Node Classification On Pubmed Full Supervised
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| inductive-representation-learning-on-large | 87.1% |
| fdgatii-fast-dynamic-graph-attention-with | 90.3524% |
| adaptive-sampling-towards-fast-graph | 90.6% |
| simple-and-deep-graph-convolutional-networks-1 | 90.30% |
| beyond-homophily-with-graph-echo-state-1 | 89.2±0.3 |
| fastgcn-fast-learning-with-graph | 88.00% |
| the-truly-deep-graph-convolutional-networks | 91.70% |