Diffusion models have become a new SOTA generative modeling method in various fields, for which there are multiple survey works that provide an overall survey. With the number of articles on diffusion models increasing exponentially in the past few years, there is an increasing need for surveys of diffusion models on specific fields. In this work, we are committed to conducting a survey on the graph diffusion models. Even though our focus is to cover the progress of diffusion models in graphs, we first briefly summarize how other generative modeling methods are used for graphs. After that, we introduce the mechanism of diffusion models in various forms, which facilitates the discussion on the graph diffusion models. The applications of graph diffusion models mainly fall into the category of AI-generated content (AIGC) in science, for which we mainly focus on how graph diffusion models are utilized for generating molecules and proteins but also cover other cases, including materials design. Moreover, we discuss the issue of evaluating diffusion models in the graph domain and the existing challenges.

翻译：扩散模型已成为多个领域中新的SOTA生成建模方法，已有若干综述对其进行了全面概述。由于近些年扩散模型相关论文数量呈指数级增长，针对特定领域的扩散模型综述需求日益迫切。本工作致力于对图扩散模型进行系统综述。尽管核心目标在于涵盖扩散模型在图数据中的研究进展，我们首先简要总结其他生成建模方法在图数据中的应用。随后，阐述不同形式的扩散模型机制，为探讨图扩散模型奠定基础。图扩散模型的应用主要归属于科学领域的AI生成内容（AIGC），我们重点关注这类模型如何用于生成分子与蛋白质，同时涵盖材料设计等其他案例。此外，我们讨论了图扩散模型在评估方面的问题及现有挑战。