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Cited by 207Topic ModelingInformation Retrieval and Search BehaviorMultimodal Machine Learning Applications

Graph Retrieval-Augmented Generation: A Survey

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Peng, Boci, Zhu, Yun, Liu, Yongchao, Bo, Xiaohe, Shi, Haizhou, Hong, Chuntao, Zhang, Yan, Tang, Siliang·2026·ACM Transactions on Information Systems
DOI 10.1145/3777378
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Abstract

Recently, Retrieval-Augmented Generation (RAG) has achieved remarkable success in addressing the challenges of Large Language Models (LLMs) without necessitating retraining. By referencing an external knowledge base, RAG refines LLM outputs, effectively mitigating issues such as “hallucination,” lack of domain-specific knowledge, and outdated information. However, the complex structure of relationships among different entities in databases presents challenges for RAG systems. In response, GraphRAG leverages structural information across entities to enable more precise and comprehensive retrieval, capturing relational knowledge and facilitating more accurate, context-aware responses. Given the novelty and potential of GraphRAG, a systematic review of current technologies is imperative. This article provides the first comprehensive overview of GraphRAG methodologies. We formalize the GraphRAG workflow, encompassing Graph-Based Indexing, Graph-Guided Retrieval, and Graph-Enhanced Generation. We then outline the core technologies and training methods at each stage. Additionally, we examine downstream tasks, application domains, evaluation methodologies, and industrial use cases of GraphRAG. Finally, we explore future research directions to inspire further inquiries and advance progress in the field. In order to track recent progress, we set up a repository at https://github.com/pengboci/GraphRAG-Survey .

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  • Modular RAG [28] and related surveys [24, 35, 86, 126] focus on conceptual overviews and lack implementation or evaluation support for GraphRAG.

    “However, Modular RAG [28] and related surveys [24, 35, 86, 126] focus on conceptual overviews and lack implementation or evaluation support for GraphRAG.”

    From LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration (2025) · cites this paper as [86]

    The Abstract and Conclusion describe this source as a survey that organizes GraphRAG methods, supporting its characterization as an overview. But §9, particularly §§9.3.1–9.3.2, covers benchmarks and evaluation metrics; the claim that it lacks evaluation support is therefore too broad. The cited passages do not establish whether it provides practical implementation support. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

  • The lack of a unified categorization for systematically analyzing GraphRAG techniques hinders effective research summarization and identification of promising candidates.

    “The lack of a unified categorization for systematically analyzing these techniques-despite prior efforts such as Modular RAG [28] and other surveys [24, 35, 86, 126] on LLMs with graphs-hinders effective research summarization and the identification of promising candidates for further study.”

    From LEGO-GraphRAG: Modularizing Graph-Based Retrieval-Augmented Generation for Design Space Exploration (2025) · cites this paper as [86]

    In Section 2.1, the paper says earlier RAG surveys touched on GraphRAG but focused mainly on textual data. It describes itself as the first systematic GraphRAG survey and categorizes techniques by workflow stage (Retrieved Triplets section). This supports the need for a more systematic categorization, but the copy does not state that its absence hindered research summarization or the identification of promising candidates. (LLM, conf=0.82)

    Experimental — accuracy not yet measured

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Cited by

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Authors
Peng, B., Zhu, Y., Liu, Y., Bo, X., Shi, H., Hong, C., Zhang, Y., & Tang, S.
Affiliations
  • Peking University (CN)
  • Zhejiang University (CN)
  • Ant Group (China) (CN)
  • Renmin University of China (CN)
  • Rutgers, The State University of New Jersey (US)
ORCID
Venue
ACM Transactions on Information Systems
Publisher
Association for Computing Machinery (ACM)
Published
2025-12-23
Volume
44
Issue
2
Pages
1-52
ISSN
  • 1046-8188 (ISSN-L)
  • 1558-2868
DOI
10.1145/3777378
OpenAlex
W4416379250
Cited by
206
References
34
Citations by year
2026: 155 · 2025: 47 · 2024: 4
FWCI
81.02
Type
journal article
Language
en
Open access
Locations
  • ACM Transactions on Information Systems (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University)
Funders
Ant Group
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Topic Modeling
  • Physical Sciences › Computer Science › Information Systems › Information Retrieval and Search Behavior
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Multimodal Machine Learning Applications
Keywords
GraphRAG, graph-based retrieval-augmented generation, large language models, graph-based indexing, relational knowledge, hallucination mitigation, knowledge base retrieval, entity relationships

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

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