DiGress: Discrete Denoising diffusion for graph generation. (arXiv:2209.14734v1 [cs.LG])

This work introduces DiGress, a discrete denoising diffusion model for
generating graphs with categorical node and edge attributes. Our model defines
a diffusion process that progressively edits a graph with noise (adding or
removing edges, changing the categories), and a graph transformer network that
learns to revert this process. With these two ingredients in place, we reduce
distribution learning over graphs to a simple sequence of classification tasks.
We further improve sample quality by proposing a new Markovian noise model …

arxiv denoising diffusion graph