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：事实证明,在基因化任务中,不区分扩散概率模型和得分匹配模型的作用非常大,虽然这些方法还被用于生成离散图,但迄今为止,它们依赖连续的高斯扰动。相反,我们建议,在这项工作中,为前马尔科夫进程使用离散噪音。这确保了在每一个中间步骤中,该图都保持离散。与前一种方法相比,我们在四个数据集和多个结构上的实验结果显示,使用离散无漏过程可以产生质量更高的样本,显示平均MMDs减少1.5倍。此外,除尘步骤的数量从1000个减少到32个,导致30倍的快速取样程序。