3 papers
cs.LG2026
Remedying Coarsening-Based GNN Training under Heterophily via Adaptive Complementary Enhancement
Guoming Li, Jian Yang, Xukun Wang +3
Coarsening-based training for graph neural networks (GNNs), i.e.\ training on coarsened graphs rather than the original large ones, has become a promising direction for scaling GNN…
cs.LG2025
ERGNN: Spectral Graph Neural Network With Explicitly-Optimized Rational Graph Filters
Guoming Li, Jian Yang, Shangsong Liang
Approximation-based spectral graph neural networks, which construct graph filters with function approximation, have shown substantial performance in graph learning tasks. Despite t…
eess.SP2025
Polynomial Selection in Spectral Graph Neural Networks: An Error-Sum of Function Slices Approach
Guoming Li, Jian Yang, Shangsong Liang +1
Spectral graph neural networks are proposed to harness spectral information inherent in graph-structured data through the application of polynomial-defined graph filters, recently…