  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.
