The repo is based on LRGB, it is built for DGL library.
- PascalVOC-SP
- COCO-SP
- PCQM-Contact
- Peptides-func
- Peptides-struct
9/30/2023: Now support PascalVOC-SP, PascalVOC-SP, Peptides-func, Peptides-struct
We present the Long Range Graph Benchmark (LRGB) with 5 graph learning datasets that arguably require long-range reasoning to achieve strong performance in a given task.
# PascalVOC-SP
python datasets/voc_superpixels.py
# COCO-SP
python datasets/coco_superpixels.py
# Peptides-func
python datasets/peptides_functional.py
# Peptides-struct
python datasets/peptides_structural.py| Dataset | Domain | Task | Node Feat. (dim) | Edge Feat. (dim) | Perf. Metric |
|---|---|---|---|---|---|
| PascalVOC-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| COCO-SP | Computer Vision | Node Prediction | Pixel + Coord (14) | Edge Weight (1 or 2) | macro F1 |
| PCQM-Contact | Quantum Chemistry | Link Prediction | Atom Encoder (9) | Bond Encoder (3) | Hits@K, MRR |
| Peptides-func | Chemistry | Graph Classification | Atom Encoder (9) | Bond Encoder (3) | AP |
| Peptides-struct | Chemistry | Graph Regression | Atom Encoder (9) | Bond Encoder (3) | MAE |
| Dataset | # Graphs | # Nodes | μ Nodes | μ Deg. | # Edges | μ Edges | μ Short. Path | μ Diameter |
|---|---|---|---|---|---|---|---|---|
| PascalVOC-SP | 11,355 | 5,443,545 | 479.40 | 5.65 | 30,777,444 | 2,710.48 | 10.74±0.51 | 27.62±2.13 |
| COCO-SP | 123,286 | 58,793,216 | 476.88 | 5.65 | 332,091,902 | 2,693.67 | 10.66±0.55 | 27.39±2.14 |
| PCQM-Contact | 529,434 | 15,955,687 | 30.14 | 2.03 | 32,341,644 | 61.09 | 4.63±0.63 | 9.86±1.79 |
| Peptides-func | 15,535 | 2,344,859 | 150.94 | 2.04 | 4,773,974 | 307.30 | 20.89±9.79 | 56.99±28.72 |
| Peptides-struct | 15,535 | 2,344,859 | 150.94 | 2.04 | 4,773,974 | 307.30 | 20.89±9.79 | 56.99±28.72 |