Publications

Preprints

Enyan Dai, Minhua Lin, Suhang Wang. “PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection.” [paper]

Minhua Lin, Enyan Dai, Junjie Xu, Suhang Wang. “Stealing Training Graphs from Graph Neural Networks.” In Proceedings of the 31th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2025) [paper]

Enyan Dai, Suhang Wang. “Towards Prototype-Based Self-Explainable Graph Neural Network.” Accepted by TKDD [paper]
Enyan Dai, Tianxiang Zhao, Huaisheng Zhu, Junjie Xu, Zhimeng Guo, Hui Liu, Jiliang Tang, and Suhang Wang. “A Comprehensive Survey on Trustworthy Graph Neural Networks: Privacy, Robustness, Fairness, and Explainability.” Accepted by Machine Intelligence Research [paper].
Xuanhao Fan, Enyan Dai “Effective Clean-Label Backdoor Attacks on Graph Neural Networks.” In Proceedings of the 33th ACM International Conference on Information and Knowledge Management (CIKM 2024).
Junjie Xu, Enyan Dai, Dongsheng Luo, Xiang Zhang, Suhang Wang. “Shape-aware Graph Spectral Learning.” In Proceedings of the 33th ACM International Conference on Information and Knowledge Management (CIKM 2024) [paper].
Zhiwei Zhang, Minhua Lin, Enyan Dai, Suhang Wang “Rethinking Graph Backdoor Attacks: A Distribution-Preserving Perspective.” In Proceedings of the 30th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2024)
Shuotong Bai, Huaxiao Liu, Enyan Dai, Liu Lei. ``AIPL: Automated Linking GitHub Issue and PR.” Accepted by IEEE Transactions on Software Engineering.

Enyan Dai et al. “A Unified Framework of Graph Information Bottleneck for Robustness and Membership Privacy” In Proceedings of the 29th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2023)
Enyan Dai*, Minhua Lin*, Xiang Zhang, and Suhang Wang. “Unnoticeable Backdoor Attacks on Graph Neural Networks” In Proceedings of The Web Conference (WWW 2023) [paper, code]
Minhua Lin, Teng Xiao, Enyan Dai, Xiang Zhang, Suhang Wang. “Certifiably Robust Graph Contrastive Learning” Accepted by Thirty-seventh Conference on Neural Information Processing Systems (NeurIPS 2023)
Huaisheng Zhu, Enyan Dai, Hui Liu, Suhang Wang. “Learning Fair Models without Sensitive Attributes: A Generative Approach” Accepted by Neurocomputing

Enyan Dai, and Jie Chen. “Graph-Augmented Normalizing Flows for Anomaly Detection of Multiple Time Series. “ Spotlight in International Conference on Learning Representations (ICLR 2022) [paper, code]
Enyan Dai, Jin Wei, Hui Liu, and Suhang Wang. “Towards Robust Graph Neural Networks for Noisy Graphs with Sparse Labels.” Oral In Proceedings of 15th ACM International Conference on Web Search and Data Mining (WSDM 2022) [paper, code]
Enyan Dai, Shijie Zhou, Zhimeng Guo, and Suhang Wang. “Label-Wise Message Passing Graph Neural Network on Heterophilic Graphs.” In Proceedings of 1st Learning On Graphs Conference (LOG 2022) [paper]
Tianxiang Zhao, Enyan Dai, Kai Shu, Suhang Wang. “You Can Still Achieve Fairness Without Sensitive Attributes: Exploring Biases in Non-Sensitive Features” In Proceedings of 15th ACM International Conference on Web Search and Data Mining (WSDM 2022) [paper]
Junjie Xu, Enyan Dai, Xiang Zhang, Suhang Wang. “HP-GMN:Graph Memory Networks for Heterophilous Graphs” Accepted by The IEEE International Conference on Data Mining (ICDM 2022)
Enyan Dai, Suhang Wang. “Learning Fair Graph Neural Networks with Limited andPrivate Sensitive Attribute Information” [paper] Accepted by TKDE

Enyan Dai, Kai Shu, Yiwei Sun, Suhang Wang. “Labeled Data Generation with Inexact Supervision” In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). [paper]
Enyan Dai, Aggarwal Charu, Wang, Suhang. “NRGNN: Learning a Label Noise-Resistant Graph Neural Network on Sparsely and Noisily Labeled Graphs” In Proceedings of the 27th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2021). [paper, code]
Enyan Dai, and Suhang Wang. “Say No to the Discrimination: Learning Fair Graph Neural Networks with Limited Sensitive Attribute Information” In Proceedings of the 14th ACM International Conference on Web Search and Data Mining (WSDM 2021). [paper, code]
Enyan Dai, and Suhang Wang. “ Towards Self-Explainable Graph Neural Network.” In Proceedings of 30th ACM International Conference on Information and Knowledge Management (CIKM 2021) [paper, code]
Yuqing Hu, Xiaoyuan Cheng, Suhang Wang, Jianli Chen, Tianxiang Zhao, Enyan Dai. “Times Series Forecasting for Urban Building Energy Consumption Based on Graph Convolutional Network” (Applied Energy 2022) [paper]
Xiaoyuan Cheng, Yuqing Hu, Jianxiang Huang, Suhang Wang, Tianxiang Zhao, and Enyan Dai. ``Urban Building Energy Modeling: A Time-Series Building Energy Consumption Use Simulation Prediction Tool Based on Graph Neural Network.” In Computing in Civil Engineering

Enyan Dai, Yiwei Sun, and Suhang Wang. “Ginger Cannot Cure Cancer: Battling Fake Health News with a Comprehensive Data Repository.” In Proceedings of the International AAAI Conference on Web and Social Media (ICWSM 2020). [pdf, code, data]

Chen, Chacha, Chieh-Yang Huang, Yaqi Hou, Yang Shi, Enyan Dai, and Jiaqi Wang. “TEST_POSITIVE at W-NUT 2020 Shared Task-3: Cross-task modeling.” In Proceedings of the Sixth Workshop on Noisy User-generated Text (W-NUT 2020). 2020.
Chen, Shuaijun, Zhen Han, Enyan Dai, et al. “Unsupervised image super-resolution with an indirect supervised path.” In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops. 2020.