| Traffic flow prediction via spatial temporal graph neural network X Wang, Y Ma, Y Wang, W Jin, X Wang, J Tang, C Jia, J Yu Proceedings of the web conference 2020, 1082-1092, 2020 | 606 | 2020 |
| Recommender systems in the era of large language models (llms) W Fan IEEE Transactions on Knowledge and Data Engineering, 1-20, 2024 | 317 | 2024 |
| Node similarity preserving graph convolutional networks W Jin, T Derr, Y Wang, Y Ma, Z Liu, J Tang Proceedings of the 14th ACM international conference on web search and data …, 2021 | 285 | 2021 |
| Trustworthy ai: A computational perspective H Liu, Y Wang, W Fan, X Liu, Y Li, S Jain, Y Liu, A Jain, J Tang ACM Transactions on Intelligent Systems and Technology 14 (1), 1-59, 2022 | 277 | 2022 |
| Self-supervised learning on graphs: Deep insights and new direction W Jin, T Derr, H Liu, Y Wang, S Wang, Z Liu, J Tang The Web Conference (WWW 2021) Workshop: Self-Supervised Learning for the Web, 2021 | 244 | 2021 |
| Adversarial attacks and defenses on graphs W Jin, Y Li, H Xu, Y Wang, S Ji, C Aggarwal, J Tang ACM SIGKDD Explorations Newsletter 22 (2), 19-34, 2021 | 198 | 2021 |
| Investigating and mitigating degree-related biases in graph convoltuional networks X Tang, H Yao, Y Sun, Y Wang, J Tang, C Aggarwal, P Mitra, S Wang Proceedings of the 29th ACM International Conference on Information …, 2020 | 161 | 2020 |
| Elastic graph neural networks X Liu, W Jin, Y Ma, Y Li, H Liu, Y Wang, M Yan, J Tang International Conference on Machine Learning, 6837-6849, 2021 | 146 | 2021 |
| Adversarial attacks and defenses on graphs: A review and empirical study W Jin, Y Li, H Xu, Y Wang, J Tang arXiv preprint arXiv:2003.00653 10 (3447556.3447566), 2020 | 126 | 2020 |
| Gophormer: Ego-graph transformer for node classification J Zhao, C Li, Q Wen, Y Wang, Y Liu, H Sun, X Xie, Y Ye arXiv preprint arXiv:2110.13094, 2021 | 86 | 2021 |
| Graph neural networks for multimodal single-cell data integration H Wen, J Ding, W Jin, Y Wang, Y Xie, J Tang Proceedings of the 28th ACM SIGKDD conference on knowledge discovery and …, 2022 | 70 | 2022 |
| Mitigating gender bias for neural dialogue generation with adversarial learning H Liu, W Wang, Y Wang, H Liu, Z Liu, J Tang Proceedings of the 2020 Conference on Empirical Methods in Natural Language …, 2020 | 70 | 2020 |
| House: Knowledge graph embedding with householder parameterization R Li, J Zhao, C Li, D He, Y Wang, Y Liu, H Sun, S Wang, W Deng, Y Shen, ... International conference on machine learning, 13209-13224, 2022 | 60 | 2022 |
| Linrec: Linear attention mechanism for long-term sequential recommender systems L Liu, L Cai, C Zhang, X Zhao, J Gao, W Wang, Y Lv, W Fan, Y Wang, ... Proceedings of the 46th International ACM SIGIR Conference on Research and …, 2023 | 51 | 2023 |
| A comprehensive survey on trustworthy recommender systems W Fan, X Zhao, X Chen, J Su, J Gao, L Wang, Q Liu, Y Wang, H Xu, ... arXiv preprint arXiv:2209.10117, 2022 | 51 | 2022 |
| Deep graph learning: Foundations, advances and applications Y Rong, T Xu, J Huang, W Huang, H Cheng, Y Ma, Y Wang, T Derr, L Wu, ... Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 45 | 2020 |
| An Adaptive Graph Pre-training Framework for Localized Collaborative Filtering Y Wang, C Li, Z Liu, M Li, J Tang, X Xie, L Chen, PS Yu ACM Transactions on Information Systems, 2021 | 28 | 2021 |
| Deep embedding for determining the number of clusters Y Wang, Z Shi, X Guo, X Liu, E Zhu, J Yin Proceedings of the AAAI Conference on Artificial Intelligence 32 (1), 2018 | 27 | 2018 |
| STRec: Sparse transformer for sequential recommendations C Li, Y Wang, Q Liu, X Zhao, W Wang, Y Wang, L Zou, W Fan, Q Li Proceedings of the 17th ACM conference on recommender systems, 101-111, 2023 | 25 | 2023 |
| Test-time training for graph neural networks Y Wang, C Li, W Jin, R Li, J Zhao, J Tang, X Xie arXiv preprint arXiv:2210.08813, 2022 | 21 | 2022 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityPreverjeni e-poštni naslov na msu.edu