Graph Framelets

Graph framelets are multi-resolution representations of graph signals, designed to improve the efficiency and effectiveness of graph neural networks (GNNs). Current research focuses on developing data-adaptive and permutation-equivariant framelet constructions, often incorporating techniques like the p-Laplacian and singular value decomposition (SVD), to address challenges in handling heterophilous graphs and large-scale datasets. These advancements enhance GNN performance in tasks such as node classification, link prediction, and signal denoising, particularly for directed graphs and dynamic graph data, leading to more efficient and robust graph learning algorithms.