Denoising diffusion probabilistic models and score-matching models have proven to be very powerful for generative tasks. While these approaches have also been applied to the generation of discrete graphs, they have, so far, relied on continuous Gaussian perturbations. Instead, in this work, we suggest using discrete noise for the forward Markov process. This ensures that in every intermediate step the graph remains discrete. Compared to the previous approach, our experimental results on four datasets and multiple architectures show that using a discrete noising process results in higher quality generated samples 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 30 times faster sampling procedure.

翻译：去噪扩散概率模型和分数匹配模型已被证明在生成任务中非常强大。尽管这些方法也已应用于离散图的生成，但迄今为止，它们仍依赖于连续的加性高斯噪声。相反，在这项工作中，我们建议在前向马尔可夫过程中使用离散噪声。这确保了在每一个中间步骤中，图都保持离散状态。与先前方法相比，我们在四个数据集和多种架构上的实验结果表明，使用离散噪声过程可以生成更高质量的样本，平均MMD降低了1.5倍。此外，去噪步数从1000步减少到32步，使得采样过程速度提高了30倍。