Node Classification On Citeseer Full
Métriques
Accuracy
Résultats
Résultats de performance de divers modèles sur ce benchmark
Nom du modèle | Accuracy | Paper Title | Repository |
|---|---|---|---|
| ASGCN | 79.66% | Adaptive Sampling Towards Fast Graph Representation Learning | |
| GraphSAGE | 71.40% | Inductive Representation Learning on Large Graphs | |
| GCNII* | 77.13% | Simple and Deep Graph Convolutional Networks | |
| IncepGCN+DropEdge | 80.50% | DropEdge: Towards Deep Graph Convolutional Networks on Node Classification | |
| Graph ESN | 74.5±2.1 | Beyond Homophily with Graph Echo State Networks | - |
| FDGATII | 75.6434% | FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping | |
| FastGCN | 77.60% | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling |
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