CrazyCharles6 / GAT

Graph Attention Networks (https://arxiv.org/abs/1710.10903)

GAT

Graph Attention Networks (Veličković et al., ICLR 2018): https://arxiv.org/abs/1710.10903

GAT layer	t-SNE + Attention coefficients on Cora

Overview

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

• data/ contains the necessary dataset files for Cora;
• models/ contains the implementation of the GAT network (gat.py);
• pre_trained/ contains a pre-trained Cora model (achieving 84.4% accuracy on the test set);
• 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_cora.py puts all of the above together and may be used to execute a full training run on Cora.

Sparse version

An experimental sparse version is also available, working only when the batch size is equal to 1. The sparse model may be found at models/sp_gat.py.

You may execute a full training run of the sparse model on Cora through execute_cora_sparse.py.

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

If you make advantage of the GAT model in your research, please cite the following in your 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},
}

You may also be interested in the following unofficial ports of the GAT model:

• [Keras] keras-gat, currently under development by Daniele Grattarola;
• [PyTorch] pyGAT, currently under development by Diego Antognini.

License

MIT