Paper ID: 2210.15092

Generalized Laplacian Regularized Framelet Graph Neural Networks

Zhiqi Shao, Andi Han, Dai Shi, Andrey Vasnev, Junbin Gao

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.

Submitted: Oct 27, 2022