| ESA + RWSE (Edge set attention, Random Walk Structural Encoding, + validation set) |
- AP
- 0.7479
|
An end-to-end attention-based approach for learning on graphs 2024-02-16 |
코드 1 |
| ECFP + LightGBM |
- AP
- 0.7460
|
Molecular Fingerprints Are Strong Models for Peptide Function Prediction 2025-01-29 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| ESA + RWSE (Edge set attention, Random Walk Structural Encoding, tuned) |
- AP
- 0.7357±0.0036
|
An end-to-end attention-based approach for learning on graphs 2024-02-16 |
코드 1 |
| TT + LightGBM |
- AP
- 0.7318
|
Molecular Fingerprints Are Strong Models for Peptide Function Prediction 2025-01-29 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| S²GCN |
- AP
- 0.7311±0.0066
|
Spatio-Spectral Graph Neural Networks 2024-05-29 |
코드 1 |
| RDKit + LightGBM |
- AP
- 0.7311
|
Molecular Fingerprints Are Strong Models for Peptide Function Prediction 2025-01-29 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |
| GCN+ |
- AP
- 0.7261 ± 0.0067
|
Unlocking the Potential of Classic GNNs for Graph-level Tasks: Simple Architectures Meet Excellence 2025-02-13 |
코드 1 |
| GraphGPS + HDSE |
- AP
- 0.7156±0.0058
|
Enhancing Graph Transformers with Hierarchical Distance Structural Encoding 2023-08-22 |
코드 1 |
| DRew-GCN+LapPE |
- AP
- 0.7150±0.0044
|
DRew: Dynamically Rewired Message Passing with Delay 2023-05-13 |
코드 1 |
| GRED+LapPE |
- AP
- 0.7133±0.0011
|
Recurrent Distance Filtering for Graph Representation Learning 2023-12-03 |
코드 1 |
| NeuralWalker |
- AP
- 0.7096 ± 0.0078
|
Learning Long Range Dependencies on Graphs via Random Walks 2024-06-05 |
코드 1 |
| GRED |
- AP
- 0.7085±0.0027
|
Recurrent Distance Filtering for Graph Representation Learning 2023-12-03 |
코드 1 |
| ESA (Edge set attention, no positional encodings, tuned) |
- AP
- 0.7071±0.0015
|
An end-to-end attention-based approach for learning on graphs 2024-02-16 |
코드 1 |
| GRIT |
- AP
- 0.6988±0.0082
|
Graph Inductive Biases in Transformers without Message Passing 2023-05-27 |
코드 1 · 코드 2 |
| CKGCN |
- AP
- 0.6952
|
CKGConv: General Graph Convolution with Continuous Kernels 2024-04-21 |
코드 1 |
| Graph ViT |
- AP
- 0.6942±0.0075
|
A Generalization of ViT/MLP-Mixer to Graphs 2022-12-27 |
코드 1 · 코드 2 · 코드 3 |
| GraphMLPMixer |
- AP
- 0.6921±0.0054
|
A Generalization of ViT/MLP-Mixer to Graphs 2022-12-27 |
코드 1 · 코드 2 · 코드 3 |
| GatedGCN-HSG |
- AP
- 0.6866±0.0038
|
Next Level Message-Passing with Hierarchical Support Graphs 2024-06-22 |
코드 1 |
| ESA (Edge set attention, no positional encodings, not tuned) |
- AP
- 0.6863±0.0044
|
An end-to-end attention-based approach for learning on graphs 2024-02-16 |
코드 1 |
| GCN-tuned |
- AP
- 0.6860±0.0050
|
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark 2023-09-01 |
코드 1 · 코드 2 |
| MGT+WavePE |
- AP
- 0.6817±0.0064
|
Multiresolution Graph Transformers and Wavelet Positional Encoding for Learning Hierarchical Structures 2023-02-17 |
코드 1 · 코드 2 |
| PathNN |
- AP
- 0.6816±0.0026
|
Path Neural Networks: Expressive and Accurate Graph Neural Networks 2023-06-09 |
코드 1 |
| GatedGCN-tuned |
- AP
- 0.6765±0.0047
|
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark 2023-09-01 |
코드 1 · 코드 2 |
| GatedGCN+RWSE+virtual node |
- AP
- 0.6685±0.0062
|
On the Connection Between MPNN and Graph Transformer 2023-01-27 |
코드 1 |
| TIGT |
- AP
- 0.6679
|
Topology-Informed Graph Transformer 2024-02-03 |
코드 1 · 코드 2 |
| Graph Diffuser |
- AP
- 0.6651±0.0010
|
Diffusing Graph Attention 2023-03-01 |
연결 없음 |
| GINE-tuned |
- AP
- 0.6621±0.0067
|
Where Did the Gap Go? Reassessing the Long-Range Graph Benchmark 2023-09-01 |
코드 1 · 코드 2 |
| ViT-PS |
- AP
- 0.6575
|
Learning Probabilistic Symmetrization for Architecture Agnostic Equivariance 2023-06-05 |
코드 1 |
| CIN++-500k |
- AP
- 0.6569±0.0117
|
CIN++: Enhancing Topological Message Passing 2023-06-06 |
코드 1 |
| GPS |
- AP
- 0.6535±0.0041
|
Recipe for a General, Powerful, Scalable Graph Transformer 2022-05-25 |
코드 1 · 코드 2 · 코드 3 · 코드 4 |