Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Denoising graphs is a fundamental problem in graph learning and the core operation of graph diffusion models. Attention-based architectures like graph transformers have recently shown promise in denoising graphs. However, our principled understanding of attention-based graph denoising remains limited, making it unclear whether standard attention is the right mechanism for this task. Here we show that, under a denoising objective, linear attention is suboptimal and can only learn an average spectral denoising filter over the training distribution. This creates a fundamental limitation as graphs
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- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-xl-base-1.0 →
“Fuzzy title match (0.73): “Graph Convolutional Attention: A Spectral Perspective on Gra” ≈ “stabilityai/stable-diffusion-xl-base-1.0””
- FuzzySimilar title/name (fuzzy) · 59%CompVis/stable-diffusion-v1-4 →
“Fuzzy title match (0.73): “Graph Convolutional Attention: A Spectral Perspective on Gra” ≈ “CompVis/stable-diffusion-v1-4””
- FuzzySimilar title/name (fuzzy) · 59%stabilityai/stable-diffusion-3.5-large →
“Fuzzy title match (0.73): “Graph Convolutional Attention: A Spectral Perspective on Gra” ≈ “stabilityai/stable-diffusion-3.5-large””
- LinkedLinked via arxiv author · 85%Shervin Khalafi →
“Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion”
- LinkedLinked via arxiv author · 85%Igor Krawczuk →
“Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion”
- LinkedLinked via arxiv author · 85%Sergio Rozada →
“Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion”
- LinkedLinked via arxiv author · 85%Charilaos Kanatsoulis →
“Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion”
- LinkedLinked via arxiv author · 85%Antonio G Marques →
“Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion”