Node Classification On Pattern 100K
Metriken
Accuracy (%)
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| EGT | 86.816 | Global Self-Attention as a Replacement for Graph Convolution |
| DGN | 86.680 | Directional Graph Networks |
| FactorGCN | 86.57 ± 0.02 | Factorizable Graph Convolutional Networks |
| PNA | 86.567 | Principal Neighbourhood Aggregation for Graph Nets |
| GIN | 85.590 | How Powerful are Graph Neural Networks? |
| MoNet | 85.482 | Geometric deep learning on graphs and manifolds using mixture model CNNs |
| GatedGCN | 84.480 | Residual Gated Graph ConvNets |
| GAT | 75.824 | Graph Attention Networks |
| GraphSage | 50.516 | Inductive Representation Learning on Large Graphs |
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