Cross-Domain Trajectory Association Based on Hierarchical Spatiotemporal Enhanced Attention Hypergraph - Wu, Chenlong; Wang, Ze; Cen, Keqing; Bai, Yude; Hao, Jin (Proceedings of the AAAI Conference on Artificial Intelligence, 2025)
Identifying and linking the same users across different social platforms is crucial for understanding user behavior and preferences. However, cross-domain datasets exhibit diverse characteristics, such as varying check-in frequencies, significant disparities in data precision, and distinct distributions. Existing trajectory representations rely on recurrent neural network, which fails to dynamically learn multi-dimensional feature relations and capture high-order associations. Furthermore, current methods for integrating trajectory information fails to capture the complex relations and dynamic variations among cross-domain mobility trajectories. To this end, we propose the Hierarchical Spatio-Temporal Enhanced Attention Hypergraph Network (StarNet). This model dynamically regulates the multi-dimensional features of trajectories through a locally enhanced spatiotemporal graph neural network. Meanwhile, StarNet employs a hypergraph network enhanced by a global spatiotemporal to capture high-order associations between cross-domain trajectories. The fusion enhancement association integrates local and global information, which enables this model to link user identities. Extensive experiments on two well-known LBSN cross-domain datasets reveal that StarNet outperforms state-of-the-art baselines in the accuracy of user identity linkage.