Graph Classification On Reddit B
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| CRaWl | 93.15 | Walking Out of the Weisfeiler Leman Hierarchy: Graph Learning Beyond Message Passing | - |
| WEGL | 92 | Wasserstein Embedding for Graph Learning | - |
| δ-2-LWL | 89.0 | Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings | - |
| GAT-GC (f-Scaled) | 92.57 | Improving Attention Mechanism in Graph Neural Networks via Cardinality Preservation | - |
| NDP | 84.3 | Hierarchical Representation Learning in Graph Neural Networks with Node Decimation Pooling | - |
| Graph-JEPA | 56.73 | Graph-level Representation Learning with Joint-Embedding Predictive Architectures | - |
| GIN-0 | 92.4 | How Powerful are Graph Neural Networks? | - |
| ApproxRepSet | 80.3 | Rep the Set: Neural Networks for Learning Set Representations | - |
| 2-WL-GNN | 89.4 | A Novel Higher-order Weisfeiler-Lehman Graph Convolution | - |
| GraphSAGE | 84.3 | A Fair Comparison of Graph Neural Networks for Graph Classification | - |
| DiffPool | 92.1 | Fast Graph Representation Learning with PyTorch Geometric | - |
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