3 papers
stat.ML2026
DiPhon: Diffusion on Graphons for Scalable Graph Generation
Sergio Rozada, Yiming Qin, Manuel Madeira +2
Diffusion models represent a leading paradigm for graph generation, with notable impact in domains such as molecular design. Yet, scaling these models to large graphs remains an op…
cs.LG2026
Graph Convolutional Attention: A Spectral Perspective on Graph Denoising and Diffusion
Shervin Khalafi, Igor Krawczuk, Sergio Rozada +3
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 sh…
cs.LG2025
Learning Efficient Positional Encodings with Graph Neural Networks
Charilaos I. Kanatsoulis, Evelyn Choi, Stephanie Jegelka +2
Positional encodings (PEs) are essential for effective graph representation learning because they provide position awareness in inherently position-agnostic transformer architectur…