About Me
👋 I'm Runlin Lei (雷润林), a third-year Ph.D. student at Renmin University of China. I'm a member of the ALGO Lab, advised by Prof. Zhewei Wei. My research interests primarily focus on graph machine learning, trustworthy graph neural networks and graph foundation models (graph large languge models).
🎓 Before embarking on my Ph.D. journey, I earned my Bachelor's degree from Shanghai University of Finance and Economics, where I was advised by Prof. Hongsong Yuan and Prof. Hua Liu.
📧 Feel free to reach out to me via email: runlin_lei@ruc.edu.cn.
Visitor Opportunities
🚀 I am actively seeking visitor opportunities to collaborate with leading researchers and institutions in the field of graph machine learning, large language models, and multi-agent. I am eager to engage in joint projects, exchange ideas, and contribute to innovative research initiatives.
🤝 If you are interested in hosting a visiting researcher or exploring potential collaborations, please feel free to contact me.
Publications
-
Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level
Runlin Lei, Yuwei Hu, Yuchen Ren, Zhewei Wei.
In NeurIPS 2024. [Download paper] [code] -
Dynamic and Textual Graph Generation Via Large-Scale LLM-based Agent Simulation
Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding.
Preprint [Download paper] [code] -
Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks
Jie Peng, Runlin Lei, Zhewei Wei.
In CIKM 2024. [Download paper] -
Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents
Yuwei Hu, Runlin Lei, Xinyi Huang, Zhewei Wei, Yongchao Liu.
Preprint [Download paper] -
Learning-based Property Estimation with Polynomials
Jiajun Li, Runlin Lei, Sibo Wang, Bolin Ding, Zhewei Wei.
In SIGMOD 2024. [Download paper] -
PolyGCL: Graph Contrastive Learning via Learnable Spectral Polynomial Filters
Jingyu Chen, Runlin Lei, Zhewei Wei.
In ICLR 2024. [Download paper] [code] -
Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks
Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei.
In NeurIPS 2022. [Download paper] [code]
Talks
- 2023-10: Gave a talk about Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks to the Complex Networks Analysis discussion group. Talk Link
- 2022-10: Gave a talk about Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks to AITIMES. Talk Link
Honors & Scholarships
- 2024, 2023: Academic Excellence Scholarship, Academic Scholarship
- 2022: Shanghai Excellent Graduate, School Excellent Graduate
- 2021: China Merchants Bank Scholarship, American Undergraduate Mathematical Modeling Competition M Award, National Mathematical Modeling Competition National Second Prize
- 2020: People's Scholarship First Prize
- 2019: National Scholarship
Services
I serve(d) as a program committee member / reviewer for:
- 2025: ICLR, AAAI, TPAMI
- 2024: NeurIPS, ICML, ICLR, KDD, ACML
- 2023: NeurIPS, ICML