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Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations

Official Code Repository for the paper Score-based Generative Modeling of Graphs via the System of Stochastic Differential Equations (ICML 2022).

🔴UPDATE: We provide an seperate code repo for GDSS using Graph Transformer here!

In this repository, we implement the Graph Diffusion via the System of SDEs (GDSS).

Contribution

  • We propose a novel score-based generative model for graphs that overcomes the limitation of previous generative methods, by introducing a diffusion process for graphs that can generate node features and adjacency simultaneously via the system of SDEs.
  • We derive novel training objectives to estimate the gradient of the joint log-density for the proposed diffusion process and further introduce an efficient integrator to solve the proposed system of SDEs.
  • We validate our method on both synthetic and real-world graph generation tasks, on which ours outperforms existing graph generative models.

Dependencies

GDSS is built in Python 3.7.0 and Pytorch 1.10.1. Please use the following command to install the requirements:

pip install -r requirements.txt

For molecule generation, additionally run the following command:

conda install -c conda-forge rdkit=2020.09.1.0

Running Experiments

1. Preparations

We provide four generic graph datasets (Ego-small, Community_small, ENZYMES, and Grid) and two molecular graph datasets (QM9 and ZINC250k).

We additionally provide the commands for generating generic graph datasets as follows:

python data/data_generators.py --dataset ${dataset_name}

To preprocess the molecular graph datasets for training models, run the following command:

python data/preprocess.py --dataset ${dataset_name}
python data/preprocess_for_nspdk.py --dataset ${dataset_name}

For the evaluation of generic graph generation tasks, run the following command to compile the ORCA program (see http://www.biolab.si/supp/orca/orca.html):

cd evaluation/orca 
g++ -O2 -std=c++11 -o orca orca.cpp

2. Configurations

The configurations are provided on the config/ directory in YAML format. Hyperparameters used in the experiments are specified in the Appendix C of our paper.

3. Training

We provide the commands for the following tasks: Generic Graph Generation and Molecule Generation.

To train the score models, first modify config/${dataset}.yaml accordingly, then run the following command.

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type train --config ${train_config} --seed ${seed}

for example,

CUDA_VISIBLE_DEVICES=0 python main.py --type train --config community_small --seed 42

and

CUDA_VISIBLE_DEVICES=0,1 python main.py --type train --config zinc250k --seed 42

4. Generation and Evaluation

To generate graphs using the trained score models, run the following command.

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type sample --config sample_qm9

or

CUDA_VISIBLE_DEVICES=${gpu_ids} python main.py --type sample --config sample_zinc250k

Pretrained checkpoints

We provide checkpoints of the pretrained models on the checkpoints/ directory, which are used in the main experiments.

  • ego_small/gdss_ego_small.pth
  • community_small/gdss_community_small.pth
  • ENZYMES/gdss_enzymes.pth
  • grid/gdss_grid.pth
  • QM9/gdss_qm9.pth
  • ZINC250k/gdss_zinc250k.pth

We also provide a checkpoint of improved GDSS that uses GMH blocks instead of GCN blocks in $s_{\theta,t}$ (i.e., that uses ScoreNetworkX_GMH instead of ScoreNetworkX). The numbers of training epochs are 800 and 1000 for $s_{\theta,t}$ and $s_{\phi,t}$, respectively. For this checkpoint, use Rev. + Langevin solver and set snr as 0.2 and scale_eps as 0.8.

  • ZINC250k/gdss_zinc250k_v2.pth

Citation

If you found the provided code with our paper useful in your work, we kindly request that you cite our work.

@article{jo2022GDSS,
  author    = {Jaehyeong Jo and
               Seul Lee and
               Sung Ju Hwang},
  title     = {Score-based Generative Modeling of Graphs via the System of Stochastic
               Differential Equations},
  journal   = {arXiv:2202.02514},
  year      = {2022},
  url       = {https://arxiv.org/abs/2202.02514}
}