| Paper | Code | Accuracy | ModelName | ReleaseDate |
|---|---|---|---|---|
| Learning Long Range Dependencies on Graphs via Random Walks | ✓ Link | 86.46 ± 0.09 | NeuralWalker | 2024-06-05 |
| Classic GNNs are Strong Baselines: Reassessing GNNs for Node Classification | ✓ Link | 86.33 ± 0.17 | GCN | 2024-06-13 |
| Polynormer: Polynomial-Expressive Graph Transformer in Linear Time | ✓ Link | 86.10±0.05 | Polynormer | 2024-03-02 |
| Finding Global Homophily in Graph Neural Networks When Meeting Heterophily | ✓ Link | 83.05±0.07 | GloGNN++ | 2022-05-15 |
| Graph Neural Networks with Learnable and Optimal Polynomial Bases | ✓ Link | 82.83±0.04 | OptBasisGNN | 2023-02-24 |
| Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods | ✓ Link | 82.04±0.07 | LINKX | 2021-10-27 |
| Feature Selection: Key to Enhance Node Classification with Graph Neural Networks | ✓ Link | 81.55±0.09 | Dual-Net GNN | 2023-01-25 |