This is a Tensorflow 2.0 implementation of the Graph Attention Network (GAT) model by Veličković et al. (2017, [arXiv link]).

I have no affiliation with the authors of the paper and I am implementing this code for non-commercial, educational purposes.
The authors published their reference Tensorflow implementation here, so check it out for something that is guaranteed to work as intended.

You should cite the paper if you use any of this code for your research:

@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},
}

I also copied the code in utils.py almost verbatim from this repo by Thomas Kipf, who I thank sincerely for sharing his work on GCNs and GAEs.

I do not own any rights to the datasets distributed with this code, but they are publicly available at the following links:

• CORA: https://relational.fit.cvut.cz/dataset/CORA
• PubMed: https://catalog.data.gov/dataset/pubmed
• CiteSeer: http://csxstatic.ist.psu.edu/about/data

Run Tensorflow_2_0_Graph_Attention_Networks_(GAT).ipynb

In the colab sheet, you can choose to run different datasets and modify the training properties of the Graph Attention Networks