Abstract: Large language models(LLMs),with their strong semantic understanding and knowledge reasoning capabilities,offer a promising pathway for overcoming the generalization bottlenecks of traditional graph learning in zero-shot reasoning and cross-domain transfer.However,existing studies lack a systematic analysis of the methodological framework and evaluation benchmarks for LLM-empowered graph learning.This paper reviews the three-stage development trajectory of this field and constructs a two-dimensional taxonomy based on collaboration level and graph structure type.The collaboration dimension covers three paradigms:LLM-only graph modeling,layered LLM-GNN collaboration,and deep integration paradigms,with a detailed analysis of the core mechanisms,applicability,and limitations of each.The graph structure dimension focuses on the adaptation mechanisms of LLMs for complex topologies,including text-attributed graphs,multimodal graphs,heterogeneous graphs,and dynamic graphs.Building upon this foundation,progress in typical applications such as recommender systems and anomaly detection is summarized.Furthermore,it systematically reviews existing benchmarks from the perspectives of LLM interaction modes and task types,highlighting the capability boundaries of different methods.Results indicate that collaborative schemes are generally more robust,while current evaluation systems remain insufficient in areas such as dynamic benchmark construction and quantitative assessment of structural perception decay.Finally,the paper summarizes current challenges regarding large-scale spatiotemporal graphs,cross-scenario generalization,efficiency optimization,and trustworthiness and security,and outlines future research directions.

Key words: Graph learning, Large language models, Graph neural networks, Cross-modal alignment