GRAN

This is the official PyTorch implementation of Efficient Graph Generation with Graph Recurrent Attention Networks as described in the following NeurIPS 2019 paper:

@inproceedings{liao2019gran,
  title={Efficient Graph Generation with Graph Recurrent Attention Networks}, 
  author={Liao, Renjie and Li, Yujia and Song, Yang and Wang, Shenlong and Nash, Charlie and Hamilton, William L. and Duvenaud, David and Urtasun, Raquel and Zemel, Richard}, 
  booktitle={NeurIPS},
  year={2019}
}

Visualization

Generation of GRAN per step:

Overall generation process:

Dependencies

Python 3, PyTorch(1.2.0)

Other dependencies can be installed via

pip install -r requirements.txt

Run Demos

Train

• To run the training of experiment X where X is one of {gran_grid, gran_DD, gran_DB, gran_lobster}:

  python run_exp.py -c config/X.yaml

Note:

• Please check the folder config for a full list of configuration yaml files.
• Most hyperparameters in the configuration yaml file are self-explanatory.

Test

• After training, you can specify the test_model field of the configuration yaml file with the path of your best model snapshot, e.g.,

  test_model: exp/gran_grid/xxx/model_snapshot_best.pth

• To run the test of experiments X:

  python run_exp.py -c config/X.yaml -t

Note:

• Please check the evaluation to set up.

Trained Models

• You could use our trained model for comparisons. Please make sure you are using the same split of the dataset. Running the following script will download the trained model:

  ./download_model.sh

Sampled Graphs from GRAN

• Proteins Graphs from Training Set:

• Proteins Graphs Sampled from GRAN:

Cite

Please cite our paper if you use this code in your research work.

Questions/Bugs

Please submit a Github issue or contact rjliao@cs.toronto.edu if you have any questions or find any bugs.