This paper introduces a novel Framelet Graph approach based on p-Laplacian GNN. The proposed two models, named p-Laplacian undecimated framelet graph convolution (pL-UFG) and generalized p-Laplacian undecimated framelet graph convolution (pL-fUFG) inherit the nature of p-Laplacian with the expressive power of multi-resolution decomposition of graph signals. The empirical study highlights the excellent performance of the pL-UFG and pL-fUFG in different graph learning tasks including node classification and signal denoising.
@article{arxiv.2210.15092,
title = {Generalized Laplacian Regularized Framelet Graph Neural Networks},
author = {Zhiqi Shao and Andi Han and Dai Shi and Andrey Vasnev and Junbin Gao},
journal= {arXiv preprint arXiv:2210.15092},
year = {2023}
}