A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material

• URL: http://arxiv.org/abs/2304.01565v1
• Date: Tue, 4 Apr 2023 06:41:15 GMT
• Title: A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
• Authors: Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung, Chenshuang Zhang, Sung-Ho Bae, Chaoning Zhang
• Abstract summary: Diffusion models have become a new SOTA generative modeling method in various fields. The applications of graph diffusion models mainly fall into the category of AI-generated content (AIGC) in science. We discuss the issue of evaluating diffusion models in the graph domain and the existing challenges.
• Score: 9.887032352886052
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: 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.

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