| Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information E Dai, S Wang WSDM 2021, 2020 | 295 | 2020 |
| A comprehensive survey on trustworthy graph neural networks: Privacy, robustness, fairness, and explainability E Dai, T Zhao, H Zhu, J Xu, Z Guo, H Liu, J Tang, S Wang Machine Intelligence Research 21 (6), 1011-1061, 2024 | 165 | 2024 |
| Ginger cannot cure cancer: Battling fake health news with a comprehensive data repository E Dai, Y Sun, S Wang Proceedings of the International AAAI Conference on Web and Social Media 14 …, 2020 | 143 | 2020 |
| Nrgnn: Learning a label noise resistant graph neural network on sparsely and noisily labeled graphs E Dai, C Aggarwal, S Wang KDD 2021, 227-236, 2021 | 118 | 2021 |
| Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series E Dai, J Chen ICLR 2022, 2022 | 109 | 2022 |
| Towards robust graph neural networks for noisy graphs with sparse labels E Dai, W Jin, H Liu, S Wang WSDM 2022, 181-191, 2022 | 108 | 2022 |
| Towards self-explainable graph neural network E Dai, S Wang CIKM 2021, 302-311, 2021 | 96 | 2021 |
| Towards Fair Classifiers Without Sensitive Attributes: Exploring Biases in Related Features T Zhao, E Dai, K Shu, S Wang WSDM 2022, 1433-1442, 2022 | 91* | 2022 |
| Times series forecasting for urban building energy consumption based on graph convolutional network Y Hu, X Cheng, S Wang, J Chen, T Zhao, E Dai Applied Energy 307, 118231, 2022 | 85 | 2022 |
| Unsupervised image super-resolution with an indirect supervised path S Chen, Z Han, E Dai, X Jia, Z Liu, L Xing, X Zou, C Xu, J Liu, Q Tian Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2020 | 62 | 2020 |
| Unnoticeable backdoor attacks on graph neural networks E Dai, M Lin, X Zhang, S Wang WWW 2023, 2263-2273, 2023 | 53 | 2023 |
| Learning fair graph neural networks with limited and private sensitive attribute information E Dai, S Wang IEEE Transactions on Knowledge and Data Engineering 35 (7), 7103-7117, 2022 | 35 | 2022 |
| Label-wise graph convolutional network for heterophilic graphs E Dai, S Zhou, Z Guo, S Wang Learning on Graphs Conference, 26: 1-26: 21, 2022 | 32* | 2022 |
| HP-GMN: Graph Memory Networks for Heterophilous Graphs J Xu, E Dai, X Zhang, S Wang ICDM 2022, 2022 | 25 | 2022 |
| Towards prototype-based self-explainable graph neural network E Dai, S Wang ACM Transactions on Knowledge Discovery from Data, 2022 | 17 | 2022 |
| Certifiably Robust Graph Contrastive Learning M Lin, T Xiao, E Dai, X Zhang, S Wang NeurIPS 2023, 2023 | 16 | 2023 |
| A unified framework of graph information bottleneck for robustness and membership privacy E Dai, L Cui, Z Wang, X Tang, Y Wang, M Cheng, B Yin, S Wang KDD 2023, 2023 | 15 | 2023 |
| Learning fair models without sensitive attributes: A generative approach H Zhu, E Dai, H Liu, S Wang Neurocomputing 561, 126841, 2023 | 13 | 2023 |
| Rethinking graph backdoor attacks: A distribution-preserving perspective Z Zhang, M Lin, E Dai, S Wang Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and …, 2024 | 12 | 2024 |
| Robustness-Inspired Defense Against Backdoor Attacks on Graph Neural Networks Z Zhang, M Lin, J Xu, Z Wu, E Dai, S Wang arXiv preprint arXiv:2406.09836, 2024 | 7 | 2024 |
Suhang WangPennsylvania State UniversityCorreu electrònic verificat a psu.edu
