This code is about the implementation of Filter-Informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis.
In SGWN, the spectral graph wavelet convolutional (SGWConv) layer is established upon the spectral graph wavelet transform, which can decompose a graph signal into scaling function coefficients and spectral graph wavelet coefficients. With the help of SGWConv, SGWN is able to prevent the over-smoothing problem caused by long-range low-pass filtering, by simultaneously extracting low-pass and band-pass features. Furthermore, to speed up the computation of SGWN, the scaling kernel function and graph wavelet kernel function in SGWConv are approximated by the Chebyshev polynomials. .
The data for running this code can be found in PHMGNNBenchmark
python ./SGWM/train_graph.py --model_name SGWN --checkpoint_dir ./results/ --data_name XJTUSpurgearKnn --data_dir ./data/XJTU_Spurgear --per_node 10 --s 2 --n 2
SGWN: @ARTICLE{10079151, author={Li, Tianfu and Sun, Chuang and Fink, Olga and Yang, Yuangui and Chen, Xuefeng and Yan, Ruqiang}, journal={IEEE Transactions on Cybernetics}, title={Filter-Informed Spectral Graph Wavelet Networks for Multiscale Feature Extraction and Intelligent Fault Diagnosis}, year={2024}, volume={54}, number={1}, pages={506-518}, doi={10.1109/TCYB.2023.3256080}}