Hello, I am Zihan Luo, a 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 graph data mining, especially on topics like Large Language Models (LLM), fairness and bias. I have published several papers at the top international AI conferences and journals with 
🔥 News
- 2024.11: 🎉🎉 Our paper on GNN hybrid fairness is accepted by KDD 2025, accept rate 19%. See you in Toronto!
- 2024.09: 🎉🎉 Our paper on GNN fairness attacks NIFA is accepted by NeurIPS 2024, accept rate 25.8%. See you in Vancouver!
- 2024.03: 🎉🎉 Our paper on empowering LLMs with graph reasoning capability GraphInstruct is released on Arxiv.
- 2023.11: 🎉🎉 I am invited to give a spotlight sharing for our NeurIPS 2023 paper at MLA 2023 conference.
- 2023.09: 🎉🎉 Our paper on cross-links debias is accepted by NeurIPS 2023, accept rate 26.1%.
📝 Publications
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
- A node-injection-based fairness attack framework on GNNs.
- Code.
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.
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
- A novel GNN paradigm for large-scale social recommendation.
- Code.
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
- Academic Scholarship, 2020.09, 2021.09, 2022.09, 2023.09.
- Huawei Scholarship, 2022.06.
- 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 - Now, Zhipu AI, Beijing, China.
- Internship on LLM Alignment
- Mentor: Zhenyu Hou
💬 Invited Talks
- 2023.11, Paper Spotlight Sharing at MLA 2023 Conference in Nanjing, China.