Graph Signal Filter

Graph signal filtering leverages the structure of graph data to process information residing on its nodes, aiming to extract meaningful patterns and improve performance in various machine learning tasks. Current research focuses on developing more effective graph filters, including those based on polynomial approximations, tensor decompositions, and infinite impulse response (IIR) models, often tailored to specific frequency ranges (e.g., band-pass filters) for enhanced signal representation. These advancements are improving the accuracy and efficiency of graph neural networks in applications such as recommendation systems, spectral clustering, and anomaly detection in complex networks like smart grids.