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Principled Latent Diffusion for Graphs via Laplacian Autoencoders
Antoine Siraudin, Christopher Morris
Graph diffusion models achieve state-of-the-art performance in graph generation but suffer from quadratic complexity in the number of nodes -- and much of their capacity is wasted…
GraphBench: Next-generation graph learning benchmarking
Timo Stoll, Chendi Qian, Ben Finkelshtein +16
Machine learning on graphs has made substantial progress across domains such as molecular property prediction and chip design. Yet benchmarking practices remain fragmented, often r…
Cometh: A continuous-time discrete-state graph diffusion model
Antoine Siraudin, Fragkiskos D. Malliaros, Christopher Morris
Discrete-state denoising diffusion models led to state-of-the-art performance in graph generation, especially in the molecular domain. Recently, they have been transposed to contin…