2 citations · 4 across the 6 of their papers we have counts for
6 papers
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…
Partition-wise Graph Filtering: A Unified Perspective Through the Lens of Graph Coarsening
Guoming Li, Jian Yang, Yifan Chen
Filtering-based graph neural networks (GNNs) constitute a distinct class of GNNs that employ graph filters to handle graph-structured data, achieving notable success in various gra…
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…
Lipschitz-Driven Noise Robustness in VQ-AE for High-Frequency Texture Repair in ID-Specific Talking Heads
Jian Yang, Xukun Wang, Wentao Wang +7
Audio-driven IDentity-specific Talking Head Generation (ID-specific THG) has shown increasing promise for applications in filmmaking and virtual reality. Existing approaches are ge…
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…
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…