Enhancing Spectral Graph Neural Networks through Improved Band-pass Filter Approximation

Spectral Graph Neural Networks (GNNs) with polynomial-based graph filters (poly-GNNs) can achieve efficient and effective graph learning, but their performance is hindered by the inability to accurately approximate band-pass graph filters. This paper proposes TrigoNet, a novel poly-GNN that constructs graph filters using trigonometric polynomials, which excel at approximating band-pass functions. TrigoNet also employs a Multiple Linear Transform mechanism to further enhance its flexibility and efficiency.