An Effective Universal Polynomial Basis for Spectral Graph Neural Networks

Keke Huang, Pietro Lio

Primary Area: unsupervised, self-supervised, semi-supervised, and supervised representation learning

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Keywords: Graph neural networks, Spectral graph filter

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TL;DR: We propose an effective universal polynomial basis for spectral graph neural networks generalized to varying heterophily graphs.

Abstract: Spectral Graph Neural Networks (GNNs), also referred to as *graph filters* have gained increasing prevalence for heterophily graphs. Optimal graph filters rely on Laplacian eigendecomposition for Fourier transform. In an attempt to avert the prohibitive computations, numerous polynomial filters by leveraging distinct polynomials have been proposed to approximate the desired graph filters. However, polynomials in the majority of polynomial filters are *predefined* and remain *fixed* across all graphs, failing to accommodate the diverse heterophily degrees across different graphs. To tackle this issue, we first investigate the correlation between polynomial bases of desired graph filters and the degrees of graph heterophily via a thorough theoretical analysis. Afterward, we develop an adaptive heterophily basis by incorporating graph heterophily degrees. Subsequently, we integrate this heterophily basis with the homophily basis, creating a universal polynomial basis *UniBasis*. In consequence, we devise a general polynomial filter *UniFilter*. Comprehensive experiments on both real-world and synthetic datasets with varying heterophily degrees significantly support the superiority of UniFilter, demonstrating the effectiveness and generality of UniBasis, as well as its promising capability as a new method for graph analysis.

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Submission Number: 3587