🤖 AI Summary
This work addresses the challenge of linking anonymized trajectories to user identities, a task hindered by the difficulty of effectively integrating multi-source heterogeneous mobility semantics and leveraging trajectory structural knowledge. To this end, the study introduces knowledge graph representation learning into the trajectory-user linkage problem for the first time, constructing a multi-relational mobility knowledge graph that models visit time, POI category, and transition speed as typed relations. These relations jointly constrain POI embeddings and incorporate higher-order co-occurrence patterns to enrich representations. A dual-branch classifier is further designed to fuse structural and sequential evidence. The proposed method achieves significant improvements in linkage accuracy, demonstrating particularly strong performance in scenarios involving sparse and overlapping trajectories.
📝 Abstract
Trajectory-User Linking (TUL) aims to identify the owner of an anonymous trajectory from a set of candidate users, providing a basis for user mobility analysis and personalized location-aware services. Existing methods often learn Point of Interest (POI), temporal, and semantic features independently, make limited use of structural knowledge shared across trajectories, and compress structural and sequential information before classification. To address these issues, we propose Multi-Relational Knowledge Graph Enhanced Embedding for Trajectory-User Linking (MakeTUL), which, to the best of our knowledge, is the first attempt to introduce knowledge graph representation learning into TUL. MakeTUL organizes visit-time, POI-category, and transfer-speed information as typed relations in a multi-relational mobility knowledge graph, allowing heterogeneous mobility semantics to jointly constrain the learned embeddings. The resulting POI representations are further enriched with high-order co-occurrence patterns extracted from the trajectory collection, providing structural prior knowledge for sparse and overlapping trajectories. By integrating these prior-enhanced representations with temporal, category, and transfer information, the trajectory sequence learning module captures ordered mobility patterns, while a dual-branch classification layer preserves and combines global structural evidence and sequential evidence at the decision level.