Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation

This repository contains PyTorch implementation of our JMLR paper: Fitting Autoregressive Graph Generative Models through Maximum Likelihood Estimation

This is a minimum working version of the code used for the paper.

0. Environment Setup

enviroment setup

pip install -r requirements.txt

Run build.sh script in the project's root directory for MMD computation.

./build.sh

1. Training

To list the arguments, run the following command:

python main.py -h

To train the model on datasets with Rout and DAGG, run the following:

python main.py -dataset caveman_small

2.Evaluation

To evaluate the generated graph, run the following:

python main.py -task evaluate -load_model_path 'saved_model_path'

If you find our work helpful, please cite:

@article{han2023fitting,
  title={Fitting autoregressive graph generative models through maximum likelihood estimation},
  author={Han, Xu and Chen, Xiaohui and Ruiz, Francisco JR and Liu, Li-Ping},
  journal={Journal of Machine Learning Research},
  volume={24},
  number={97},
  pages={1--30},
  year={2023}
}