A survey of large language models for data challenges in graphs

Mengran Li, Pengyu Zhang, Wenbin Xing, Yijia Zheng, Klim Zaporojets, Junzhou Chen, Ronghui Zhang, Yong Zhang, Siyuan Gong, Jia Hu, Xiaolei Ma, Zhiyuan Liu, Paul Groth, Marcel Worring

Abstract: Highlights•Survey four fundamental data-centric challenges in graph learning.•Review solutions to incompleteness, imbalance, heterogeneity, and dynamics in graphs.•Summarize recent LLM-based approaches tackling graph data challenges.•Propose future directions on efficiency, robustness, and fairness in LLM-graph learning.•Provide a comprehensive resource at the intersection of LLMs and graph learning.

External IDs:dblp:journals/eswa/LiZXZZCZZGHMLGW26