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Denoising diffusion probabilistic models and score-matching models haveproven to be very powerful for generative tasks. While these approaches havealso been applied to the generation of discrete graphs, they have, so far,relied on continuous Gaussian perturbations. Instead, in this work, we suggestusing discrete noise for the forward Markov process. This ensures that in everyintermediate step the graph remains discrete. Compared to the previousapproach, our experimental results on four datasets and multiple architecturesshow that using a discrete noising process results in higher quality generatedsamples indicated with an average MMDs reduced by a factor of 1.5. Furthermore,the number of denoising steps is reduced from 1000 to 32 steps, leading to a 30times faster sampling procedure.

Diffusion Models for Graphs Benefit From Discrete State Spaces