Guangyin Jin

Guangyin Jin

Explorer of artificial intelligence and social complex systems

Publications

You can also find my articles on my Google Scholar profile.

If you need the PDF of any of the papers, please feel free to contact me.

Journal Articles

  1. Jin, G.., Ni, X., Wei, K., Zhao, J., Zhang, H., & Jia, L. (2025). Will the technological singularity come soon? Modeling the dynamics of artificial intelligence development via multi-logistic growth process. Physica A: Statistical Mechanics and its Applications, 130450.
  2. Wen, H., Lin, Y., Wu, L., Mao, X., Cai, T., Hou, Y., Jin, G.,… & Wan, H. (2024). A survey on service route and time prediction in instant delivery: Taxonomy, progress, and prospects. IEEE Transactions on Knowledge and Data Engineering.
  3. Shao, Z., Wang, F., Xu, Y., Wei, W., Yu, C., Zhang, Z., Jin, G., … & Cheng, X. (2024). Exploring progress in multivariate time series forecasting: Comprehensive benchmarking and heterogeneity analysis. IEEE Transactions on Knowledge and Data Engineering.
  4. Gao, M., Du, Z., Qin, H., Wang, W., Jin, G., & Xie, G. (2024). Dynamic multi-scale spatial-temporal graph convolutional network for traffic flow prediction. Knowledge-Based Systems, 305, 112586.
  5. Jin, G., Yan, H., Li, F., Huang, J., & Li, Y. (2024). Spatio-temporal dual graph neural networks for travel time estimation. ACM Transactions on Spatial Algorithms and Systems, 10(3), 1-22.
  6. Jin, G., ZHAO, X. J., & GONG, Y. X. (2024). Moving trajectory destination prediction based on long short-term memory network. Computer Engineering & Science, 46(03), 525.
  7. Yang, H., Wang, M., Wang, Q., Yu, Z., Jin, G., Zhou, C., & Zhou, Y. (2024). Non-informative noise-enhanced stochastic neural networks for improving adversarial robustness. Information Fusion, 108, 102397.
  8. Jin, G., Liang, Y., Fang, Y., Shao, Z., Huang, J., Zhang, J., & Zheng, Y. (2023). Spatio-temporal graph neural networks for predictive learning in urban computing: A survey. IEEE Transactions on Knowledge and Data Engineering.
  9. Jin, G., Yan, H., Li, F., Li, Y., & Huang, J. (2023). Dual graph convolution architecture search for travel time estimation. ACM Transactions on Intelligent Systems and Technology, 14(4), 1-23.
  10. Jin, G., Sha, H., Xi, Z., & Huang, J. (2023). Urban hotspot forecasting via automated spatio-temporal information fusion. Applied Soft Computing, 136, 110087.
  11. Zhang, J., Li, H., Zhang, S., Yang, L., Jin, G., & Qi, J. (2023). A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems. International Journal of General Systems, 52(6), 694-721.
  12. Li, F., Feng, J., Yan, H., Jin, G., Yang, F., Sun, F., … & Li, Y. (2023). Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution. ACM Transactions on Knowledge Discovery from Data, 17(1), 1-21.
  13. Zhang, J., Chen, Y., Panchamy, K., Jin, G., Wang, C., Yang, L. (2023). Attention-based Multi-step Short-term Passenger Flow Spatial-temporal Integrated Prediction Model in URT Systems. Journal of Geo-information Science, 25(4): 698-713
  14. Jin, G., Li, F., Zhang, J., Wang, M., & Huang, J. (2022). Automated dilated spatio-temporal synchronous graph modeling for traffic prediction. IEEE Transactions on Intelligent Transportation Systems, 24(8), 8820-8830.
  15. Jin, G.., Wang, M., Zhang, J., Sha, H., & Huang, J. (2022). STGNN-TTE: Travel time estimation via spatial–temporal graph neural network. Future Generation Computer Systems, 126, 70-81.
  16. Jin, G., Liu, C., Xi, Z., Sha, H., Liu, Y., & Huang, J. (2022). Adaptive dual-view wavenet for urban spatial–temporal event prediction. Information Sciences, 588, 315-330.
  17. Jin, G.., Xi, Z., Sha, H., Feng, Y., & Huang, J. (2022). Deep multi-view graph-based network for citywide ride-hailing demand prediction. Neurocomputing, 510, 79-94.
  18. Zhang, J., Chen, F., Yang, L., Ma, W., Jin, G., & Gao, Z. (2022). Network-wide link travel time and station waiting time estimation using automatic fare collection data: A computational graph approach. IEEE Transactions on Intelligent Transportation Systems, 23(11), 21034-21049.
  19. Sha, H. , Jin, G., Cheng, G., Huang, J., Wu, K. (2022). A Deep Urban Hotspots Prediction Framework with Modeling Geography-Semantic Dynamics. Journal of Geo-information Science, 24(1): 25-37
  20. Jin, G., Sha, H., Feng, Y., Cheng, Q., & Huang, J. (2021). GSEN: An ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction. Neurocomputing, 455, 353-367.
  21. Chen, S., Jin, G., & Ma, X. (2021). Detection and analysis of real-time anomalies in large-scale complex system. Measurement, 184, 109929.
  22. Jin, G., Cui, Y., Zeng, L., Tang, H., Feng, Y., & Huang, J. (2020). Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network. Transportation Research Part C: Emerging Technologies, 117, 102665.
  23. Jin, G., Wang, Q., Zhu, C., Feng, Y., Huang, J., & Hu, X. (2020). Urban Fire Situation Forecasting: Deep sequence learning with spatio-temporal dynamics. Applied Soft Computing, 97, 106730.
  24. Wang, Q., Jin, G., Zhao, X., Feng, Y., & Huang, J. (2020). CSAN: A neural network benchmark model for crime forecasting in spatio-temporal scale. Knowledge-Based Systems, 189, 105120.

