Generalized Laplacian Regularized Framelet Graph Neural Networks

Autor:	Shao, Zhiqi, Han, Andi, Shi, Dai, Vasnev, Andrey, Gao, Junbin
Rok vydání:	2022
Předmět:	Computer Science - Machine Learning
Druh dokumentu:	Working Paper
Popis:	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.
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2210.15092 Zobrazit plný text záznamu View this record from Arxiv