Elevating Spectral GNNs through Enhanced Band-pass Filter Approximation

arXiv:2404.15354v1 Announce Type: cross
Abstract: Spectral Graph Neural Networks (GNNs) have attracted great attention due to their capacity to capture patterns in the frequency domains with essential graph filters. Polynomial-based ones (namely poly-GNNs), which approximately construct graph filters with conventional or rational polynomials, are routinely adopted in practice for their substantial performances on graph learning tasks. However, previous poly-GNNs aim at achieving overall lower approximation error on different types of filters, e.g., low-pass and high-pass, but ignore a key question: …

abstract approximation arxiv attention capacity construct cs.ai cs.lg cs.na domains eess.sp filter filters gnns graph graph neural networks math.na networks neural networks ones patterns performances polynomial practice through type