mlsanityData to 14 Oct 2026

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

arXiv 2501.13958 ↗First posted 21 Jan 2025cs.CL cs.AI cs.IRcited by 88

On the map: Retrieval-augmented generation → Knowledge-graph and structured-knowledge RAG

Abstract (arXiv)

Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise. Retrieval-Augmented generation (RAG) has emerged as a promising solution to customize LLMs for professional fields by seamlessly integrating external knowledge bases, enabling real-time access to domain-specific expertise during inference. Despite its potential, traditional RAG systems, based on flat text retrieval, face three critical challenges: (i) complex query understanding in professional contexts, (ii) difficulties in knowledge integration across distributed sources, and (iii) system efficiency bottlenecks at scale. This survey presents a systematic analysis of Graph-based Retrieval-Augmented Generation (GraphRAG), a new paradigm that revolutionizes domain-specific LLM applications. GraphRAG addresses traditional RAG limitations through three key innovations: (i) graph-structured knowledge representation that explicitly captures entity relationships and domain hierarchies, (ii) efficient graph-based retrieval techniques that enable context-preserving knowledge retrieval with multihop reasoning ability, and (iii) structure-aware knowledge integration algorithms that leverage retrieved knowledge for accurate and logical coherent generation of LLMs. In this survey, we systematically analyze the technical foundations of GraphRAG and examine current implementations across various professional domains, identifying key technical challenges and promising research directions. All the related resources of GraphRAG, including research papers, open-source data, and projects, are collected for the community in https://github.com/DEEP-PolyU/Awesome-GraphRAG.

Key facts. Cited by 88 papers in the map (data through 14 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 186 of its 252 references that are in the map. First posted on arXiv 21 Jan 2025.

Builds on

186 of 252 references.

Closest ancestors · 19

Same direction, newest first

  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)31 Dec
  2. 2024OG-RAG: Ontology-Grounded Retrieval-Augmented Generation For Large Language Models12 Dec
  3. 2024FastRAG: Retrieval Augmented Generation for Semi-structured Data21 Nov
  4. 2024LEGO-GraphRAG: Modularizing Graph-based Retrieval-Augmented Generation for Design Space Exploration6 Nov
  5. 2024Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation6 Nov
  6. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation28 Oct
  7. 2024Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphs14 Oct
  8. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation8 Oct
  9. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation10 Sep
  10. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  11. 2024Knowledge graph enhanced retrieval-augmented generation for failure mode and effects analysis26 Jun
  12. 2024Mix-of-Granularity: Optimize the Chunking Granularity for Retrieval-Augmented Generation1 Jun
7 more
  1. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning30 May
  2. 2024Empowering Large Language Models to Set up a Knowledge Retrieval Indexer via Self-Learning27 May
  3. 2024GRAG: Graph Retrieval-Augmented Generation26 May
  4. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering26 Apr
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  6. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering12 Feb
  7. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 167

  1. 2020Language Models are Few-Shot Learnerscited by 32,419
  2. 2023GPT-4 Technical Reportcited by 18,396
  3. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,380
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  6. 2017Inductive Representation Learning on Large Graphscited by 6,320
  7. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,326
  8. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  9. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  10. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  11. 2022Large Language Models Encode Clinical Knowledgecited by 1,830
  12. 2021Improving language models by retrieving from trillions of tokenscited by 1,293
38 more
  1. 2022Atlas: Few-shot Learning with Retrieval Augmented Language Modelscited by 952
  2. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 938
  3. 2021Towards a Unified View of Parameter-Efficient Transfer Learningcited by 872
  4. 2018Link Prediction Based on Graph Neural Networkscited by 832
  5. 2023REPLUG: Retrieval-Augmented Black-Box Language Modelscited by 792
  6. 2022Automatic Chain of Thought Prompting in Large Language Modelscited by 688
  7. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
  8. 2023Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Modelscited by 560
  9. 2023Active Retrieval Augmented Generationcited by 516
  10. 2020UnifiedQA: Crossing Format Boundaries With a Single QA Systemcited by 476
  11. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 410
  12. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  13. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
  14. 2023ChatDoctor: A Medical Chat Model Fine-Tuned on a Large Language Model Meta-AI (LLaMA) Using Medical Domain Knowledgecited by 325
  15. 2023LLM-Adapters: An Adapter Family for Parameter-Efficient Fine-Tuning of Large Language Modelscited by 324
  16. 2023Transformers as Statisticians: Provable In-Context Learning with In-Context Algorithm Selectioncited by 298
  17. 2023SayPlan: Grounding Large Language Models using 3D Scene Graphs for Scalable Robot Task Planningcited by 294
  18. 2024HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Modelscited by 281
  19. 2023RECOMP: Improving Retrieval-Augmented LMs with Compression and Selective Augmentationcited by 256
  20. 2024Does Fine-Tuning LLMs on New Knowledge Encourage Hallucinations?cited by 232
  21. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 226
  22. 2023Language is All a Graph Needscited by 220
  23. 2023LoftQ: LoRA-Fine-Tuning-Aware Quantization for Large Language Modelscited by 196
  24. 2020Generation-Augmented Retrieval for Open-domain Question Answeringcited by 186
  25. 2023Chain-of-Note: Enhancing Robustness in Retrieval-Augmented Language Modelscited by 184
  26. 2024LLaGA: Large Language and Graph Assistantcited by 181
  27. 2020Beyond I.I.D.: Three Levels of Generalization for Question Answering on Knowledge Basescited by 171
  28. 2017Variational Reasoning for Question Answering with Knowledge Graphcited by 170
  29. 2024Do Large Language Models Latently Perform Multi-Hop Reasoning?cited by 169
  30. 2020Answering Complex Open-Domain Questions with Multi-Hop Dense Retrievalcited by 162
  31. 2024The Impact of Reasoning Step Length on Large Language Modelscited by 153
  32. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 152
  33. 2023What Makes Good Examples for Visual In-Context Learning?cited by 150
  34. 2024Does Prompt Formatting Have Any Impact on LLM Performance?cited by 141
  35. 2023Domain Specialization as the Key to Make Large Language Models Disruptive: A Comprehensive Surveycited by 135
  36. 2022Internet-augmented language models through few-shot prompting for open-domain question answeringcited by 131
  37. 2024Visual In-Context Learning for Large Vision-Language Modelscited by 128
  38. 2015Large-scale Simple Question Answering with Memory Networkscited by 127

