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README.md

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 # GAT
-Graph Attention Networks
+Graph Attention Networks (Veličković *et al.*, ICLR 2018)
+
+## 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.
+
+## 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={Petar Veli{\v{c}}kovi{\'{c}}, Guillem Cucurull, Arantxa Casanova, Adriana Romero, Pietro Li{\`{o}}, Yoshua Bengio},
+  journal={International Conference on Learning Representations},
+  year={2018},
+  url={https://openreview.net/forum?id=rJXMpikCZ},
+  note={accepted as poster},
+}
+```
+
+## License
+MIT