Abstract
With the widespread adoption of mobile internet, users generate vast amounts of location-based data across multiple social networking platforms. This data is valuable for applications such as personalized recommendations and targeted advertising. Accurately identifying users across different platforms enhances understanding of user behavior and preferences. To address the complexity of cross-domain user identification caused by varying check-in frequencies and data precision differences, we propose HTEGAT, a hierarchical trajectory embedding-based graph attention network model. HTEGAT addresses these issues by combining an Encoder and a Trajectory Identification module. The Encoder module, by integrating self-attention mechanisms with LSTM, can effectively extract location point-level features and accurately capture trajectory transition features, thereby accurately characterizing hierarchical temporal trajectories. Trajectory Identification module introduces trajectory distance-neighbor relationships and constructs an adjacency matrix based on these relationships. By utilizing attention weight coefficients in a graph attention network to capture similarities between trajectories, this approach reduces identification complexity while addressing the issue of dataset sparsity. Experiments on two cross-domain Location-Based Social Network (LBSN) datasets demonstrate that HTEGAT achieves higher hit rates with lower time complexity. On the Foursquare-Twitter dataset, HTEGAT significantly improved hit rates, surpassing state-of-the-art methods. On the Instagram-Twitter dataset, HTEGAT consistently outperformed contemporary models, showcasing its effectiveness and superiority.
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References
Chen, M., & Peng, A. Y. (2023). Why do people choose different social media platforms? Linking use motives with social media affordances and personalities. Social Science Computer Review, 41(2), 330–352.
Chen, W., Huang, C., Yu, Y., et al. (2024). Trajectory-user linking via hierarchical spatio-temporal attention networks. ACM Transactions on Knowledge Discovery from Data, 18(4), 1–22.
Chen, W., Wang, W., Yin, H., Zhao, L., & Zhou, X. (2023). HFUL: A hybrid framework for user account linkage across location-aware social networks. The VLDB Journal, 32, 1–22.
Chen, W., Yin, H., Wang, W., Zhao, L., & Zhou, X. (2018). Effective and efficient user account linkage across location based social networks. In 2018 IEEE 34th international conference on data engineering (ICDE) (pp. 1085–1096). IEEE.
Feng, J., Li, Y., Yang, Z., Zhang, M., Wang, H., Cao, H., & Jin, D. (2020). User identity linkage via co-attentive neural network from heterogeneous mobility data. IEEE Transactions on Knowledge and Data Engineering, 34, 954–968.
Gao, H., & Liu, H. (2014). Data analysis on location-based social networks. In A. Chin & D. Zhang (Eds.), Mobile social networking (pp. 165–194). Springer.
Gao, Q., Zhou, F., Zhang, K., Trajcevski, G., Luo, X., & Zhang, F. (2017). Identifying human mobility via trajectory embeddings. In IJCAI (pp. 1689–1695).
Goga, O., Lei, H., Parthasarathi, S.H.K., Friedland, G., Sommer, R., & Teixeira, R. (2013). Exploiting innocuous activity for correlating users across sites. In Proceedings of the 22nd international conference on World Wide Web (pp. 447–458). ACM.
Han, X., Wang, L., Xu, L., & Zhang, S. (2016). Social media account linkage using user-generated geo-location data. In 2016 IEEE conference on intelligence and security informatics (ISI) (pp. 157–162). IEEE.
Jin, F., Hua, W., Xu, J., & Zhou, X. (2019). Moving object linking based on historical trace. In 2019 IEEE 35th international conference on data engineering (ICDE) (pp. 1058–1069). IEEE.
Li, X., Zhao, K., Cong, G., Jensen, C.S., & Wei, W. (2018). Deep representation learning for trajectory similarity computation. In 2018 IEEE 34th international conference on data engineering (ICDE) (pp. 617–628). IEEE.
Li, Y., Fan, Z., Yin, D., Jiang, R., Deng, J., & Song, X. (2023). HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation. World Wide Web, 26, 1625–1648. https://doi.org/10.1007/s11280-022-01092-5
Li, Y., Fan, Z., Zhang, J., Shi, D., Xu, T., Yin, D., Deng, J., & Song, X. (2022). Heterogeneous hypergraph neural network for friend recommendation with human mobility. In Proceedings of the 31st ACM international conference on information & knowledge management (pp. 4209–4213). ACM.
Liang, W., & Zhang, W. (2020). Learning social relations and spatiotemporal trajectories for next check-in inference. IEEE Transactions on Neural Networks and Learning Systems., 34, 1789–1799.
Ma, X., Ding, F., Peng, K., Yang, Y., & Wang, C. (2022). CP-link: Exploiting continuous spatio-temporal check-in patterns for user identity linkage. IEEE Transactions on Mobile Computing, 22, 4594–4606.
Narayanan, A., & Shmatikov, V. (2008). Robust de-anonymization of large sparse datasets. In 2008 IEEE symposium on security and privacy (sp 2008) (pp. 111–125). IEEE.
Park, D., Jaewoo J., & Yoon, K.-J. (2024). Improving transferability for cross-domain trajectory prediction via neural stochastic differential equation. In Proceedings of the international AAAI conference on artificial intelligence (Vol. 38. No. 9).
Park, S., Xu, Y., Jiang, L., Chen, Z., & Huang, S. (2020). Spatial structures of tourism destinations: A trajectory data mining approach leveraging mobile big data. Annals of Tourism Research, 84, 102973.
Riederer, C., Kim, Y., Chaintreau, A., Korula, N., & Lattanzi, S. (2016). Linking users across domains with location data: Theory and validation. In Proceedings of the 25th international conference on World Wide Web. International World Wide Web Conferences Steering Committee, Montréal Québec Canada (pp. 707–719).
