SaGess: Sampling Graph Denoising Diffusion Model for Scalable Graph
  Generation
Stratis Limnios Praveen Selvaraj Mihai Cucuringu Carsten Maple Gesine Reinert Andrew Elliott
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. The algorithm is capable of generating larger
graphs by sampling a covering of subgraphs of the initial graph in order to
train DiGress. SaGess then constructs a synthetic graph using the subgraphs
that have been generated by DiGress. We evaluate the quality of the synthetic
data sets against several competitor methods by comparing graph statistics
between the original and synthetic samples, as well as evaluating the utility
of the synthetic data set produced by using it to train a task-driven model,
namely link prediction. In our experiments, SaGess, outperforms most of the
one-shot state-of-the-art graph generating methods by a significant factor,
both on the graph metrics and on the link prediction task.