Enyan Dai
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Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information
E Dai, S Wang
WSDM 2021, 2020
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
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
arXiv preprint arXiv:2204.08570, 2022
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
Towards robust graph neural networks for noisy graphs with sparse labels
E Dai, W Jin, H Liu, S Wang
WSDM 2022, 181-191, 2022
Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series
E Dai, J Chen
ICLR 2022, 2022
Towards self-explainable graph neural network
E Dai, S Wang
CIKM 2021, 302-311, 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
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
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
Unnoticeable backdoor attacks on graph neural networks
E Dai, M Lin, X Zhang, S Wang
WWW 2023, 2263-2273, 2023
Learning fair graph neural networks with limited and private sensitive attribute information
E Dai, S Wang
IEEE Transactions on Knowledge and Data Engineering, 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
HP-GMN: Graph Memory Networks for Heterophilous Graphs
J Xu, E Dai, X Zhang, S Wang
ICDM 2022, 2022
Learning fair models without sensitive attributes: A generative approach
H Zhu, E Dai, H Liu, S Wang
Neurocomputing 561, 126841, 2023
Towards prototype-based self-explainable graph neural network
E Dai, S Wang
arXiv preprint arXiv:2210.01974, 2022
Certifiably Robust Graph Contrastive Learning
M Lin, T Xiao, E Dai, X Zhang, S Wang
NeurIPS 2023, 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
Labeled Data Generation with Inexact Supervision
E Dai, K Shu, Y Sun, S Wang
KDD 2021, 218-226, 2021
Learning Graph Filters for Spectral GNNs via Newton Interpolation
J Xu, E Dai, D Luo, X Zhang, S Wang
arXiv preprint arXiv:2310.10064, 2023
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