Cross-Domain Trajectory Association Based on Hierarchical Spatiotemporal Enhanced Attention Hypergraph

Abstract

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.