This is the code for BrainGS.

This code was developed and tested with Python 3.7.12, PyTorch 1.13.0, and PyG 2.1.0. All dependencies are specified in the requirements.txt file.

We provide a well-trained diffusion based graph generator in the path: checkpoints/qm9_denoise.pth. If readers want to train the diffusion model from the scratch, we suggest following the codes from GDSS. Our code should be compatible with any continuous-state graph diffusion model. For the graph diffusion model trained on other datasets, modifications for the enbale_index variable in the convert_sparse_to_dense function from the utils.mol_utils.py are needed.

Following is an example command to run experiments on ABIDE datasets.

# ABIDE
python main.py --dataset ABIDE

The dataset name can be any of ['ABIDE', 'ADHD200','OSF']

We provide a checkpoint to facilitate the reproduction of the results. The checkpoint file is located in the path:

./BrainGS/checkpoints/ABIDE/best.pth.