GAT

Graph Attention Networks

Overview

Here we provide the implementation of a Graph Attention Network (GAT) layer in TensorFlow, along with a minimal execution example (on the KE dataset). The repository is organised as follows:

• data/ contains the necessary dataset files for KE;
• models/ contains the implementation of the GAT network (gat.py);
• pre_trained/ contains a pre-trained KE model ;
• utils/ contains:
  • an implementation of an attention head, along with an experimental sparse version (layers.py);
  • preprocessing subroutines (process.py);
  • preprocessing utilities for the PPI benchmark (process_ppi.py).

Finally, execute_keyphrase_extraction.py puts all of the above together and may be used to execute a full training run on KE.

Dependencies

The script has been tested running under Python 3.5.2, with the following packages installed (along with their dependencies):

• numpy==1.14.1
• scipy==1.0.0
• networkx==2.1
• tensorflow-gpu==1.6.0

In addition, CUDA 9.0 and cuDNN 7 have been used.

Reference

The base GAT model is taken from following manuscript:

@article{
  velickovic2018graph,
  title="{Graph Attention Networks}",
  author={Veli{\v{c}}kovi{\'{c}}, Petar and Cucurull, Guillem and Casanova, Arantxa and Romero, Adriana and Li{\`{o}}, Pietro and Bengio, Yoshua},
  journal={International Conference on Learning Representations},
  year={2018},
  url={https://openreview.net/forum?id=rJXMpikCZ},
  note={accepted as poster},
}

License

MIT