Polynomial Filter

Polynomial filters are increasingly used in spectral graph neural networks (GNNs) to process graph-structured data, aiming to improve efficiency and effectiveness compared to methods relying on full Laplacian eigendecomposition. Current research focuses on developing adaptive polynomial bases that can handle varying graph structures and heterophily (nodes with dissimilar neighbors), as well as optimizing filter design through techniques like eigenvalue correction and automated learning of filter coefficients. These advancements enhance the expressive power and performance of GNNs in applications such as node classification and relational inference, leading to more accurate and efficient graph analysis across diverse domains.