Node Classification On Cora Fixed 20 Node Per
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| ScaleNet | 82.3±1.1 | Scale Invariance of Graph Neural Networks | - |
| Self-supervised GraphMAE | 84.2 | GraphMAE: Self-Supervised Masked Graph Autoencoders | - |
| Graph InfoClust (GIC) | 81.7 ± 1.5 | Graph InfoClust: Leveraging cluster-level node information for unsupervised graph representation learning | - |
| DSGCN | 84.2 | Bridging the Gap Between Spectral and Spatial Domains in Graph Neural Networks | - |
| SEGCN | 83.5 ± 0.4 | Every Node Counts: Self-Ensembling Graph Convolutional Networks for Semi-Supervised Learning | - |
| SSGC | 83.0 | Simple Spectral Graph Convolution | |
| LDS-GNN | 84.1 | Learning Discrete Structures for Graph Neural Networks | - |
| TREE-G | 83.5 | TREE-G: Decision Trees Contesting Graph Neural Networks | - |
| PairE | - | Graph Representation Learning Beyond Node and Homophily | - |
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