| 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 | 161* | 2022 |
| Imgagn: Imbalanced network embedding via generative adversarial graph networks L Qu, H Zhu, R Zheng, Y Shi, H Yin Proceedings of the 27th ACM SIGKDD conference on knowledge discovery & data …, 2021 | 121 | 2021 |
| Continuous-time link prediction via temporal dependent graph neural network L Qu, H Zhu, Q Duan, Y Shi Proceedings of the web conference 2020, 3026-3032, 2020 | 79 | 2020 |
| Simple and asymmetric graph contrastive learning without augmentations T Xiao, H Zhu, Z Chen, S Wang Advances in neural information processing systems 36, 16129-16152, 2023 | 35 | 2023 |
| On the safety of open-sourced large language models: Does alignment really prevent them from being misused? H Zhang, Z Guo, H Zhu, B Cao, L Lin, J Jia, J Chen, D Wu arXiv preprint arXiv:2310.01581, 2023 | 20 | 2023 |
| Learning fair models without sensitive attributes: A generative approach H Zhu, E Dai, H Liu, S Wang Neurocomputing 561, 126841, 2023 | 12 | 2023 |
| Fairness-aware message passing for graph neural networks H Zhu, G Fu, Z Guo, Z Zhang, T Xiao, S Wang arXiv preprint arXiv:2306.11132, 2023 | 10 | 2023 |
| 3m-diffusion: Latent multi-modal diffusion for text-guided generation of molecular graphs H Zhu, T Xiao, VG Honavar arXiv e-prints, arXiv: 2403.07179, 2024 | 9 | 2024 |
| Efficient Contrastive Learning for Fast and Accurate Inference on Graphs T Xiao, H Zhu, Z Zhang, Z Guo, CC Aggarwal, S Wang, VG Honavar Forty-first International Conference on Machine Learning, 2024 | 8 | 2024 |
| Jailbreak open-sourced large language models via enforced decoding H Zhang, Z Guo, H Zhu, B Cao, L Lin, J Jia, J Chen, D Wu Proceedings of the 62nd Annual Meeting of the Association for Computational …, 2024 | 7 | 2024 |
| Cal-dpo: Calibrated direct preference optimization for language model alignment T Xiao, Y Yuan, H Zhu, M Li, V Honavar Advances in Neural Information Processing Systems 37, 114289-114320, 2025 | 5 | 2025 |
| How to leverage demonstration data in alignment for large language model? a self-imitation learning perspective T Xiao, M Li, Y Yuan, H Zhu, C Cui, VG Honavar arXiv preprint arXiv:2410.10093, 2024 | 5 | 2024 |
| Molbind: Multimodal alignment of language, molecules, and proteins T Xiao, C Cui, H Zhu, VG Honavar arXiv preprint arXiv:2403.08167, 2024 | 5 | 2024 |
| A comprehensive survey on trustworthy graph neural networks: Privacy E Dai, T Zhao, H Zhu, J Xu, Z Guo, H Liu, J Tang, S Wang Robustness, Fairness, and Explainability. arXiv 2204, 2022 | 5 | 2022 |
| 3M-Diffusion: Latent Multi-Modal Diffusion for Language-Guided Molecular Structure Generation H Zhu, T Xiao, VG Honavar First Conference on Language Modeling, 2024 | 2 | 2024 |
| Self-explainable graph neural networks for link prediction H Zhu, D Luo, X Tang, J Xu, H Liu, S Wang arXiv preprint arXiv:2305.12578, 2023 | 2 | 2023 |
| BSOGCN: brain storm optimization graph convolutional networks based heterogeneous information networks embedding L Qu, H Zhu, Y Shi 2020 IEEE Congress on Evolutionary Computation (CEC), 1-7, 2020 | 2 | 2020 |
| You Need to Look Globally: Discovering Representative Topology Structures to Enhance Graph Neural Network H Zhu, X Tang, TX Zhao, S Wang Pacific-Asia Conference on Knowledge Discovery and Data Mining, 40-52, 2023 | 1 | 2023 |
| GeomCLIP: Contrastive Geometry-Text Pre-training for Molecules T Xiao, C Cui, H Zhu, VG Honavar 2024 IEEE International Conference on Bioinformatics and Biomedicine (BIBM …, 2024 | | 2024 |
| Enhancing Diffusion Posterior Sampling for Inverse Problems by Integrating Crafted Measurements S Zhou, H Zhu, R Sharma, R Zhang, K Ji, C Chen arXiv preprint arXiv:2411.09850, 2024 | | 2024 |
