Node Classification On Pokec
Metriken
Accuracy
Ergebnisse
Leistungsergebnisse verschiedener Modelle zu diesem Benchmark
| Paper Title | ||
|---|---|---|
| NeuralWalker | 86.46 ± 0.09 | Learning Long Range Dependencies on Graphs via Random Walks |
| GCN | 86.33 ± 0.17 | Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification |
| Polynormer | 86.10±0.05 | Polynormer: Polynomial-Expressive Graph Transformer in Linear Time |
| GloGNN++ | 83.05±0.07 | Finding Global Homophily in Graph Neural Networks When Meeting Heterophily |
| OptBasisGNN | 82.83±0.04 | Graph Neural Networks with Learnable and Optimal Polynomial Bases |
| LINKX | 82.04±0.07 | Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods |
| Dual-Net GNN | 81.55±0.09 | Feature Selection: Key to Enhance Node Classification with Graph Neural Networks |
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