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Node Classification On Pubmed Full Supervised
Metrics
Accuracy
Results
Performance results of various models on this benchmark
| Paper Title | Repository | ||
|---|---|---|---|
| GraphSAGE+DropEdge | 91.70% | DropEdge: Towards Deep Graph Convolutional Networks on Node Classification | |
| ASGCN | 90.6% | Adaptive Sampling Towards Fast Graph Representation Learning | |
| FDGATII | 90.3524% | FDGATII : Fast Dynamic Graph Attention with Initial Residual and Identity Mapping | |
| GCNII* | 90.30% | Simple and Deep Graph Convolutional Networks | |
| Graph ESN | 89.2±0.3 | Beyond Homophily with Graph Echo State Networks | - |
| FastGCN | 88.00% | FastGCN: Fast Learning with Graph Convolutional Networks via Importance Sampling | |
| GraphSAGE | 87.1% | Inductive Representation Learning on Large Graphs |
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