| Graph Structure Learning for Robust Graph Neural Networks W Jin, Y Ma, X Liu, X Tang, S Wang, J Tang KDD 2020, 2020 | 677 | 2020 |
| 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 | 556 | 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 | 299* | 2021 |
| Node Similarity Preserving Graph Convolutional Networks W Jin, T Derr, Y Wang, Y Ma, Z Liu, J Tang International Conference on Web Search and Data Mining (WSDM), 2021 | 246 | 2021 |
| Exploring the potential of large language models (llms) in learning on graphs Z Chen, H Mao, H Li, W Jin, H Wen, X Wei, S Wang, D Yin, W Fan, H Liu, ... SIGKDD Explorations, 2023 | 239 | 2023 |
| 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 | 237 | 2021 |
| From Stars to Subgraphs: Uplifting Any GNN with Local Structure Awareness L Zhao, W Jin, L Akoglu, N Shah International Conference on Learning Representations (ICLR), 2022 | 167 | 2022 |
| Graph Condensation for Graph Neural Networks W Jin, L Zhao, S Zhang, Y Liu, J Tang, N Shah International Conference on Learning Representations (ICLR), 2022 | 150 | 2022 |
| Deeprobust: A pytorch library for adversarial attacks and defenses Y Li*, W Jin*, H Xu, J Tang arXiv preprint arXiv:2005.06149, 2020 | 145 | 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 (ICML), 6837-6849, 2021 | 137 | 2021 |
| Graph trend filtering networks for recommendation W Fan, X Liu, W Jin, X Zhao, J Tang, Q Li Proceedings of the 45th international ACM SIGIR conference on research and …, 2022 | 108 | 2022 |
| Graph Data Augmentation for Graph Machine Learning: A Survey T Zhao, W Jin, Y Wang, G Liu, Y Liu, S Gunnemann, N Shah, M Jiang IEEE Data Engineering Bulletin (DEBULL), 2023 | 107 | 2023 |
| Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels E Dai, W Jin, H Liu, S Wang WSDM 2022, 2022 | 95 | 2022 |
| Condensing Graphs via One-Step Gradient Matching W Jin, X Tang, H Jiang, Z Li, D Zhang, J Tang, B Yin KDD 2022, 2022 | 94 | 2022 |
| Automated self-supervised learning for graphs W Jin, X Liu, X Zhao, Y Ma, N Shah, J Tang International Conference on Learning Representations (ICLR), 2022 | 85 | 2022 |
| Graph neural networks with adaptive residual X Liu, J Ding, W Jin, H Xu, Y Ma, Z Liu, J Tang NeurIPS 2021, 2021 | 73 | 2021 |
| Empowering graph representation learning with test-time graph transformation W Jin, T Zhao, J Ding, Y Liu, J Tang, N Shah ICLR 2023, 2022 | 64 | 2022 |
| Graph Neural Networks for Multimodal Single-Cell Data Integration H Wen*, J Ding*, W Jin*, Y Wang*, Y Xie, J Tang KDD 2022, 2022 | 61 | 2022 |
| Label-free node classification on graphs with large language models (llms) Z Chen, H Mao, H Wen, H Han, W Jin, H Zhang, H Liu, J Tang arXiv preprint arXiv:2310.04668, 2023 | 49 | 2023 |
| Demystifying structural disparity in graph neural networks: Can one size fit all? H Mao, Z Chen, W Jin, H Han, Y Ma, T Zhao, N Shah, J Tang Advances in neural information processing systems 36, 2024 | 44 | 2024 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State University在 msu.edu 的电子邮件经过验证