| Backdoor attacks to graph neural networks Z Zhang, J Jia, B Wang, NZ Gong Proceedings of the 26th ACM Symposium on Access Control Models and …, 2021 | 150 | 2021 |
| Motif-based graph self-supervised learning for molecular property prediction Z Zhang, Q Liu, H Wang, C Lu, CK Lee Advances in Neural Information Processing Systems 34, 15870-15882, 2021 | 137 | 2021 |
| ProtGNN: Towards Self-Explaining Graph Neural Networks Z Zhang, Q Liu, H Wang, C Lu, C Lee AAAI 2022, 2021 | 64 | 2021 |
| FLDetector: Defending Federated Learning Against Model Poisoning Attacks via Detecting Malicious Clients Z Zhang, X Cao, J Jia, NZ Gong KDD 2022, arXiv: 2207.09209, 2022 | 60 | 2022 |
| GraphMI: Extracting Private Graph Data from Graph Neural Networks Z Zhang, Q Liu, Z Huang, H Wang, C Lu, C Liu, E Chen IJCAI'21, 2021 | 42 | 2021 |
| Hierarchical Graph Transformer with Adaptive Node Sampling Z Zhang, Q Liu, Q Hu, CK Lee NeurIPS 2022, 2022 | 22 | 2022 |
| Flcert: Provably secure federated learning against poisoning attacks X Cao, Z Zhang, J Jia, NZ Gong IEEE Transactions on Information Forensics and Security 17, 3691-3705, 2022 | 20 | 2022 |
| Model inversion attacks against graph neural networks Z Zhang, Q Liu, Z Huang, H Wang, CK Lee, E Chen IEEE Transactions on Knowledge and Data Engineering, 2022 | 18 | 2022 |
| Fedrecover: Recovering from poisoning attacks in federated learning using historical information X Cao, J Jia, Z Zhang, NZ Gong 2023 IEEE Symposium on Security and Privacy (SP), 1366-1383, 2023 | 15 | 2023 |
| Molecule Generation For Target Protein Binding with Structural Motifs Z ZHANG, Y Min, S Zheng, Q Liu The Eleventh International Conference on Learning Representations, 2023 | 12 | 2023 |
| Untargeted attack against federated recommendation systems via poisonous item embeddings and the defense Y Yu, Q Liu, L Wu, R Yu, SL Yu, Z Zhang Proceedings of the AAAI Conference on Artificial Intelligence 37 (4), 4854-4863, 2023 | 8 | 2023 |
| Learning Subpocket Prototypes for Generalizable Structure-based Drug Design Z Zhang, Q Liu ICML 2023, 2023 | 6 | 2023 |
| Graph Self-Supervised Learning for Optoelectronic Properties of Organic Semiconductors Z Zhang, Q Liu, S Zhang, CY Hsieh, L Shi, CK Lee ICML AI4Science Workshop, 2021 | 6 | 2021 |
| A Systematic Survey in Geometric Deep Learning for Structure-based Drug Design Z Zhang, J Yan, Q Liu, E Che arXiv preprint arXiv:2306.11768, 2023 | 3 | 2023 |
| Backdoor Defense via Deconfounded Representation Learning Z Zhang, Q Liu, Z Wang, Z Lu, Q Hu CVPR 2023, 2023 | 3 | 2023 |
| An equivariant generative framework for molecular graph-structure Co-design Z Zhang, Q Liu, CK Lee, CY Hsieh, E Chen Chemical Science 14 (31), 8380-8392, 2023 | 3 | 2023 |
| Implementation of fldetector Z Zhang, X Cao, J Jia, NZ Gong | 2 | 2022 |
| Differentiable Optimized Product Quantization and Beyond Z Lu, D Lian, J Zhang, Z Zhang, C Feng, H Wang, E Chen Proceedings of the ACM Web Conference 2023, 3353-3363, 2023 | 1 | 2023 |
| Sparse Attention-Based Neural Networks for Code Classification Z Xiang, Z Zhang, Q Liu arXiv preprint arXiv:2311.06575, 2023 | | 2023 |
| Mapping the intermolecular interaction universe through self-supervised learning on molecular crystals A Fang, Z ZHANG, M Zitnik NeurIPS 2023 AI for Science Workshop, 2023 | | 2023 |
Chee Kong LeeIBM QuantumVerified email at ibm.com
