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Learning to generate graphs is challenging as a graph is a set of pairwiseconnected, unordered nodes encoding complex combinatorial structures. Recently,several works have proposed graph generative models based on normalizing flowsor score-based diffusion models. However, these models need to generate nodesand edges in parallel from the same process, whose dimensionality isunnecessarily high. We propose NVDiff, which takes the VGAE structure and usesa score-based generative model (SGM) as a flexible prior to sample nodevectors. By modeling only node vectors in the latent space, NVDiffsignificantly reduces the dimension of the diffusion process and thus improvessampling speed. Built on the NVDiff framework, we introduce an attention-basedscore network capable of capturing both local and global contexts of graphs.Experiments indicate that NVDiff significantly reduces computations and canmodel much larger graphs than competing methods. At the same time, it achievessuperior or competitive performances over various datasets compared to previousmethods.

NVDiff: Graph Generation through the Diffusion of Node Vectors