  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.
