| 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 | 276 | 2020 |
| 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 | 126 | 2021 |
| Self-supervised learning on graphs: Deep insights and new direction W Jin, T Derr, H Liu, Y Wang, S Wang, Z Liu, J Tang arXiv preprint arXiv:2006.10141, 2020 | 126 | 2020 |
| 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 | 96 | 2020 |
| 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 | 82 | 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 | 77 | 2022 |
| 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 | 66 | 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 | 64 | 2020 |
| Mitigating gender bias for neural dialogue generation with adversarial learning H Liu, W Wang, Y Wang, H Liu, Z Liu, J Tang arXiv preprint arXiv:2009.13028, 2020 | 50 | 2020 |
| 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 | 25 | 2020 |
| 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 | 21 | 2022 |
| 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 | 18 | 2021 |
| Graph pooling with representativeness J Li, Y Ma, Y Wang, C Aggarwal, CD Wang, J Tang 2020 IEEE International Conference on Data Mining (ICDM), 302-311, 2020 | 14 | 2020 |
| 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 | 13 | 2018 |
| GT-WGS: an efficient and economic tool for large-scale WGS analyses based on the AWS cloud service Y Wang, G Li, M Ma, F He, Z Song, W Zhang, C Wu BMC genomics 19, 89-98, 2018 | 12 | 2018 |
| 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 | 9 | 2022 |
| Localized Graph Collaborative Filtering Y Wang, C Li, M Li, W Jin, Y Liu, H Sun, X Xie, J Tang arXiv preprint arXiv:2108.04475, 2021 | 7 | 2021 |
| 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 | 4 | 2022 |
| 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 ArXiv Preprint arXiv:2112.07191, 2021 | 4 | 2021 |
| Are Graph Neural Networks Really Helpful for Knowledge Graph Completion? J Li, H Shomer, J Ding, Y Wang, Y Ma, N Shah, J Tang, D Yin arXiv preprint arXiv:2205.10652, 2022 | 3 | 2022 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu
