{"type":"ScholarlyArticle","title":"Continuous-Time Graph Learning for Cascade Popularity Prediction","authors":[{"name":"Xiaodong Lu","slug":"xiaodong-lu","given":"Xiaodong","family":"Lu","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Shuo Ji","slug":"shuo-ji","given":"Shuo","family":"Ji","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Le Yu","slug":"le-yu","given":"Le","family":"Yu","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Leilei Sun","slug":"leilei-sun","given":"Leilei","family":"Sun","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Bowen Du","slug":"bowen-du","given":"Bowen","family":"Du","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"},{"name":"Tongyu Zhu","slug":"tongyu-zhu","given":"Tongyu","family":"Zhu","affiliation":"SKLSDE Lab , Beihang University , Beijing 100191 , China"}],"abstract":"Information propagation on social networks could be modeled as cascades, and many efforts have been made to predict the future popularity of cascades. However, most of the existing research treats a cascade as an individual sequence. Actually, the cascades might be correlated with each other due to the shared users or similar topics. Moreover, the preferences of users and semantics of a cascade are usually continuously evolving over time. In this paper, we propose a continuous-time graph learning method for cascade popularity prediction, which first connects different cascades via a universal sequence of user-cascade and user-user interactions and then chronologically learns on the sequence by maintaining the dynamic states of users and cascades. Specifically, for each interaction, we present an evolution learning module to continuously update the dynamic states of the related users and cascade based on their currently encoded messages and previous dynamic states. We also devise a cascade representation learning component to embed the temporal information and structural information carried by the cascade. Experiments on real-world datasets demonstrate the superiority and rationality of our approach.","doi":"10.24963/ijcai.2023/247","url":"https://ijcai.org/proceedings/2023/247","pdf":"https://ijcai.org/proceedings/2023/0247.pdf","markdown":"https://ijcai.org/proceedings/2023/247.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/247","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":"Data Mining","track":"Main Track","number":247,"pages":{"first":2224,"last":2232},"keywords":["Data Mining: DM: Mining text, web, social media","Data Mining: DM: Mining spatial and/or temporal data"],"videos":[],"references":[{"position":1,"raw":"[Cao et al., 2017] Qi Cao, Huawei Shen, Keting Cen, Wen- tao Ouyang, and Xueqi Cheng. 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