graphRL

Comparative study between GraphRNN, GRAN and GraphOpt

This is using 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}
}

Run Demos

Train

• To run the training of experiment X where X is one of {gran_grid, gran_community, graphrnn_mlp_community, graphrnn_rnn_community}:

  python run_exp.py -c config/X.yaml

The training file are stored in the exp/GRAN and exp/GraphRNN directory

Test

• To run the test of experiments X:

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