论文标题
广义的拉普拉斯正规框架图神经网络
Generalized Laplacian Regularized Framelet Graph Neural Networks
论文作者
论文摘要
本文介绍了一种基于p-Laplacian GNN的新型Framlet图方法。提出的两个模型，名为p-Laplacian未选中的Framelet图卷积（PL-UFG）和广义的P-Laplacian未选中的Framelet图卷积（PL-FUFG）继承了P-Laplacian的性质，具有多分辨率的图形信号的多分辨率分解。实证研究强调了PL-FUFG和PL-FUFG在不同的图表学习任务中的出色表现，包括节点分类和信号降解。
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.
