In recent years, generative diffusion models have achieved a rapid paradigm
shift in deep generative models by showing groundbreaking performance across
various applications. Meanwhile, structured data, encompassing tabular and time
series data, has been received comparatively limited attention from the deep
learning research community, despite its omnipresence and extensive
applications. Thus, there is still a lack of literature and its review on
structured data modelling via diffusion models, compared to other data
modalities such as computer vision and natural language processing. Hence, in
this paper, we present a comprehensive review of recently proposed diffusion
models in the field of structured data. First, this survey provides a concise
overview of the score-based diffusion model theory, subsequently proceeding to
the technical descriptions of the majority of pioneering works using structured
data in both data-driven general tasks and domain-specific applications.
Thereafter, we analyse and discuss the limitations and challenges shown in
existing works and suggest potential research directions. We hope this review
serves as a catalyst for the research community, promoting the developments in
generative diffusion models for structured data.

本文综述了最近的扩散模型在结构化数据中的应用情况及相关研究工作，重点介绍了基于分数的扩散模型理论以及在数据驱动的常规任务和特定领域应用中采用结构化数据模型的先驱工作，分析和讨论了现有工作的局限性和挑战，并提出了潜在的研究方向。