| Heterogeneous graph attention networks for semi-supervised short text classification H Linmei, T Yang, C Shi, H Ji, X Li Proceedings of the 2019 conference on empirical methods in natural language …, 2019 | 378 | 2019 |
| Metapath-guided heterogeneous graph neural network for intent recommendation S Fan, J Zhu, X Han, C Shi, L Hu, B Ma, Y Li Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 350 | 2019 |
| Graph neural news recommendation with long-term and short-term interest modeling L Hu, C Li, C Shi, C Yang, C Shao Information Processing & Management 57 (2), 102142, 2020 | 196 | 2020 |
| Compare to the knowledge: Graph neural fake news detection with external knowledge L Hu, T Yang, L Zhang, W Zhong, D Tang, C Shi, N Duan, M Zhou Proceedings of the 59th Annual Meeting of the Association for Computational …, 2021 | 150 | 2021 |
| Graph neural news recommendation with unsupervised preference disentanglement L Hu, S Xu, C Li, C Yang, C Shi, N Duan, X Xie, M Zhou Proceedings of the 58th annual meeting of the association for computational …, 2020 | 140 | 2020 |
| Relation structure-aware heterogeneous information network embedding Y Lu, C Shi, L Hu, Z Liu Proceedings of the AAAI conference on artificial intelligence 33 (01), 4456-4463, 2019 | 134 | 2019 |
| HGAT: Heterogeneous graph attention networks for semi-supervised short text classification T Yang, L Hu, C Shi, H Ji, X Li, L Nie ACM Transactions on Information Systems (TOIS) 39 (3), 1-29, 2021 | 127 | 2021 |
| A survey of knowledge enhanced pre-trained language models L Hu, Z Liu, Z Zhao, L Hou, L Nie, J Li IEEE Transactions on Knowledge and Data Engineering, 2023 | 97 | 2023 |
| Valley: Video assistant with large language model enhanced ability R Luo, Z Zhao, M Yang, J Dong, D Li, P Lu, T Wang, L Hu, M Qiu, Z Wei arXiv preprint arXiv:2306.07207, 2023 | 85 | 2023 |
| Deep learning for fake news detection: A comprehensive survey L Hu, S Wei, Z Zhao, B Wu AI open 3, 133-155, 2022 | 67 | 2022 |
| Rimom-im: A novel iterative framework for instance matching C Shao, LM Hu, JZ Li, ZC Wang, T Chung, JB Xia Journal of computer science and technology 31, 185-197, 2016 | 65 | 2016 |
| What happens next? Future subevent prediction using contextual hierarchical LSTM L Hu, J Li, L Nie, XL Li, C Shao Proceedings of the AAAI Conference on Artificial Intelligence 31 (1), 2017 | 63 | 2017 |
| Improving distantly-supervised relation extraction with joint label embedding L Hu, L Zhang, C Shi, L Nie, W Guan, C Yang Proceedings of the 2019 conference on empirical methods in natural language …, 2019 | 62 | 2019 |
| Virtually trying on new clothing with arbitrary poses N Zheng, X Song, Z Chen, L Hu, D Cao, L Nie Proceedings of the 27th ACM international conference on multimedia, 266-274, 2019 | 62 | 2019 |
| Adversarial label-flipping attack and defense for graph neural networks M Zhang, L Hu, C Shi, X Wang 2020 IEEE International Conference on Data Mining (ICDM), 791-800, 2020 | 55 | 2020 |
| Adaptive online event detection in news streams L Hu, B Zhang, L Hou, J Li Knowledge-Based Systems 138, 105-112, 2017 | 54 | 2017 |
| Graph neural entity disambiguation L Hu, J Ding, C Shi, C Shao, S Li Knowledge-Based Systems 195, 105620, 2020 | 29 | 2020 |
| RHINE: Relation structure-aware heterogeneous information network embedding C Shi, Y Lu, L Hu, Z Liu, H Ma IEEE Transactions on Knowledge and Data Engineering 34 (1), 433-447, 2020 | 25 | 2020 |
| Sequence-aware heterogeneous graph neural collaborative filtering C Li, L Hu, C Shi, G Song, Y Lu Proceedings of the 2021 SIAM International Conference on Data Mining (SDM …, 2021 | 23 | 2021 |
| Enhancing joint entity and relation extraction with language modeling and hierarchical attention R Chi, B Wu, L Hu, Y Zhang Web and Big Data: Third International Joint Conference, APWeb-WAIM 2019 …, 2019 | 22 | 2019 |
