{"type":"ScholarlyArticle","title":"Dual-Channel Hybrid Graph Neural Network for Mobility Social Relationship Inference","authors":[{"name":"Liangkun Chen","slug":"liangkun-chen","given":"Liangkun","family":"Chen","affiliation":""},{"name":"Xiang Li","slug":"xiang-li","given":"Xiang","family":"Li","affiliation":""},{"name":"Guiyuan Jiang","slug":"guiyuan-jiang","given":"Guiyuan","family":"Jiang","affiliation":""},{"name":"Zhongying Zhao","slug":"zhongying-zhao","given":"Zhongying","family":"Zhao","affiliation":""},{"name":"Junyu Dong","slug":"junyu-dong","given":"Junyu","family":"Dong","affiliation":""},{"name":"Yanwei Yu","slug":"yanwei-yu","given":"Yanwei","family":"Yu","affiliation":""}],"abstract":"Inferring latent social ties from large-scale spatiotemporal mobility traces is a foundational AI task with broad applicability. Existing hypergraph-based methods often model higher-order relations by treating hyperedges as static snapshots, thus failing to capture the temporal dynamics and co-evolution of user interactions. Meanwhile, many approaches still struggle to distinguish stable social gatherings from transient noisy co-occurrences. To address these challenges, we propose a novel Dual-Channel Hybrid Graph Neural Network (HyGNN) that jointly models temporal dynamics and high-order structural dependencies. The framework consists of two complementary components: (1) a temporal meeting graph channel that decomposes multi-user interactions into ordered snapshots and aligns trajectory evolution through message propagation, and (2) a gathering hypergraph channel that uses a structure-aware encoder with homogeneity-guided aggregation to filter noise in high-order co-occurrences. A mutual-information-aware fusion module integrates both views while preserving their distinct semantics. Extensive experiments across three real-world datasets show that HyGNN consistently outperforms state-of-the-art baselines in PRAUC and ROCAUC metrics, and robustly captures the nonlinear interplay between mobility and sociality. The code is available at https://github.com/KunLiangChen/HyGNN.","doi":"10.24963/ijcai.2026/278","url":"https://ijcai.org/proceedings/2026/278","pdf":"https://ijcai.org/proceedings/2026/0278.pdf","markdown":"https://ijcai.org/proceedings/2026/278.md","bibtex":"https://ijcai.org/proceedings/2026/bibtex/278","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":278,"pages":{"first":2501,"last":2509},"keywords":["Data Mining: Mining spatial and/or temporal data","Data Mining: Mining graphs"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}