Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach

TL;DR

This paper introduces a theoretical framework for polynomial selection in spectral graph neural networks, linking approximation errors to spectral GNN performance, and proposes a new scalable model, TFGNN, with demonstrated effectiveness.

Contribution

It provides a novel error-sum of function slices perspective for polynomial selection, bridging theory and practice in spectral GNNs, and introduces TFGNN, a scalable, efficient spectral GNN model.

Findings

Theoretical bound linking polynomial approximation errors to GNN performance.

Proposed trigonometric polynomial filter with provable efficiency.

TFGNN achieves competitive results on node classification and anomaly detection.

Abstract

Spectral graph neural networks are proposed to harness spectral information inherent in graph-structured data through the application of polynomial-defined graph filters, recently achieving notable success in graph-based web applications. Existing studies reveal that various polynomial choices greatly impact spectral GNN performance, underscoring the importance of polynomial selection. However, this selection process remains a critical and unresolved challenge. Although prior work suggests a connection between the approximation capabilities of polynomials and the efficacy of spectral GNNs, there is a lack of theoretical insights into this relationship, rendering polynomial selection a largely heuristic process. To address the issue, this paper examines polynomial selection from an error-sum of function slices perspective. Inspired by the conventional signal decomposition, we represent graph filters as a sum of disjoint function slices. Building on this, we then bridge the polynomial capability and spectral GNN efficacy by proving that the construction error of graph convolution layer is bounded by the sum of polynomial approximation errors on function slices. This result leads us to develop an advanced filter based on trigonometric polynomials, a widely adopted option for approximating narrow signal slices. The proposed filter remains provable parameter efficiency, with a novel Taylor-based parameter decomposition that achieves streamlined, effective implementation. With this foundation, we propose TFGNN, a scalable spectral GNN operating in a decoupled paradigm. We validate the efficacy of TFGNN via benchmark node classification tasks, along with an example graph anomaly detection application to show its practical utility.