Node Classification On Citeseer 05
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
Vergleichstabelle
| Modellname | Accuracy |
|---|---|
| break-the-ceiling-stronger-multi-scale-deep | 61.99% |
| lanczosnet-multi-scale-deep-graph | 53.2 ± 4.0 |
| gated-graph-convolutional-recurrent-neural | 44.3% |
| break-the-ceiling-stronger-multi-scale-deep | 62.05% |
| inductive-representation-learning-on-large | 33.8% |
| mutual-teaching-for-graph-convolutional | 67.7% |
| convolutional-networks-on-graphs-for-learning | 43.9% |
| diffusion-convolutional-neural-networks | 53.1% |
| view-consistent-heterogeneous-network-on | 65.6% |
| lanczosnet-multi-scale-deep-graph | 53.8 ± 4.7 |
| convolutional-neural-networks-on-graphs-with | 45.3% |
| break-the-ceiling-stronger-multi-scale-deep | 64.64% |
| graph-attention-networks | 38.2% |
| break-the-ceiling-stronger-multi-scale-deep | 59.41% |