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Gradient gating (ICLR 2023)
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tk-rusch/gradientgating
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This repository contains the implementation to reproduce the numerical experiments of theICLR 2023 paperGradient Gating for Deep Multi-Rate Learning on Graphs
Main dependencies (with python >= 3.7):
torch==1.9.0
torch-cluster==1.5.9
torch-geometric==2.0.3
torch-scatter==2.0.9
torch-sparse==0.6.12
torch-spline-conv==1.2.1
Commands to install all the dependencies in a new conda environment
(python 3.7 and cuda 10.2 -- for other cuda versions change accordingly)
conda create --name gradientgating python=3.7conda activate gradientgatingpip install torch==1.9.0pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.htmlpip install torch-sparse -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.htmlpip install torch-cluster -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.htmlpip install torch-spline-conv -f https://pytorch-geometric.com/whl/torch-1.9.0+cu102.htmlpip install torch-geometricpip install scipypip install numpyIf you found our work useful in your research, please cite our paper at:
@inproceedings{rusch2022gradient,title={Gradient Gating for Deep Multi-Rate Learning on Graphs},author={Rusch, T Konstantin and Chamberlain, Benjamin P and Mahoney, Michael W and Bronstein, Michael M and Mishra, Siddhartha},booktitle={International Conference on Learning Representations},year={2023}}
(Also consider starring the project on GitHub.)
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