{"type":"ScholarlyArticle","title":"Tracking Topological Shifts: How Can Dynamic Graph Invariant Learning Enable Reliable Out-of-Time Spatio-Temporal Prediction?","authors":[{"name":"Xinyan Hao","slug":"xinyan-hao","given":"Xinyan","family":"Hao","affiliation":""},{"name":"Huaiyu Wan","slug":"huaiyu-wan","given":"Huaiyu","family":"Wan","affiliation":""},{"name":"Shengnan Guo","slug":"shengnan-guo","given":"Shengnan","family":"Guo","affiliation":""},{"name":"Shaojiang Wang","slug":"shaojiang-wang","given":"Shaojiang","family":"Wang","affiliation":""},{"name":"Youfang Lin","slug":"youfang-lin","given":"Youfang","family":"Lin","affiliation":""}],"abstract":"Spatio-temporal graph networks form the foundation of modern traffic prediction, yet their deployment is fundamentally challenged by the pervasive reality of distribution shifts. While out-of-distribution (OOD) learning holds promise for robustness, existing methods rely on static graph structures, failing to capture the inherent topological dynamics of real-world traffic systems and thus limiting long-term deployment reliability. To bridge this gap, we propose DynaSTar, a Dynamic Spatio-Temporal Graph Invariant Learning model designed for reliable out-of-time (OOT) traffic prediction under evolving topologies. Our model employs a dynamic probabilistic graph structure, which is continuously refined through momentum-based updates and differentiable sparse sampling to model evolving inter-node dependencies. Besides, it utilizes node-level environment construction and modulated prediction to extract representations invariant to heterogeneous neighborhood fluctuations, enabling robust generalization. Comprehensive experiments on large-scale, long-term real-world datasets demonstrate that by effectively tracking evolving topological shifts, DynaSTar consistently outperforms state-of-the-art baselines across various OOT scenarios.","doi":"10.24963/ijcai.2026/296","url":"https://ijcai.org/proceedings/2026/296","pdf":"https://ijcai.org/proceedings/2026/0296.pdf","markdown":"https://ijcai.org/proceedings/2026/296.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/296","volume":{"slug":"2026","title":"Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2026","url":"https://ijcai.org/proceedings/2026/","year":2026,"location":"Bremen, Germany","dates":"15-21 August 2026","editors":"Diego Calvanese","isbn":"978-1-956792-09-6","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2026"},"section":"Data Mining","track":"Main Track","number":296,"pages":{"first":2662,"last":2670},"keywords":["Data Mining: Mining spatial and/or temporal data"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}