This is a Keras implementation of the Graph Attention Network (GAT) model presented by Veličković et. al (2017, https://arxiv.org/abs/1710.10903).

I have no affiliation with the authors of the paper and I am implementing this code for non-commercial reasons.
The authors published their official Tensorflow implementation here, so check it out for something that is guaranteed to work as intended. From what I gather, their implementation is slightly different than mine, so that may be something I will investigate in the future.
You should also 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},
}

If you would like to give me credit, feel free to link to my Github profile, blog, or Twitter.

I also copied the code in utils.py almost verbatim from this repo by Thomas Kipf, whom I thank sincerely for sharing his work on GCNs and GAEs, and for giving me a few pointers on how to split the data into train/test/val sets.

A big thank you to matthias-samwald, who was able to run the full model on a better GPU than mine and report the performance of the config described in the paper (see section below).

Credits for commit f4974ac go to user mawright who forked the code implementing attention to make it way more fast and memory efficient.

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

To install as a module:

$ git clone https://github.com/danielegrattarola/keras-gat.git
$ cd keras-gat
$ pip install .
$ python
>>> from keras_gat import GraphAttention

Or you can just copy and paste graph_attention_layer.py into your project.

If you wish to replicate the experimental results of the paper, simply run:

$ python examples/gat.py

from the base folder.

matthias-samwald got a 81.7% test accuracy with v1.1, so pretty close to the 83.5% reported by the authors (the difference in implementation w.r.t the authors might be responsible). Since version 1.2 the code is much more efficient and fits even on the GTX 1050 of my laptop. I ran the same setup of the paper and got a test accuracy of up to 81.9% after 263 epochs.

I'm working on a blog post explaining GATs, so stay tuned. (Also, I lied and I'm not actually working on the post RIGHT NOW. But I'll get around to it, I promise).