Optimizing Polynomial Graph Filters: A Novel Adaptive Krylov Subspace Approach
Keke Huang, Wencai Cao, Hoang Ta, Xiaokui Xiao, Píetro Lió · 2024
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 proposed to approximate graph filters by leveraging various polynomial bases for filter training. However, no existing studies have explored the diverse polynomial graph filters from a unified perspective for optimization.