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 such as graph transformers have shown promise for this task, yet the mechanisms through which attention denoises graphs remain poorly understood. We show that, under a graph denoising objective, standard attention scores learn an average spectral denoising filter across the training distribution and are suboptimal when the graph spectra vary. To address this limitation, we introduce spectral attention scores, which are attention scores that depend on the input graph spectrum. We characterize the improvement over standard attention scores in terms of t...
| Authors | Shervin Khalafi, Igor Krawczuk, Sergio Rozada et al. |
|---|---|
| Primary category | cs.LG |
| Published | 7 July 2026 |
| Last updated at source | 9 October 2026 |
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