| Paper | Code | Accuracy | ModelName | ReleaseDate |
|---|---|---|---|---|
| CKGConv: General Graph Convolution with Continuous Kernels | ✓ Link | 88.661 | CKGCN | 2024-04-21 |
| Graph Inductive Biases in Transformers without Message Passing | ✓ Link | 87.196 | GRIT | 2023-05-27 |
| Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence | ✓ Link | 87.029 ± 0.037 | GatedGCN+ | 2025-02-13 |
| Learning Long Range Dependencies on Graphs via Random Walks | ✓ Link | 86.977 ± 0.012 | NeuralWalker | 2024-06-05 |
| Global Self-Attention as a Replacement for Graph Convolution | ✓ Link | 86.821 | EGT | 2021-08-07 |
| Exphormer: Sparse Transformers for Graphs | ✓ Link | 86.74 | Exphormer | 2023-03-10 |
| Graph Transformers without Positional Encodings | 86.738 | EIGENFORMER | 2024-01-31 | |
| Graph Propagation Transformer for Graph Representation Learning | ✓ Link | 86.734±0.008 | GPTrans-Nano | 2023-05-19 |
| Recipe for a General, Powerful, Scalable Graph Transformer | ✓ Link | 86.685 | GPS | 2022-05-25 |
| Topology-Informed Graph Transformer | ✓ Link | 86.680 | TIGT | 2024-02-03 |
| Benchmarking Graph Neural Networks | ✓ Link | 86.508 | GatedGCN | 2020-03-02 |