{"type":"ScholarlyArticle","title":"Generative Diffusion Models on Graphs: Methods and Applications","authors":[{"name":"Chengyi Liu","slug":"chengyi-liu","given":"Chengyi","family":"Liu","affiliation":"The Hong Kong Polytechnic University"},{"name":"Wenqi Fan","slug":"wenqi-fan","given":"Wenqi","family":"Fan","affiliation":"The Hong Kong Polytechnic University"},{"name":"Yunqing Liu","slug":"yunqing-liu","given":"Yunqing","family":"Liu","affiliation":"The Hong Kong Polytechnic University"},{"name":"Jiatong Li","slug":"jiatong-li","given":"Jiatong","family":"Li","affiliation":"The Hong Kong Polytechnic University"},{"name":"Hang Li","slug":"hang-li","given":"Hang","family":"Li","affiliation":"Michigan State University"},{"name":"Hui Liu","slug":"hui-liu","given":"Hui","family":"Liu","affiliation":"Michigan State University"},{"name":"Jiliang Tang","slug":"jiliang-tang","given":"Jiliang","family":"Tang","affiliation":"Michigan State University"},{"name":"Qing Li","slug":"qing-li","given":"Qing","family":"Li","affiliation":"The Hong Kong Polytechnic University"}],"abstract":"Diffusion models, as a novel generative paradigm, have achieved remarkable success in various image generation tasks such as image inpainting, image-to-text translation, and video generation. Graph generation is a crucial computational task on graphs with numerous real-world applications. It aims to learn the distribution of given graphs and then generate new graphs. Given the great success of diffusion models in image generation, increasing efforts have been made to leverage these techniques to advance graph generation in recent years.  In this paper, we first provide a comprehensive overview of generative diffusion models on graphs, In particular, we review representative algorithms for three variants of graph diffusion models, i.e., Score Matching with Langevin Dynamics (SMLD), Denoising Diffusion Probabilistic Model (DDPM), and Score-based Generative Model (SGM). Then, we summarize the major applications of generative diffusion models on graphs with a specific focus on molecule and protein modeling. Finally, we discuss promising directions in generative diffusion models on graph-structured data.","doi":"10.24963/ijcai.2023/751","url":"https://ijcai.org/proceedings/2023/751","pdf":"https://ijcai.org/proceedings/2023/0751.pdf","markdown":"https://ijcai.org/proceedings/2023/751.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/751","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"Survey Track","track":"Survey Track","number":751,"pages":{"first":6702,"last":6711},"keywords":["Survey: Knowledge Representation and Reasoning","Survey: Machine Learning","Survey: Data Mining"],"videos":[],"references":[{"position":1,"raw":"[Anand and Achim, 2022] Namrata Anand and Tudor Achim. 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