Abstract
In the past few decades, we have witnessed the flourishing of location-based social networks (LBSNs), where many users tend to create different accounts on multiple platforms to enjoy various services. Benefiting from the large-scale check-in data generated on LBSNs, the task of location-based user identity linkage (UIL) has attracted increasing attention recently. Despite the great contributions made by existing work on location-based UIL, they usually investigate the task with data mining methods, which are hard to extract and utilize the latent features contained by check-in records for more precise user identity linkage. In view of the deficiencies of existing studies, we propose a graph convolutional network (GCN) based model namely GCNUL that consists of a GCN-based encoder, an interaction layer, and a classifier, to fully exploit the spatial features hidden in check-in records. Specifically, the GCN-based encoder aims to exploit the spatial proximity of check-in records and mine user mobility patterns. The interaction layer is developed to capture deep correlations between users’ behaviors explicitly. The extensive experiments conducted on two real-world datasets demonstrate that our proposed model GCNUL outperforms the state-of-the-art methods.
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Acknowledgments
This work is supported by the National Natural Science Foundation of China No. 62272332, the Major Program of the Natural Science Foundation of Jiangsu Higher Education Institutions of China No. 22KJA520006.
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Li, Q., Zhou, Q., Chen, W., Zhao, L. (2023). User Identity Linkage via Graph Convolutional Network Across Location-Based Social Networks. In: Garrigós, I., Murillo Rodríguez, J.M., Wimmer, M. (eds) Web Engineering. ICWE 2023. Lecture Notes in Computer Science, vol 13893. Springer, Cham. https://doi.org/10.1007/978-3-031-34444-2_12
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