https://github.com/chapzq77/gat
Graph Attention Networks (https://arxiv.org/abs/1710.10903)
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Graph Attention Networks (https://arxiv.org/abs/1710.10903)
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# GAT
Graph Attention Networks (Velikovi *et al.*, ICLR 2018): [https://arxiv.org/abs/1710.10903](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.
## 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},
}
```
## License
MIT
Owner
- Name: 周奇
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