Efficient Graph Generation with Graph Recurrent Attention Networks, Deep Generative Model of Graphs, Graph Neural Networks, NeurIPS 2019
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}
}
Python 3, PyTorch(1.2.0)
Other dependencies can be installed via
pip install -r requirements.txt
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:
config
for a full list of configuration yaml files.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:
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
Proteins Graphs from Training Set:
Proteins Graphs Sampled from GRAN:
Please cite our paper if you use this code in your research work.
Please submit a Github issue or contact [email protected] if you have any questions or find any bugs.