3 papers
cs.LG2024
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…
cs.LG2024
Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach
Keke Huang, Wencai Cao, Hoang Ta +2
Graph Neural Networks (GNNs), known as spectral graph filters, find a wide range of applications in web networks. To bypass eigendecomposition, polynomial graph filters are propose…
cs.LG2023
An Effective Universal Polynomial Basis for Spectral Graph Neural Networks
Keke Huang, Pietro Liò
Spectral Graph Neural Networks (GNNs), also referred to as graph filters have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendeco…