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A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

Paper · 2025 · Qinggang Zhang, Shengyuan Chen, Yuanchen Bei et al.

Summary

General-purpose LLMs often fall short in specialized fields. Their domain knowledge is shallow, they struggle with long chains of rule-bound reasoning, and they misread terms whose meaning depends on context. Fine-tuning on domain data can introduce hallucinations and catastrophic forgetting. Classic RAG avoids retraining, but because it retrieves flat text chunks it has its own problems. It handles complex queries poorly, cannot link knowledge spread across many sources, is constrained by the LLM's context window, and scales badly. This survey, by authors from The Hong Kong Polytechnic University and Jilin University, reviews Graph-based RAG (GraphRAG) as a remedy. GraphRAG stores knowledge as graphs of entities and relations, retrieves it with structure-aware and multi-hop methods, and feeds the result into generation. The authors organize the field around knowledge organization, retrieval and integration. They also review applications, open-source projects and benchmark datasets, and they outline open problems in knowledge quality, efficiency, generalization and security. The survey offers no new experiments. Its value is a structured map of the field and a curated resource list.

Key points

  • The survey sorts GraphRAG into three families:
    • knowledge-based, where the graph itself carries the knowledge;
    • index-based, where the graph serves as an index pointing back to raw text;
    • hybrid, which combines the two.
  • It names four weaknesses of flat-text RAG:
    • difficulty understanding complex queries, e.g. missing intermediate concepts B and C when linking A to D;
    • poor integration of knowledge from distributed sources;
    • LLM context windows of roughly 2K–32K tokens;
    • efficiency and scalability bottlenecks.
  • The core chapters cover:
    • structured knowledge organization through knowledge carrier graphs and index graphs;
    • graph retrieval techniques;
    • methods for integrating retrieved knowledge into LLMs.
  • The benchmarks surveyed include WebQSP, CWQ, MetaQA, GrailQA, HotpotQA, KQAPro, FACTKG and UltraDomain. The representative systems covered include Microsoft-style GraphRAG, LightRAG, ToG, RoG, G-Retriever, GNN-RAG and MedGraphRAG.
  • Papers, datasets and projects are collected in the GitHub repository DEEP-PolyU/Awesome-GraphRAG.

Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.

 Institutions

🎓Hong Kong Polytechnic UniversityHong Kong · China34 works · 4,476 citations🎓Jilin UniversityChangchun · China8 works · 5,204 citations

📚 Shelves 6The field of science this work belongs to (OpenAlex fields).Computer Science · 9,085 worksArtificial Intelligence · 8,888 worksArts and Humanities · 9,296 worksLanguage and Linguistics · 9,206 worksComputer Vision and Pattern Recognition · 1,282 worksInformation Systems · 523 works

🧭 Topics 2Research subjects and languages the work is about.Language models & pretraining · 1,344 worksInformation retrieval & search · 146 works

Cite

Cite as Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, Zheng Yuan, Huachi Zhou, Zijin Hong, Hao Chen, Yilin Xiao, Chuang Zhou, Junnan Dong, Yi Chang and Xiao Huang. 2025. A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models. arXiv:2501.13958.

The paper itself, not this page. BibTeX from the library's record of the work. DOI 10.48550/arxiv.2501.13958 · arXiv 2501.13958

@misc{zhang2025survey,
  title = {{A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models}},
  author = {Qinggang Zhang, Shengyuan Chen, Yuanchen Bei, Zheng Yuan, Huachi Zhou, Zijin Hong, Hao Chen, Yilin Xiao, Chuang Zhou, Junnan Dong, Yi Chang and Xiao Huang},
  year = {2025},
  doi = {10.48550/arxiv.2501.13958},
  eprint = {2501.13958},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.13958}
}

References

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