Learnable Graph Convolutional Attention Networks (LCAT)

This is the official Pytorch implementation of CAT and L-CAT.

Installation

For CPU usage:

conda create --name lcat python=3.9.7  --no-default-packages
conda activate lcat

pip install torch==1.13.1 torchvision==0.14.1


pip install pyg-lib torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cpu.html --force-reinstall

For GPU usage:

conda create --name lcat_gpu python=3.9.7 --no-default-packages
conda activate l

pip3 install torch==1.13.1 torchvision==0.14.1 torchaudio==0.13.1 --extra-index-url https://download.pytorch.org/whl/cu116

pip install pyg-lib torch-scatter torch-sparse torch-cluster torch-spline-conv torch-geometric -f https://data.pyg.org/whl/torch-1.13.0+cu116.html 

Then install the following dependencies:

pip install pytest==7.2.0
pip install pytest-helpers-namespace

Usage

To use the L-CAT in your own project, see the tests folder for examples. In particular, check out tests/test_lcat_pyg.py to learn how to use LCAT with GCN, GCN2, and PNA with the PyTorch Geometric implementation.

Testing

This project uses pytest for testing. To run the tests, you can use the following command:

pytest tests

This will run all the tests in the tests/ directory. You can also run individual test files using the following command:

pytest tests/test_lcat_pyg.py

License

This project is licensed under the MIT License.

Citing

If you use CAT or L-CAT, consider citing our paper

@inproceedings{javaloy2023learnable,
title={Learnable Graph Convolutional Attention Networks},
author={Adri{\'a}n Javaloy and Pablo Sanchez Martin and Amit Levi and Isabel Valera},
booktitle={International Conference on Learning Representations (ICLR) },
year={2023},
url={https://openreview.net/forum?id=WsUMeHPo-2}
}

Contact

For further information: psanchez@tue.mpg.de