{"type":"ScholarlyArticle","title":"Temporal Graph ODEs for Irregularly-Sampled Time Series","authors":[{"name":"Alessio Gravina","slug":"alessio-gravina","given":"Alessio","family":"Gravina","affiliation":"University of Pisa , Pisa , Italy"},{"name":"Daniele Zambon","slug":"daniele-zambon","given":"Daniele","family":"Zambon","affiliation":"The Swiss AI Lab IDSIA , Università della Svizzera italiana , Lugano , Switzerland"},{"name":"Davide Bacciu","slug":"davide-bacciu","given":"Davide","family":"Bacciu","affiliation":"University of Pisa , Pisa , Italy"},{"name":"Cesare Alippi","slug":"cesare-alippi","given":"Cesare","family":"Alippi","affiliation":"The Swiss AI Lab IDSIA , Università della Svizzera italiana , Lugano , Switzerland; Politecnico di Milano , Milan , Italy"}],"abstract":"Modern graph representation learning works mostly under the assumption of dealing with regularly sampled temporal graph snapshots, which is far from realistic, e.g., social networks and physical systems are characterized by continuous dynamics and sporadic observations. To address this limitation, we introduce the Temporal Graph Ordinary Differential Equation (TG-ODE) framework, which learns both the temporal and spatial dynamics from graph streams where the intervals between observations are not regularly spaced. We empirically validate the proposed approach on several graph benchmarks, showing that TG-ODE can achieve state-of-the-art performance in irregular graph stream tasks.","doi":"10.24963/ijcai.2024/445","url":"https://ijcai.org/proceedings/2024/445","pdf":"https://ijcai.org/proceedings/2024/0445.pdf","markdown":"https://ijcai.org/proceedings/2024/445.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/445","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Machine Learning","track":"Main Track","number":445,"pages":{"first":4025,"last":4034},"keywords":["Machine Learning: ML: Time series and data streams","Machine Learning: ML: Sequence and graph learning","Machine Learning: ML: Deep learning architectures"],"videos":[],"references":[{"position":1,"raw":"[Ascher and Petzold, 1998] Uri M. 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