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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.LG2024★ 2 cited
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…
cs.LG2024
Spectral GNN via Two-dimensional (2-D) Graph Convolution
Guoming Li, Jian Yang, Shangsong Liang +1
Spectral Graph Neural Networks (GNNs) have achieved tremendous success in graph learning. As an essential part of spectral GNNs, spectral graph convolution extracts crucial frequen…