About Me
I’m Runlin Lei (雷润林), a third-year Ph.D. student at Renmin University of China, advised by Prof. Zhewei Wei. My research interests primary focus on graph machine learning.
Before my Ph.D. journey, I obtained my Bachelor’s degree from Shanghai University of Finance and Economics, advised by Prof. Hongsong Yuan and Prof. Hua Liu.
Publications
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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
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2023-10: Give talk about Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks to Complex Networks Analysis discussion group. [Talk]
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2022-10: Give talk about Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks to AITIMES. [Talk]
Services
I serve(d) as a program committee member / reviewer for:
- 2025: ICLR, AAAI
- 2024: NeurIPS, ICML, ICLR, KDD, ACML
- 2023: NeurIPS, ICML