Conference Articles

  1. Jin, G., Liu, L., Li, F., & Huang, J. (2023, June). Spatio-temporal graph neural point process for traffic congestion event prediction. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 37, No. 12, pp. 14268-14276).
  2. Jin, G., Yan, H., Li, F., Li, Y., & Huang, J. (2021, November). Hierarchical neural architecture search for travel time estimation. In Proceedings of the 29th International Conference on Advances in Geographic Information Systems (pp. 91-94).
  3. Li, F., Yan, H., Jin, G., Liu, Y., Li, Y., & Jin, D. (2022, October). Automated spatio-temporal synchronous modeling with multiple graphs for traffic prediction. In Proceedings of the 31st ACM International Conference on Information & Knowledge Management (pp. 1084-1093).
  4. Yan, H., Jin, G., Wang, D., Liu, Y., & Li, Y. (2022, August). Jointly Modeling Intersections and Road Segments for Travel Time Estimation via Dual Graph Convolutional Networks. In International Conference on Spatial Data and Intelligence (pp. 19-34). Cham: Springer Nature Switzerland.
  5. Sha, H., Jin, G., Cheng, G., Huang, J., & Huang, K. (2021). A Deep Urban Hotspots Prediction Framework with Modeling Geography-Semantic Dynamics. In Spatial Data and Intelligence: Second International Conference, SpatialDI 2021, Hangzhou, China, April 22–24, 2021, Proceedings 2 (pp. 3-14). Springer International Publishing.
  6. Jin, G., Sha, H., Feng, Y., Cheng, Q., & Huang, J. (2020). Modeling Spatiotemporal Geographic-Semantic Dynamics for Urban Hotspots Prediction. In ACM SIGKDD Workshop on Deep Learning for Spatiotemporal Data, Applications, and Systems
  7. Jin, G., Wang, Q., Zhu, C., Feng, Y., Huang, J., & Zhou, J. (2020, February). Addressing crime situation forecasting task with temporal graph convolutional neural network approach. In 2020 12th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA) (pp. 474-478). IEEE.
  8. Jin, G., Zhu, C., Chen, X., Sha, H., Hu, X., & Huang, J. (2020, May). Ufsp-net: a neural network with spatio-temporal information fusion for urban fire situation prediction. In IOP Conference Series: Materials Science and Engineering (Vol. 853, No. 1, p. 012050). IOP Publishing.
  9. Jin, G., Wang, Q., Zhao, X., Feng, Y., Cheng, Q., & Huang, J. (2019, December). Crime-GAN: A context-based sequence generative network for crime forecasting with adversarial loss. In 2019 IEEE International Conference on Big Data (Big Data) (pp. 1460-1469). IEEE.
  10. Gao, Y., Jin, G., Guo, Y., Zhu, G., Yang, Q., & Yang, K. (2019, October). Weighted area coverage of maritime joint search and rescue based on multi-agent reinforcement learning. In 2019 IEEE 3rd Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC) (pp. 593-597). IEEE.
  11. Jin, G., Huang, J., Feng, Y., Cheng, G., Liu, Z., & Wang, Q. (2018, December). Addressing the Task of Rocket Recycling with Deep Reinforcement Learning. In Proceedings of the 6th International Conference on Information Technology: IoT and Smart City (pp. 284-290).