【推荐理由】图扩散模型的应用主要属于科学中的人工智能生成内容（AIGC），本文主要关注图扩散模型如何用于生成分子和蛋白质以及材料设计。
A Survey on Graph Diffusion Models: Generative AI in Science for Molecule, Protein and Material
Mengchun Zhang, Maryam Qamar, Taegoo Kang, Yuna Jung, Chenshuang Zhang, Sung-Ho Bae, Chaoning Zhang
[KAIST]
【论文链接】https://arxiv.org/pdf/2304.01565.pdf
【摘要】扩散模型已成为各个领域中新的SOTA生成建模方法，有多个综述工作提供了总体概述。随着扩散模型文章数量在过去几年呈指数增长，需要进行特定领域的扩散模型综述的需求也越来越多。本文致力于对图扩散模型进行综述。尽管文章的重点是涵盖扩散模型在图中的进展，但我们首先简要总结了其他生成建模方法在图中的应用。之后，作者介绍了各种形式的扩散模型机制，这有助于讨论图扩散模型。图扩散模型的应用主要属于科学中的人工智能生成内容（AIGC），其中主要关注图扩散模型如何用于生成分子和蛋白质，但也涵盖其他情况，包括材料设计。此外，文章还讨论了在图领域中评估扩散模型的问题和现有的挑战。
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