fast_graph_generation_via_spectral_diffusion's Introduction

Fast Graph Generation via Spectral Diffusion

Code for the paper Fast Graph Generation via Spectral Diffusion (IEEE TPAMI 2023).

Dependencies

GSDM is built in Python 3.8.0 and Pytorch 1.10.1. Please use the following command to install the requirements:

pip install -r requirements.txt

Running Experiments

Train model

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

for example,

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

Evaluation

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

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

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

Citation

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

@article{luo2023fast,
  title={Fast graph generation via spectral diffusion},
  author={Luo, Tianze and Mo, Zhanfeng and Pan, Sinno Jialin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2023},
  publisher={IEEE}
  url={https://ieeexplore.ieee.org/abstract/document/10366850}
}

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