Efficient and Degree-Guided Graph Generation via Discrete Diffusion Modeling

Understand

Diffusion-based generative graph models have been proven effective in generating high-quality small graphs.

• However, they need to be more scalable for generating large graphs containing thousands of nodes desiring graph statistics.
• In this work, we propose EDGE, a new diffusion-based generative graph model that addresses generative tasks with large graphs.
• To improve computation efficiency, we encourage graph sparsity by using a discrete diffusion process that randomly removes edges at each time step and finally obtains an empty graph.