26 citations · 37 across the 5 of their papers we have counts for
5 papers
Limiting Over-Smoothing and Over-Squashing of Graph Message Passing by Deep Scattering Transforms
Yuanhong Jiang, Dongmian Zou, Xiaoqun Zhang +1
Graph neural networks (GNNs) have become pivotal tools for processing graph-structured data, leveraging the message passing scheme as their core mechanism. However, traditional GNN…
How Universal Polynomial Bases Enhance Spectral Graph Neural Networks: Heterophily, Over-smoothing, and Over-squashing
Keke Huang, Yu Guang Wang, Ming Li +1
Spectral Graph Neural Networks (GNNs), alternatively known as graph filters, have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigen…
Framelet Message Passing
Xinliang Liu, Bingxin Zhou, Chutian Zhang +1
Graph neural networks (GNNs) have achieved champion in wide applications. Neural message passing is a typical key module for feature propagation by aggregating neighboring features…
Fractional Stochastic Partial Differential Equation for Random Tangent Fields on the Sphere
Vo V. Anh, Andriy Olenko, Yu Guang Wang
This paper develops a fractional stochastic partial differential equation (SPDE) to model the evolution of a random tangent vector field on the unit sphere. The SPDE is governed by…
On Approximation for Fractional Stochastic Partial Differential Equations on the Sphere
Vo V. Anh, Philip Broadbridge, Andriy Olenko +1
This paper gives the exact solution in terms of the Karhunen-Loève expansion to a fractional stochastic partial differential equation on the unit sphere $\mathbb{S}^{2}\subset \mat…