Official Code Repository for GraphGDP (ICDM 2022).
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 .
- Community small dataset
python main.py --config configs/vp_com_small_pgsn.py --config.model.beta_max 5.0 --mode train --workdir YOUR_PATH- Ego small dataset
- 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 0.10Note: 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.
@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.