GCN-TULHOR: Trajectory-User Linking Leveraging GCNs and Higher-Order Spatial Representations

• URL: http://arxiv.org/abs/2509.11095v1
• Date: Sun, 14 Sep 2025 05:14:09 GMT
• Title: GCN-TULHOR: Trajectory-User Linking Leveraging GCNs and Higher-Order Spatial Representations
• Authors: Khoa Tran, Pranav Gupta, Manos Papagelis,
• Abstract summary: Trajectory-user linking (TUL) aims to associate anonymized trajectories with the users who generated them.<n>In this paper, we introduce GCN-TULHOR, a method that transforms raw location data into higher-order mobility flow representations.<n>Our approach converts both sparse check-in and continuous GPS trajectory data into unified higher-order flow representations.
• Score: 3.704533038474922
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Trajectory-user linking (TUL) aims to associate anonymized trajectories with the users who generated them, which is crucial for personalized recommendations, privacy-preserving analytics, and secure location-based services. Existing methods struggle with sparse data, incomplete routes, and limited modeling of complex spatial dependencies, often relying on low-level check-in data or ignoring spatial patterns. In this paper, we introduced GCN-TULHOR, a method that transforms raw location data into higher-order mobility flow representations using hexagonal tessellation, reducing data sparsity and capturing richer spatial semantics, and integrating Graph Convolutional Networks (GCNs). Our approach converts both sparse check-in and continuous GPS trajectory data into unified higher-order flow representations, mitigating sparsity while capturing deeper semantic information. The GCN layer explicitly models complex spatial relationships and non-local dependencies without requiring side information such as timestamps or points of interest. Experiments on six real-world datasets show consistent improvements over classical baselines, RNN- and Transformer-based models, and the TULHOR method in accuracy, precision, recall, and F1-score. GCN-TULHOR achieves 1-8% relative gains in accuracy and F1. Sensitivity analysis identifies an optimal setup with a single GCN layer and 512-dimensional embeddings. The integration of GCNs enhances spatial learning and improves generalizability across mobility data. This work highlights the value of combining graph-based spatial learning with sequential modeling, offering a robust and scalable solution for TUL with applications in recommendations, urban planning, and security.

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