Expressivity-Preserving GNN Simulation

Code repository for our paper Expressivity-Preserving GNN Simulation (NeurIPS, 2023).

Setup

(a different setup is required to run CWN see cwn directory)

1. Create and activate conda environment
2. Add this directory to the python path
3. Install PyTorch (Geometric)

conda install pytorch==1.12.0 torchvision==0.13.0 torchaudio==0.12.0 -c pytorch
conda install -c pyg pyg=2.2.0

4. Install remaning dependencies

conda install -c conda-forge graph-tool=2.44
python -m pip install -r requirements.txt

Reproducing Our Experiments

Run experimentes with the scripts provided in the Scripts directory. Results will be in the Results directory.

Real Life Datasets

bash Scripts/GCN.sh
bash Scripts/GIN.sh
bash Scripts/DS_S.sh

For CWN

bash Scripts/CIN_ALL.sh

CSL

bash Scripts/CSL.sh

Speed Evaluation

bash Scripts/bench_training.sh
bash Scripts/bench_transforms.sh

For CWN

python Exp/run_experiment.py -grid Configs/Bench_Training/bench_CIN.yaml -dataset ZINC --candidates 1 --repeats 10
python Exp/bench_transforms.py CWN --repeats 10

Citation

If you use our code please cite us as

@inproceedings{Expressivity-Preserving-GNN-Simulation,
title={Expressivity-Preserving {GNN} Simulation},
author={Jogl, Fabian and Thiessen, Maximilian and Gärtner, Thomas},
booktitle={Thirty-seventh Conference on Neural Information Processing Systems},
year={2023},
url={https://openreview.net/forum?id=ytTfonl9Wd}
}