| Unnoticeable Backdoor Attacks on Graph Neural Networks E Dai*, M Lin*, X Zhang, S Wang Proceedings of the ACM Web Conference 2023, 2023 | 33 | 2023 |
| Efficient, Direct, and Restricted Black-Box Graph Evasion Attacks to Any-Layer Graph Neural Networks via Influence Function B Wang*, M Lin*, T Zhou*, P Zhou, A Li, M Pang, H Li, Y Chen Proceedings of the 17th ACM International Conference on Web Search and Data …, 2024 | 27 | 2024 |
| Certifiably Robust Graph Contrastive Learning M Lin, T Xiao, E Dai, X Zhang, S Wang Advances in Neural Information Processing Systems 36, 2023 | 5 | 2023 |
| LLM and GNN are Complementary: Distilling LLM for Multimodal Graph Learning J Xu, Z Wu, M Lin, X Zhang, S Wang arXiv preprint arXiv:2406.01032, 2024 | 2 | 2024 |
| 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 | 1 | 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 | | 2024 |
| PreGIP: Watermarking the Pretraining of Graph Neural Networks for Deep Intellectual Property Protection E Dai*, M Lin*, S Wang arXiv preprint arXiv:2402.04435, 2024 | | 2024 |
Suhang WangPennsylvania State University在 psu.edu 的电子邮件经过验证
