| 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 | 601 | 2020 |
| Gppt: Graph pre-training and prompt tuning to generalize graph neural networks M Sun, K Zhou, X He, Y Wang, X Wang Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 172 | 2022 |
| A survey on fairness in large language models Y Li, M Du, R Song, X Wang, Y Wang arXiv preprint arXiv:2308.10149, 2023 | 86 | 2023 |
| Orthogonal graph neural networks K Guo, K Zhou, X Hu, Y Li, Y Chang, X Wang Proceedings of the AAAI Conference on Artificial Intelligence 36 (4), 3996-4004, 2022 | 53 | 2022 |
| Contrastive graph convolutional networks with adaptive augmentation for text classification Y Yang, R Miao, Y Wang, X Wang Information Processing & Management 59 (4), 102946, 2022 | 47 | 2022 |
| Modeling status theory in trust prediction Y Wang, X Wang, J Tang, W Zuo, G Cai Proceedings of the AAAI conference on artificial intelligence 29 (1), 2015 | 35 | 2015 |
| Exploring graph capsual network for graph classification Y Wang, H Wang, H Jin, X Huang, X Wang Information Sciences 581, 932-950, 2021 | 27 | 2021 |
| TAERT: triple-attentional explainable recommendation with temporal convolutional network S Guo, Y Wang, H Yuan, Z Huang, J Chen, X Wang Information Sciences 567, 185-200, 2021 | 26 | 2021 |
| Exploring the Combination of Dempster‐Shafer Theory and Neural Network for Predicting Trust and Distrust X Wang, Y Wang, H Sun Computational intelligence and neuroscience 2016 (1), 5403105, 2016 | 26 | 2016 |
| Attention-guide walk model in heterogeneous information network for multi-style recommendation explanation X Wang, Y Wang, Y Ling Proceedings of the AAAI Conference on Artificial Intelligence 34 (04), 6275-6282, 2020 | 24 | 2020 |
| Negative samples selecting strategy for graph contrastive learning R Miao, Y Yang, Y Ma, X Juan, H Xue, J Tang, Y Wang, X Wang Information Sciences 613, 667-681, 2022 | 22 | 2022 |
| INS-GNN: Improving graph imbalance learning with self-supervision X Juan, F Zhou, W Wang, W Jin, J Tang, X Wang Information Sciences 637, 118935, 2023 | 19 | 2023 |
| Exploring social context for topic identification in short and noisy texts X Wang, Y Wang, W Zuo, G Cai Proceedings of the AAAI Conference on Artificial Intelligence 29 (1), 2015 | 18 | 2015 |
| PokeMQA: Programmable knowledge editing for Multi-hop Question Answering H Gu, K Zhou, X Han, N Liu, R Wang, X Wang Proceedings of the 62nd Annual Meeting of the Association for Computational …, 2024 | 17 | 2024 |
| A survey of graph prompting methods: techniques, applications, and challenges X Wu, K Zhou, M Sun, X Wang, N Liu arXiv preprint arXiv:2303.07275, 2023 | 17 | 2023 |
| Cap: Co-adversarial perturbation on weights and features for improving generalization of graph neural networks H Xue, K Zhou, T Chen, K Guo, X Hu, Y Chang, X Wang arXiv preprint arXiv:2110.14855, 2021 | 14 | 2021 |
| Adagcl: Adaptive subgraph contrastive learning to generalize large-scale graph training Y Wang, K Zhou, R Miao, N Liu, X Wang Proceedings of the 31st ACM international conference on information …, 2022 | 13 | 2022 |
| Prompt tuning pushes farther, contrastive learning pulls closer: A two-stage approach to mitigate social biases Y Li, M Du, X Wang, Y Wang arXiv preprint arXiv:2307.01595, 2023 | 12 | 2023 |
| Hierarchical recurrent neural networks for graph generation S Xianduo, W Xin, S Yuyuan, Z Xianglin, W Ying Information Sciences 589, 250-264, 2022 | 11 | 2022 |
| Unifying unsupervised graph-level anomaly detection and out-of-distribution detection: A benchmark Y Wang, Y Liu, X Shen, C Li, K Ding, R Miao, Y Wang, S Pan, X Wang ICLR 2025, 2025 | 10 | 2025 |
Jiliang TangUniversity Foundation Professor of Computer Science and Engineering, Michigan State UniversityPotvrđena adresa e-pošte na msu.edu