | - |
| GIN | 0.887±0.000 | 0.342±0.887 | How Powerful are Graph Neural Networks? | - |
| ESA (Edge set attention, no positional encodings) | 0.891±0.000 | 0.335±0.891 | An end-to-end attention-based approach for learning on graphs | - |
| PNA | 0.891±0.000 | 0.336±0.891 | Principal Neighbourhood Aggregation for Graph Nets | - |
| DropGIN | 0.886±0.000 | 0.343±0.886 | DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks | - |
| GAT | 0.886±0.000 | 0.343±0.886 | Graph Attention Networks | - |
| GATv2 | 0.885±0.000 | 0.344±0.885 | How Attentive are Graph Attention Networks? | - |
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