Graph is a prevalent discrete data structure, whose generation has wide applications such as drug discovery and circuit design. Diffusion generative models, as an emerging research focus, have been applied to graph generation tasks. Overall, according to the space of states and time steps, diffusion generative models can be categorized into discrete-/continuous-state discrete-/continuous-time fashions. In this paper, we formulate the graph diffusion generation in a discrete-state continuous-time setting, which has never been studied in previous graph diffusion models. The rationale of such a formulation is to preserve the discrete nature of graph-structured data and meanwhile provide flexible sampling trade-offs between sample quality and efficiency. Analysis shows that our training objective is closely related to generation quality, and our proposed generation framework enjoys ideal invariant/equivariant properties concerning the permutation of node ordering. Our proposed model shows competitive empirical performance against state-of-the-art graph generation solutions on various benchmarks and, at the same time, can flexibly trade off the generation quality and efficiency in the sampling phase.

本文提出了一种离散状态连续时间设置的图扩散生成模型，该模型在以前的图扩散模型中从未被研究过。分析表明，我们的训练目标与生成质量密切相关，我们提出的生成框架在节点排序的排列方面具有理想的不变/等变特性。我们的模型在各种基准测试中表现出有竞争力的实证性能，同时在采样阶段可以在生成质量和效率之间灵活权衡。

图生成的离散状态连续时间扩散