---
title: "Dual-Channel Hybrid Graph Neural Network for Mobility Social Relationship Inference"
authors:
  - "Liangkun Chen"
  - "Xiang Li"
  - "Guiyuan Jiang"
  - "Zhongying Zhao"
  - "Junyu Dong"
  - "Yanwei Yu"
doi: "10.24963/ijcai.2026/278"
url: "https://ijcai.org/proceedings/2026/278"
pdf: "https://ijcai.org/proceedings/2026/0278.pdf"
venue: "Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence"
year: 2026
pages: "2501-2509"
track: "Main Track"
keywords:
  - "Data Mining: Mining spatial and/or temporal data"
  - "Data Mining: Mining graphs"
---

# Dual-Channel Hybrid Graph Neural Network for Mobility Social Relationship Inference

Liangkun Chen, Xiang Li, Guiyuan Jiang, Zhongying Zhao, Junyu Dong, Yanwei Yu

*Proceedings of the Thirty-Fifth International Joint Conference on Artificial Intelligence*, pages 2501-2509. DOI: [10.24963/ijcai.2026/278](https://doi.org/10.24963/ijcai.2026/278)

## 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.