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