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

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

Yuwei Hu, Runlin Lei, Xinyi Huang, Zhewei Wei, Yongchao Liu.
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

Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding.
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: Give talk about Evennet: Ignoring odd-hop neighbors improves robustness of graph neural networks to Complex Networks Analysis discussion group. [Talk]

• 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