About Me
👋 I'm Runlin Lei (雷润林), a fourth-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.
Publications
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GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning
Jie Peng, Jiarui Ji, Runlin Lei, Zhewei Wei, Yongchao Liu, Chuntao Hong.
Preprint [Download paper] [code] -
Future Link Prediction Without Memory or Aggregation
Lu Yi, Runlin Lei, Fengran Mo, Yanping Zheng, Zhewei Wei, Yuhang Ye.
In NeurIPS 2025. [Download paper] -
Exploring the Potential of Large Language Models as Predictors in Dynamic Text-Attributed Graphs
Runlin Lei, Jiarui Ji, Haipeng Ding, Lu Yi, Zhewei Wei, Yongchao Liu, Chuntao Hong.
Preprint [Download paper] -
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] -
LLM-Based Multi-Agent Systems are Scalable Graph Generative Models
Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding.
In ACL (Findings) 2024. [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