A journal of IEEE and CAA , publishes
high-quality papers in English on original
theoretical/experimental research
and development in all areas of automation
Volume 13
Issue 8
IEEE/CAA Journal of Automatica Sinica
| Citation: | H. Wu, L. Yang, and Z. Zhang, “Latent factorization of tensors in hyperbolic space for spatiotemporal traffic data imputation,” IEEE/CAA J. Autom. Sinica, vol. 13, no. 8, pp. 1991–1993, Aug. 2026. doi: 10.1109/JAS.2024.124911
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| [1] |
H. Wu, X. Wu, and X. Luo, Dynamic network representation based on latent factorization of tensors. Singapore: Springer Singapore, 2023.
|
| [2] |
Z. Yang, L. T. Yang, L. Yi, X. Deng, C. Zhu, and Y. Ruan, “Transforms-based Bayesian tensor completion method for network traffic measurement data recovery,” IEEE Trans. on Network Science and Engineering, vol. 11, no. 3, pp. 2497−2509, 2024.
|
| [3] |
A. Baggag, S. Abbar, A. Sharma, T. Zanouda, A. Al-Homaid, A. Mohan, and J. Srivastava, “Learning spatiotemporal latent factors of traffic via regularized tensor factorization: imputing missing values and forecasting,” IEEE Trans. Knowledge and Data Engineering, vol. 33, no. 6, pp. 2573−2587, 2021.
|
| [4] |
A. Ben Said and A. Erradi, “Spatiotemporal tensor completion for improved urban traffic imputation,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 7, pp. 6836−6849, 2022.
|
| [5] |
Y. Zhu, J. Wang, J. Wang, and Z. He, “Multitask neural tensor factorization for road traffic speed-volume correlation pattern learning and joint imputation,” IEEE Trans. Intelligent Transportation Systems, vol. 23, no. 12, pp. 24550−24560, 2022.
|
| [6] |
J. Xing, R. Liu, K. Anish, and Z. Liu, “A customized data fusion tensor approach for interval-wise missing network volume imputation,” IEEE Trans. Intelligent Transportation Systems, vol. 24, no. 11, pp. 12107−12122, 2023.
|
| [7] |
B. Hui and W. Ku. “Low-rank nonnegative tensor decomposition in hyperbolic space,” in Proc. of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, pp. 646−654, 2022.
|
| [8] |
Y. Tan, C. Yang, X. Wei, C. Chen, L. Li, and X. Zheng, “Enhancing recommendation with automated tag taxonomy construction in hyperbolic space,” in Proc. of the IEEE 38th International Conference on Data Engineering (ICDE), Kuala Lumpur, Malaysia, 2022, pp. 1180−1192.
|
| [9] |
A. Li, B. Yang, H. Huo, H. Chen, G. Xu, and Z. Wang, “Hyperbolic neural collaborative recommender,” IEEE Trans. on Knowledge and Data Engineering, vol. 35, no. 9, pp. 9114−9127, 2023.
|
| [10] |
L. Wang, C. Gao, C. Huang, R. Liu, W. Ma, and S. Vosoughi, “Embedding heterogeneous networks into Hyperbolic space without Meta-path”, Proc. of the AAAI Conference on Artificial Intelligence, vol. 35, no. 11, pp. 10147−10155, 2021.
|
| [11] |
M. Nickel and D. Kiela, “Poincaré embeddings for learning hierarchical representations,” in Proc. of the 31st conference on Neural Information Processing Systems, Long Beach, CA, USA, 2017, pp. 6341−6350.
|
| [12] |
M. Zhao, L. Wu, Y. Liang, L. Chen, J. Zhang, Q. Deng, K. Wang, X. Shen, T. Lv, and R. Wu, “Investigating accuracy-novelty performance for graph-based collaborative filtering,” in Proc. of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, Spain, 2022, pp. 50−59.
|
| [13] |
R. Berg, T. N. Kipf, and M. Welling, “Graph convolutional matrix completion,” in Proc. of the 24th ACM SIGKDD Conference on Knowledge Discovery & Data Mining, London, UK, 2018.
|
| [14] |
J. Wu, X. Wang, F. Feng, X. He, L. Chen, J. Lian, and X. Xie, “Self-supervised graph learning for recommendation,” in Proc. of the 44th international ACM SIGIR conference on research and development in information retrieval, Virtual Event, Canada, 2021, pp. 726−735.
|
| [15] |
W. Guo, Y. Yang, Y. Hu, C. Wang, H. Guo, Y. Zhang, R. Tang, W. Zhang, and X. He, “Deep graph convolutional networks with hybrid normalization for accurate and diverse recommendation,” in Proc. of 3rd Workshop on Deep Learning Practice for High-Dimensional Sparse Data with KDD, Singapore, 2021.
|
| [16] |
W. Fan, X. Liu, W. Jin, X. Zhao, J. Tang, and Q. Li, “Graph trend filtering networks for recommendation,” in Proc. of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval, Madrid, 2022, pp. 112−121.
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