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This repository contains a PyTorch implementation of "BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation".(https://arxiv.org/abs/2106.10994)

• pytorch 1.8.1
• numpy 1.18.1
• torch-geometric 1.6.3
• tqdm 4.59.0
• scipy 1.6.2
• seaborn 0.11.1
• scikit-learn 0.24.1

We conduct an empirical analysis on 50 real images with the resolution of 100×100 from the Image Processing Toolbox in Matlab.

We provide the processed dataset and you can run the code directly. We also provide the original images in the folder './LearningFilters/image‘ and the Matlab code './LearningFilters/preprocessing.m' for preprocessing.

Input Parameters

• filter_type: the type of the filter applied to the spectral domain
• net: the GNN models, default='BernNet'

You can run the following script in the folder './LearningFilters' directly

sh bernnet.sh

or run the following Command

• The band-pass filter for BernNet

python training.py --filter_type band --net BernNet

• The high-pass filter for BernNet

python training.py --filter_type high --net BernNet

We evaluate the performance of BernNet against the competitors on 10 real-world datasets.

We provide the datasets in the folder './NodeClassification/data' and you can run the code directly, or you can choose not to download the datasets('./NodeClassification/data') here. The code will automatically build the datasets through the data loader of Pytorch Geometric.

You can run the following script in the folder './NodeClassification' directly and this script describes the hyperparameters settings of BernNet on each dataset.

sh bernnet.sh

or run the following Command

• Pubmed

python training.py  --dataset Pubmed --Bern_lr 0.01 --dprate 0.0 --weight_decay 0.0  --net BernNet

• Texas

python training.py --dataset Texas --lr 0.05 --Bern_lr 0.002 --dprate 0.5 --net BernNet

@inproceedings{he2021bernnet,
  title={BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation},
  author={He, Mingguo and Wei, Zhewei and Huang, Zengfeng and Xu, Hongteng},
  booktitle={NeurIPS},
  year={2021}
}

If you have any questions, please feel free to contact me with [email protected]