# GAT

**Repository Path**: magicat128/gat

## Basic Information

- **Project Name**: GAT
- **Description**: No description available
- **Primary Language**: Python
- **License**: MIT
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 1
- **Created**: 2020-12-17
- **Last Updated**: 2020-12-21

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# GAT
Graph Attention Networks (Veličković *et al.*, ICLR 2018): [https://arxiv.org/abs/1710.10903](https://arxiv.org/abs/1710.10903)

## 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;
- `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 reported result from paper is 83.0±0.7%.

## Dependencies

The script has been tested running under Python 3.7 Ascend 910 environment, with the following packages installed (along with their dependencies):

- `numpy`
- `scipy`
- `networkx`
- `tensorflow`

## Usage

```
python3 execute_cora.py \
	--data_url PATH_TO_DATA \
	--train_url PATH_TO_OUTPUT \
	--batch_size 1 \
	--nb_epochs 200 \
	--lr 0.005 \
	--l2_coef 0.0005
```


## License
MIT