| Dynamic graph convolutional recurrent network for traffic prediction: Benchmark and solution F Li, J Feng, H Yan, G Jin, F Yang, F Sun, D Jin, Y Li ACM Transactions on Knowledge Discovery from Data 17 (1), 1-21, 2023 | 246 | 2023 |
| Urban ride-hailing demand prediction with multiple spatio-temporal information fusion network G Jin, Y Cui, L Zeng, H Tang, Y Feng, J Huang Transportation Research Part C: Emerging Technologies 117, 102665, 2020 | 99 | 2020 |
| Spatio-temporal graph neural networks for predictive learning in urban computing: A survey G Jin, Y Liang, Y Fang, J Huang, J Zhang, Y Zheng IEEE Transactions on Knowledge and Data Engineering, 2023 | 82 | 2023 |
| STGNN-TTE: Travel time estimation via spatial–temporal graph neural network G Jin, M Wang, J Zhang, H Sha, J Huang Future Generation Computer Systems 126, 70-81, 2022 | 51 | 2022 |
| CSAN: A neural network benchmark model for crime forecasting in spatio-temporal scale Q Wang, G Jin, X Zhao, Y Feng, J Huang Knowledge-Based Systems 189 (105120), 2020 | 50 | 2020 |
| Addressing crime situation forecasting task with temporal graph convolutional neural network approach G Jin, Q Wang, C Zhu, Y Feng, J Huang, J Zhou 2020 12th International Conference on Measuring Technology and Mechatronics …, 2020 | 40 | 2020 |
| Deep multi-view graph-based network for citywide ride-hailing demand prediction G Jin, Z Xi, H Sha, Y Feng, J Huang Neurocomputing 510, 79-94, 2022 | 31* | 2022 |
| Automated Dilated Spatio-Temporal Synchronous Graph Modeling for Traffic Prediction G Jin, F Li, J Zhang, M Wang, J Huang IEEE Transactions on Intelligent Transportation Systems 24 (8), 8820-8830, 2022 | 29 | 2022 |
| Deep spatio-temporal adaptive 3d convolutional neural networks for traffic flow prediction H Li, X Li, L Su, D Jin, J Huang, D Huang ACM Transactions on Intelligent Systems and Technology (TIST) 13 (2), 1-21, 2022 | 28 | 2022 |
| Detection and analysis of real-time anomalies in large-scale complex system S Chen, G Jin, X Ma Measurement 184, 109929, 2021 | 27 | 2021 |
| GSEN: An ensemble deep learning benchmark model for urban hotspots spatiotemporal prediction G Jin, H Sha, Y Feng, Q Cheng, J Huang Neurocomputing 455, 353-367, 2021 | 24* | 2021 |
| Adaptive Dual-View WaveNet for urban spatial–temporal event prediction G Jin, C Liu, Z Xi, H Sha, Y Liu, J Huang Information Sciences 588, 315-330, 2022 | 23 | 2022 |
| Spatio-Temporal Dual Graph Neural Networks for Travel Time Estimation G Jin, H Yan, F Li, J Huang, Y Li ACM Transactions on Spatial Algorithms and Systems, 2023 | 21* | 2023 |
| Urban Fire Situation Forecasting: Deep sequence learning with spatio-temporal dynamics G Jin, Q Wang, C Zhu, Y Feng, J Huang, X Hu Applied Soft Computing 97, 106730, 2020 | 21 | 2020 |
| A spatiotemporal graph generative adversarial networks for short-term passenger flow prediction in urban rail transit systems J Zhang, H Li, S Zhang, L Yang, G Jin, J Qi International Journal of General Systems, 1-28, 2023 | 18* | 2023 |
| Crime-GAN: A context-based sequence generative network for crime forecasting with adversarial loss G Jin, Q Wang, X Zhao, Y Feng, Q Cheng, J Huang 2019 IEEE International Conference on Big Data (Big Data), 1460-1469, 2019 | 18 | 2019 |
| Ufsp-net: a neural network with spatio-temporal information fusion for urban fire situation prediction G Jin, C Zhu, X Chen, H Sha, X Hu, J Huang IOP Conference Series: Materials Science and Engineering 853 (1), 012050, 2020 | 17 | 2020 |
| Automated spatio-temporal synchronous modeling with multiple graphs for traffic prediction F Li, H Yan, G Jin, Y Liu, Y Li, D Jin Proceedings of the 31st ACM International Conference on Information …, 2022 | 14 | 2022 |
| Hierarchical neural architecture search for travel time estimation G Jin, H Yan, F Li, Y Li, J Huang Proceedings of the 29th International Conference on Advances in Geographic …, 2021 | 13 | 2021 |
| Urban hotspot forecasting via automated spatio-temporal information fusion G Jin, H Sha, Z Xi, J Huang Applied Soft Computing 136, 110087, 2023 | 11 | 2023 |
Yong LiAssociate Professor, Electronic Engineering, Tsinghua University在 tsinghua.edu.cn 的电子邮件经过验证
