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This work introduces DiGress, a discrete denoising diffusion model forgenerating graphs with categorical node and edge attributes. Our model utilizesa discrete diffusion process that progressively edits graphs with noise,through the process of adding or removing edges and changing the categories. Agraph transformer network is trained to revert this process, simplifying theproblem of distribution learning over graphs into a sequence of node and edgeclassification tasks. We further improve sample quality by introducing aMarkovian noise model that preserves the marginal distribution of node and edgetypes during diffusion, and by incorporating auxiliary graph-theoreticfeatures. A procedure for conditioning the generation on graph-level featuresis also proposed. DiGress achieves state-of-the-art performance on molecularand non-molecular datasets, with up to 3x validity improvement on a planargraph dataset. It is also the first model to scale to the large GuacaMoldataset containing 1.3M drug-like molecules without the use ofmolecule-specific representations.

DiGress: Discrete Denoising diffusion for graph generation