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.

翻译：本文提出了一种基于p-拉普拉斯图神经网络的新型框架图方法。所提出的两个模型，命名为p-拉普拉斯非抽样框架图卷积（pL-UFG）和广义p-拉普拉斯非抽样框架图卷积（pL-fUFG），继承了p-拉普拉斯在具有图信号多分辨率分解表达能力方面的特性。实验研究突出了pL-UFG和pL-fUFG在不同图学习任务（包括节点分类和信号去噪）中的卓越性能。