Rossi, L., & Musolesi, M. (2014). It’s the way you check-in: Identifying users in location-based social networks. In Proceedings of the second ACM conference on Online social networks (pp. 215–226). ACM.
Shao, J., Wang, Y., Gao, H., Shi, B., Shen, H., & Cheng, X. (2023). AsyLink: User identity linkage from text to geo-location via sparse labeled data. Neurocomputing, 515, 174–184.
Sojahrood, Z. B., Taleai, M., & Cheng, H. (2023). Hybrid POI group recommender system based on group type in LBSN. Expert Systems with Applications, 219, 119681.
Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. InAdvances in neural information processing systems (Vol. 27).
Veličković, P., Cucurull, G., Casanova, A., Romero, A., Li, P., & Bengio, Y. (2018). Graph attention networks.
Wan, S., Lan, Y., Guo, J., Xu, J., Pang, L., & Cheng, X. (2016). A deep architecture for semantic matching with multiple positional sentence representations. In Proceedings of the AAAI conference on artificial intelligence.
Wang, H., Li, Y., Wang, G., & Jin, D. (2021). Linking multiple user identities of multiple services from massive mobility traces. ACM Transactions on Intelligent Systems and Technology, 12, 1–28. https://doi.org/10.1145/3439817
Wang, Y., Feng, C., Chen, L., Yin, H., Guo, C., & Chu, Y. (2019). User identity linkage across social networks via linked heterogeneous network embedding. World Wide Web, 22, 2611–2632. https://doi.org/10.1007/s11280-018-0572-3
Werneck, H., Silva, N., Viana, M., Pereira, A. C., Mourao, F., & Rocha, L. (2021). Points of interest recommendations: Methods, evaluation, and future directions. Information Systems, 101, 101789.
Wu, Y., Li, K., Zhao, G., & Qian, X. (2020). Personalized long-and short-term preference learning for next POI recommendation. IEEE Transactions on Knowledge and Data Engineering, 34, 1944–1957.
Xu, F., Tu, Z., Huang, H., Chang, S., Sun, F., Guo, D., & Li, Y. (2019). No more than what I post: Preventing linkage attacks on check-in services. In The World Wide Web conference (pp. 3405–3412). ACM.
Xu, F., Zhang, G., Chen, Z., Huang, J., Li, Y., Yang, D., Zhao, B. Y., & Meng, F. (2018). Understanding motivations behind inaccurate check-ins. Proceedings of the ACM on Human-Computer Interaction, 2, 1–22. https://doi.org/10.1145/3274457
Xue, H., Sun, B., Mao, W., Lin, J., Zhang, Y., Liu, X., Yang, X., & Chen, Z. (2022). Spatial density-based user identity linkage across social networks. In 2022 IEEE international conference on big data (big data) (pp. 656–664). IEEE.
Yang, P., Wang, H., Zhang, Y., Qin, L., Zhang, W., & Lin, X. (2021). T3s: Effective representation learning for trajectory similarity computation. In 2021 IEEE 37th international conference on data engineering (ICDE) (pp. 2183–2188).
Yang, Z., Wang, Z., Guo, L., Liu, W., & Sun, S. (2022). Meta path-aware recommendation method based on non-negative matrix factorization in LBSN. IEEE Transactions on Network and Service Management, 19, 4284–4297.
Ye, H., & Chen, Y. (2024). Personalized POI recommendation using CAGRU and implicit semantic feature extraction in LBSN. International Journal on Semantic Web and Information Systems (IJSWIS), 20(1), 1–20.
Zhang, J., Kong, X., & Yu, P.S. (2014). Transferring heterogeneous links across location-based social networks. In Proceedings of the 7th ACM international conference on Web search and data mining (pp. 303–312). ACM.
Zhang, M., Wu, S., Yu, X., Liu, Q., & Wang, L. (2022). Dynamic graph neural networks for sequential recommendation. IEEE Transactions on Knowledge and Data Engineering, 35(5), 4741–4753.
Zhang, W., Lai, X., & Wang, J. (2020). Social link inference via multiview matching network from spatiotemporal trajectories. IEEE transactions on neural networks and learning systems., 34, 1720–1731.
Zhang, W., Zhang, Y., Zhang, J., & Wang, J. (2021). A user trajectory identification model with fusion of spatio-temporal behavior and social relation. Journal of Computer Science and Technology, 44, 2173–2188.
Zhu, Y., Tang, Z., Liu, Y., Zhuang, F., Xie, R., Zhang, X., & He, Q. (2022). Personalized transfer of user preferences for cross-domain recommendation. In Proceedings of the fifteenth ACM international conference on web search and data mining (pp. 1507–1515).
Acknowledgements
Ze Wang was supported by the Tianjin Technical Innovation Guidance Special Project under Grant 22YDTPJC00140 and Key Project Foundatio of Tianjin under Grant 20212053.
Funding
Ze Wang was supported by the Tianjin Technical Innovation Guidance Special Project under Grant 22YDTPJC00140 and Key Project Foundation of Tianjin under Grant 20212053.
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Cen wrote the main manuscript,Yang and Wang research the model,Yang and Cen do the experiment,Dong wrote the part manuscript. All authors reviewed the manuscript.
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Cen, K., Yang, Z., Wang, Z. et al. A cross-domain user association scheme based on graph attention networks with trajectory embedding. Mach Learn 113, 7905–7930 (2024). https://doi.org/10.1007/s10994-024-06613-z
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DOI: https://doi.org/10.1007/s10994-024-06613-z