{"type":"ScholarlyArticle","title":"TESTN: A Triad-Enhanced Spatio-Temporal Network for Multi-Temporal POI Relationship Inference","authors":[{"name":"Hongyu Wang","slug":"hongyu-wang","given":"Hongyu","family":"Wang","affiliation":""},{"name":"Lisi Chen","slug":"lisi-chen","given":"Lisi","family":"Chen","affiliation":""},{"name":"Shuo Shang","slug":"shuo-shang","given":"Shuo","family":"Shang","affiliation":""}],"abstract":"Multi-temporal Point-of-Interest (POI) relationship inference aims to identify evolving relationships among locations over time, providing critical insights for location-based services. While existing studies have made substantial efforts to model relationships with custom-designed graph neural networks, they face the challenge of leveraging POI contextual information characterized by spatial dependencies and temporal dynamics, as well as capturing the heterogeneity of multi-type relationships. To address these challenges, we propose a Triad-Enhanced Spatio-Temporal Network (TESTN), which conceptualizes triads as interactions between relationships for capturing potential interplay. Specifically, TESTN incorporates the spatial 2-hop aggregation layer to capture geographical and semantic information beyond first-order neighbors and the temporal context extractor to integrate relational dynamics within adjacent time segments. Furthermore, we introduce a self-supervised pairwise neighboring relation consistency detection scheme to preserve the heterogeneity of multi-type relationships. Extensive experiments on three real-world datasets demonstrate the superior performance of our TESTN framework.","doi":"10.24963/ijcai.2025/866","url":"https://ijcai.org/proceedings/2025/866","pdf":"https://ijcai.org/proceedings/2025/0866.pdf","markdown":"https://ijcai.org/proceedings/2025/866.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/866","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":866,"pages":{"first":7786,"last":7794},"keywords":["Multidisciplinary Topics and Applications: MTA: Transportation"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}