Hello, I am Zihan Luo, a third-year CS Ph.D. candidate at Huazhong University of Science and Technology (HUST), where I am fortunate to be supervised by Professor Hong Huang and Senior Researcher Jianxun Lian. Before that, I received my Bachelor degree in Electronic Engineering from HUST in 2020, and was fortunate to be advised by Professor Rui Yin from 2019-2020.
My research interests mainly include large language models and graph data mining, especially on topics like fairness and bias. I have published several papers at the top international AI conferences and journals with .
🔥 News
- 2025.10: 🎉🎉 GraphInstruct is accepted by Frontiers of Computer Science (CCF-T1).
- 2024.11: 🎉🎉 Our paper on GNN hybrid fairness is accepted by KDD 2025 (CCF-A), accept rate 19%.
- 2024.09: 🎉🎉 Our paper on graph fairness attacks is accepted by NeurIPS 2024 (CCF-A), accept rate 25.8%.
📝 Publications

GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability
Zihan Luo, Xiran Song, Hong Huang, Jianxun Lian, Chenhao Zhang, Jinqi Jiang, Xing Xie, Hai Jin
- A comprehensive benchmark GraphInstruct on enhancing and improving the graph understanding and reasoning capability.
- Code.

Towards Controllable Hybrid Fairness in Graph Neural Networks
Zihan Luo, Hong Huang, Jianxun Lian, Xiran Song, Hai Jin
- A multi-teacher knowledge distillation framework for addressing multiple kinds of fairness simultaneously.

Are Your Models Still Fair? Fairness Attacks on Graph Neural Networks via Node Injections
Zihan Luo, Hong Huang, Yongkang Zhou, Jiping Zhang, Nuo Chen, Hai Jin

Cross-links Matter for Link Prediction: Rethinking the Debiased GNN from a Data Perspective
Zihan Luo, Hong Huang, Jianxun Lian, Xiran Song, Xing Xie, Hai Jin
- A framework on investigating and mitigating the bias on Cross-links from a data augmentation perspective.
- Code.

xGCN: An Extreme Graph Convolutional Network for Large-scale Social Link Prediction
Xiran Song, Jianxun Lian, Hong Huang, Zihan Luo, Wei Zhou, Xue Lin, Mingqi Wu, Chaozhuo Li, Xing Xie, Hai Jin

Ada-GNN: Adapting to Local Patterns for Improving Graph Neural Networks
Zihan Luo, Jianxun Lian, Hong Huang, Xing Xie, Hai Jin
- A novel GNN training framework for capturing graph local patterns while preserving global information.
- Code.

Rui Yin*, Zihan Luo*, Pei Zhuang, Chee Keong Kwoh, Zhuoyi Lin
- Utilizing human prior knowledge for virulence prediction with posterior regularization.
- Code.

Rui Yin, Zihan Luo, Chee Keong Kwoh
- A simple framework for utilizing deep neural networks for coronavirus lethality prediction.
- Code.

Rui Yin, Zihan Luo, Pei Zhuang, Zhuoyi Lin, Chee Keong Kwoh
- A framework for utilizing deep neural networks for virulence prediction of influenza A virus.
- Code.
🎖 Honors and Awards
- National Scholarship, 2025.09.
- Academic Scholarship, 2020.09, 2021.09, 2022.09, 2023.09.
- Huawei Scholarship, 2022.06, 2025.05.
- Tencent Scholarship, 2022.03.
- Finalist Award (Top 1%) at The Mathematical Contest in Modeling 2019.
📖 Educations
- 2022.09 - 2026.06 (Expected), School of Computer Science and Technology, Huazhong University of Science and Technology, Ph.D. Candidate.
- 2020.09 - 2022.06, School of Computer Science and Technology, Huazhong University of Science and Technology, Master.
- 2016.09 - 2020.06, School of Electronic Information and Communication, Huazhong University of Science and Technology, Bachelor.
💻 Internships
- 2024.09 - 2025.03, Zhipu AI, Beijing, China.
- Internship on LLM Alignment for Machine Learning Engineering.
- Mentor: Zhenyu Hou
💬 Service
Program Committee Members
- Reviewer for COLM 2025
- Reviewer for NeurIPS 2025
Transactions Reviewer