Node Classification On Cora Full Supervised
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| FDGATII | 87.7867% | FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping | |
| FastGCN | 85.00% | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | |
| GCNII | 88.49% | Simple and Deep Graph Convolutional Networks | |
| ASGCN | 87.44±0.0034% | Adaptive Sampling Towards Fast Graph Representation Learning | |
| NCGCN | 73.42 ± 0.58% | Clarify Confused Nodes via Separated Learning | |
| GraphMix (GCN) | 61.8% | GraphMix: Improved Training of GNNs for Semi-Supervised Learning | |
| Graph ESN | 86.0±1.0 | Beyond Homophily with Graph Echo State Networks | - |
| GraphSAGE | 82.2% | Inductive Representation Learning on Large Graphs | |
| IncepGCN+DropEdge | 88.2% | DropEdge: Towards Deep Graph Convolutional Networks on Node Classification |
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