DiGress: Discrete Denoising diffusion models for graph generation

Warning: The code has been updated after experiments were run for the paper. If you don't manage to reproduce the paper results, please write to us so that we can investigate the issue.

For the conditional generation experiments, check the guidance branch.

Environment installation

Download anaconda/miniconda if needed
Create a rdkit environment that directly contains rdkit: conda create -c conda-forge -n digress rdkit python=3.9
Install graph-tool (https://graph-tool.skewed.de/): conda install -c conda-forge graph-tool
Install the nvcc drivers for your cuda version. For example, conda install -c "nvidia/label/cuda-11.3.1" cuda-nvcc
Install pytorch 1.10 or 1.11 (https://pytorch.org/)
Install pytorch-geometric. Your version should match the pytorch version that is installed (https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html)
Install other packages using the requirement file: pip install -r requirements.txt
Install mini-moses: pip install git+https://github.com/igor-krawczuk/mini-moses
Run pip install -e .
Navigate to the ./src/analysis/orca directory and compile orca.cpp: g++ -O2 -std=c++11 -o orca orca.cpp

Download the data

QM9 and Guacamol should download by themselves when you run the code.
For the community, SBM and planar datasets, data can be found at https://github.com/KarolisMart/SPECTRE/tree/main/data
Moses dataset can be found at https://github.com/molecularsets/moses/tree/master/data

Run the code

All code is currently launched through python3 main.py. Check hydra documentation (https://hydra.cc/) for overriding default parameters.
To run the debugging code: python3 main.py +experiment=debug.yaml. We advise to try to run the debug mode first before launching full experiments.
To run a code on only a few batches: python3 main.py general.name=test.
To run the continuous model: python3 main.py model=continuous
To run the discrete model: python3 main.py
You can specify the dataset with python3 main.py dataset=guacamol. Look at configs/dataset for the list of datasets that are currently available

Checkpoints

NOTE: since the code reformatting, these commits cannot be loaded anymore. If you want to use them, either use the commit 682e59019dd33073b1f0f4d3aaba7de6a308602e or rename src to dgd, and then run pip install -e .

We uploaded pretrained models for the Planar and SBM datasets. If you need other checkpoints, please write to us.

Planar: https://drive.switch.ch/index.php/s/tZCjJ6VXU2Z3FIh SBM: https://drive.switch.ch/index.php/s/rxWFVQX4Cu4Vq5j Guacamol: https://drive.switch.ch/index.php/s/jjM3pIHdxWrUGOH

Generated samples

We provide the generated samples for some of the models. If you have retrained a model from scratch for which the samples are not available yet, we would be very happy if you could send them to us!

Cite the paper

@inproceedings{
vignac2023digress,
title={DiGress: Discrete Denoising diffusion for graph generation},
author={Clement Vignac and Igor Krawczuk and Antoine Siraudin and Bohan Wang and Volkan Cevher and Pascal Frossard},
booktitle={The Eleventh International Conference on Learning Representations },
year={2023},
url={https://openreview.net/forum?id=UaAD-Nu86WX}
}

Jianglin954 / DiGress