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

GAT layer	t-SNE + Attention coefficients on Cora

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.

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.

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.

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