Runlin Lei

According to our database, Runlin Lei authored at least 12 papers between 2022 and 2025.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2025

A survey of estimating number of distinct values.

Jiajun Li, Runlin Lei, Zhewei Wei

Frontiers Comput. Sci., September, 2025

GDGB: A Benchmark for Generative Dynamic Text-Attributed Graph Learning.

Jie Peng, Jiarui Ji, Runlin Lei, Zhewei Wei, Yongchao Liu, Chuntao Hong

CoRR, July, 2025

Future Link Prediction Without Memory or Aggregation.

Lu Yi, Runlin Lei, Fengran Mo, Yanping Zheng, Zhewei Wei, Yuhang Ye

CoRR, May, 2025

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

CoRR, March, 2025

LLM-Based Multi-Agent Systems are Scalable Graph Generative Models.

Jiarui Ji, Runlin Lei, Jialing Bi, Zhewei Wei, Xu Chen, Yankai Lin, Xuchen Pan, Yaliang Li, Bolin Ding

Proceedings of the Findings of the Association for Computational Linguistics, 2025

2024

Learning-based Property Estimation with Polynomials.

Jiajun Li, Runlin Lei, Sibo Wang, Zhewei Wei, Bolin Ding

Proc. ACM Manag. Data, 2024

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

CoRR, 2024

Scalable and Accurate Graph Reasoning with LLM-based Multi-Agents.

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

CoRR, 2024

Intruding with Words: Towards Understanding Graph Injection Attacks at the Text Level.

Runlin Lei, Yuwei Hu, Yuchen Ren, Zhewei Wei

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

PolyGCL: GRAPH CONTRASTIVE LEARNING via Learnable Spectral Polynomial Filters.

Jingyu Chen, Runlin Lei, Zhewei Wei

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Beyond Over-smoothing: Uncovering the Trainability Challenges in Deep Graph Neural Networks.

Jie Peng, Runlin Lei, Zhewei Wei

Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, 2024

2022

EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks.

Runlin Lei, Zhen Wang, Yaliang Li, Bolin Ding, Zhewei Wei

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022