| Graph neural networks for social recommendation W Fan, Y Ma, Q Li, Y He, E Zhao, J Tang, D Yin The world wide web conference, 417-426, 2019 | 2044 | 2019 |
| Adversarial attacks and defenses in images, graphs and text: A review H Xu, Y Ma, HC Liu, D Deb, H Liu, JL Tang, AK Jain International journal of automation and computing 17, 151-178, 2020 | 755 | 2020 |
| Graph structure learning for robust graph neural networks W Jin, Y Ma, X Liu, X Tang, S Wang, J Tang Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 636 | 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 | 536 | 2020 |
| Graph convolutional networks with eigenpooling Y Ma, S Wang, CC Aggarwal, J Tang Proceedings of the 25th ACM SIGKDD international conference on knowledge …, 2019 | 387 | 2019 |
| Signed graph convolutional networks T Derr, Y Ma, J Tang 2018 IEEE International Conference on Data Mining (ICDM), 929-934, 2018 | 326 | 2018 |
| Is homophily a necessity for graph neural networks? Y Ma, X Liu, N Shah, J Tang arXiv preprint arXiv:2106.06134, 2021 | 275 | 2021 |
| Streaming Graph Neural Networks Y Ma, Z Guo, Z Ren, E Zhao, J Tang, D Yin arXiv preprint arXiv:1810.10627, 2018 | 260 | 2018 |
| 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 | 239 | 2021 |
| Deep learning on graphs Y Ma, J Tang Cambridge University Press, 2021 | 180 | 2021 |
| A unified view on graph neural networks as graph signal denoising Y Ma, X Liu, T Zhao, Y Liu, J Tang, N Shah Proceedings of the 30th ACM International Conference on Information …, 2021 | 172 | 2021 |
| A graph neural network framework for social recommendations W Fan, Y Ma, Q Li, J Wang, G Cai, J Tang, D Yin IEEE Transactions on Knowledge and Data Engineering 34 (5), 2033-2047, 2020 | 150 | 2020 |
| R-transformer: Recurrent neural network enhanced transformer Z Wang, Y Ma, Z Liu, J Tang arXiv preprint arXiv:1907.05572, 2019 | 140 | 2019 |
| Multi-dimensional graph convolutional networks Y Ma, S Wang, CC Aggarwal, D Yin, J Tang Proceedings of the 2019 siam international conference on data mining, 657-665, 2019 | 129 | 2019 |
| 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 | 119 | 2021 |
| Deep social collaborative filtering W Fan, Y Ma, D Yin, J Wang, J Tang, Q Li Proceedings of the 13th ACM conference on recommender systems, 305-313, 2019 | 114 | 2019 |
| Automated self-supervised learning for graphs W Jin, X Liu, X Zhao, Y Ma, N Shah, J Tang arXiv preprint arXiv:2106.05470, 2021 | 83 | 2021 |
| Attacking graph convolutional networks via rewiring Y Ma, S Wang, T Derr, L Wu, J Tang arXiv preprint arXiv:1906.03750, 2019 | 83 | 2019 |
| Deep adversarial social recommendation W Fan, T Derr, Y Ma, J Wang, J Tang, Q Li arXiv preprint arXiv:1905.13160, 2019 | 83 | 2019 |
| Multi-dimensional network embedding with hierarchical structure Y Ma, Z Ren, Z Jiang, J Tang, D Yin Proceedings of the eleventh ACM international conference on web search and …, 2018 | 83 | 2018 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityVerified email at msu.edu
