Awesome

GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation

Official Code Repository for GraphGDP (ICDM 2022).

Dependencies

The main requirements are:

• pytorch 1.11
• PyG 2.1
• DGL 0.9.1 (for GIN-based metrics from GGM-metrics)

Others see requirements.txt .

Code Usage

Training Example

1. Community small dataset

python main.py --config configs/vp_com_small_pgsn.py --config.model.beta_max 5.0 --mode train --workdir YOUR_PATH

2. Ego small dataset

Evaluation Example

• EM method sampling

python main.py --config configs/vp_com_small_pgsn.py --config.model.beta_max 5.0 --mode eval --workdir YOUR_PATH \
--config.eval.begin_ckpt 150 --config.eval.end_ckpt 150

• Langevin correction

python main.py --config configs/vp_com_small_pgsn.py --config.model.beta_max 5.0 --mode eval --workdir YOUR_PATH \
--config.eval.begin_ckpt 150 --config.eval.end_ckpt 150 --config.sampling.corrector langevin --config.sampling.snr 0.20

• ODE Solvers

# scipy ODE (CPU)
python main.py --config configs/vp_com_small_pgsn.py --config.model.beta_max 5.0 --mode eval --workdir YOUR_PATH \
--config.eval.begin_ckpt 150 --config.eval.end_ckpt 150 --config.sampling.method ode \
--config.sampling.rtol 1e-4 --config.sampling.atol 1e-4

# Neural ODE (GPU) - Adaptive-step
python main.py --config configs/vp_com_small_pgsn.py --config.model.beta_max 5.0 --mode eval --workdir YOUR_PATH \
--config.eval.begin_ckpt 150 --config.eval.end_ckpt 150 --config.sampling.method diffeq \
--config.sampling.ode_method dopri5 --config.sampling.rtol 1e-4 --config.sampling.atol 1e-4

# Neural ODE (GPU) - Fixed-step
python main.py --config configs/vp_com_small_pgsn.py --config.model.beta_max 5.0 --mode eval --workdir YOUR_PATH \
--config.eval.begin_ckpt 150 --config.eval.end_ckpt 150 --config.sampling.method diffeq \
--config.sampling.ode_method rk4 --config.sampling.ode_step 0.10

Note: we recommend training with config.model.beta_max 20.0 when utilizing probability flow ODEs.

Some models and generated samples are provided on Google Drive.

Citation

@article{huang2022graphgdp,
  title={GraphGDP: Generative Diffusion Processes for Permutation Invariant Graph Generation},
  author={Huang, Han and Sun, Leilei and Du, Bowen and Fu, Yanjie and Lv, Weifeng},
  journal={arXiv preprint arXiv:2212.01842},
  year={2022}
}

Acknowledgement: Our implementation is based on the repo Score_SDE. Evaluation implementation is modified from the repo GGM-metrics.