SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph Generation

Understand

Over recent years, denoising diffusion generative models have come to be considered as state-of-the-art methods for synthetic data generation, especially in the case of generating images.

• These approaches have also proved successful in other applications such as tabular and graph data generation.
• However, due to computational complexity, to this date, the application of these techniques to graph data has been restricted to small graphs, such as those used in molecular modeling.
• In this paper, we propose SaGess, a discrete denoising diffusion approach, which is able to generate large real-world networks by augmenting a diffusion model (DiGress) with a generalized divide-and-conquer framework.