ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters
Li, Guoming Yang, Jian Liang, Shangsong
Li, Guoming
Yang, Jian
Liang, Shangsong
Supervisor
Department
Machine Learning
Embargo End Date
Type
Conference proceeding
Date
2025
License
Language
English
Collections
Research Projects
Organizational Units
Journal Issue
Abstract
Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite their great success, existing works primarily employ polynomial approximation to construct the filters, whereas another superior option, namely ration approximation, remains underexplored. Although a handful of prior works have attempted to deploy the rational approximation, their implementations often involve intensive computational demands or still resort to polynomial approximations, hindering full potential of the rational graph filters. To address the issues, this paper introduces ERGNN, a novel spectral GNN with explicitly-optimized rational filter. ERGNN adopts a unique two-step framework that sequentially applies the numerator filter and the denominator filter to the input signals, thus streamlining the model paradigm while enabling explicit optimization of both numerator and denominator of the rational filter. Extensive experiments validate the superiority of ERGNN over state-of-the-art methods, establishing it as a practical solution for deploying rational-based GNNs.
Citation
G. Li, J. Yang and S. Liang, "ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters," ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Hyderabad, India, 2025, pp. 1-5, doi: 10.1109/ICASSP49660.2025.10888930
Source
ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
Conference
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025
Keywords
Spectral graph neural networks, Graph filters, Rational approximation
Subjects
Source
International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2025
Publisher
IEEE