50 most cited of 167 All 167 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 47; 2026: 41.

88 papers.

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  1. 2025MemOS: A Memory OS for AI Systemcited by 138
  2. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corporacited by 49
  3. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generationcited by 48
  4. 2025Retrieval-Augmented Generation with Hierarchical Knowledgecited by 47
  5. 2026Code as Agent Harnesscited by 47
  6. 2025Reinforced Lifelong Editing for Language Modelscited by 34
  7. 2025Generative to Agentic AI: Survey, Conceptualization, and Challengescited by 33
  8. 2025From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agentscited by 29
  9. 2025SGMem: Sentence Graph Memory for Long-Term Conversational Agentscited by 29
  10. 2025GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generationcited by 24
  11. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  12. 2025Youtu-GraphRAG: Vertically Unified Agents for Graph Retrieval-Augmented Complex Reasoningcited by 22
38 more
  1. 2025RAG-Anything: All-in-One RAG Frameworkcited by 20
  2. 2025GraphMaster: Automated Graph Synthesis via LLM Agents in Data-Limited Environmentscited by 19
  3. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Surveycited by 18
  4. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 15
  5. 2025Diagnosing and Addressing Pitfalls in KG-RAG Datasets: Toward More Reliable Benchmarkingcited by 12
  6. 2025ParamMute: Suppressing Knowledge-Critical FFNs for Faithful Retrieval-Augmented Generationcited by 11
  7. 2025USB: A Comprehensive and Unified Safety Evaluation Benchmark for Multimodal Large Language Modelscited by 10
  8. 2025Knapsack Optimization-based Schema Linking for LLM-based Text-to-SQL Generationcited by 9
  9. 2025Reliable Reasoning Path: Distilling Effective Guidance for LLM Reasoning with Knowledge Graphscited by 9
  10. 2025Scaling Beyond Context: A Survey of Multimodal Retrieval-Augmented Generation for Document Understandingcited by 9
  11. 2025Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompletenesscited by 7
  12. 2025GRAIL:Learning to Interact with Large Knowledge Graphs for Retrieval Augmented Reasoningcited by 7
  13. 2025AutoGraph-R1: End-to-End Reinforcement Learning for Knowledge Graph Constructioncited by 7
  14. 2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMscited by 7
  15. 2025How to Mitigate Information Loss in Knowledge Graphs for GraphRAG: Leveraging Triple Context Restoration and Query-Driven Feedbackcited by 6
  16. 2026KOCO-BENCH: Can Large Language Models Leverage Domain Knowledge in Software Development?cited by 5
  17. 2026LegalGraphRAG: Multi-Agent Graph Retrieval-Augmented Generation for Reliable Legal Reasoningcited by 5
  18. 2025Ella: Embodied Social Agents with Lifelong Memorycited by 4
  19. 2025Explainable Knowledge Graph Retrieval-Augmented Generation (KG-RAG) with KG-SMILEcited by 4
  20. 2025How to Auto-optimize Prompts for Domain Tasks? Adaptive Prompting and Reasoning through Evolutionary Domain Knowledge Adaptationcited by 4
  21. 2026Reliable Graph-RAG for Codebases: AST-Derived Graphs vs LLM-Extracted Knowledge Graphscited by 4
  22. 2026SD-RAG: A Prompt-Injection-Resilient Framework for Selective Disclosure in Retrieval-Augmented Generationcited by 4
  23. 2025Agentic RAG with Knowledge Graphs for Complex Multi-Hop Reasoning in Real-World Applicationscited by 3
  24. 2025LoSemB: Logic-Guided Semantic Bridging for Inductive Tool Retrievalcited by 3
  25. 2026S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answeringcited by 3
  26. 2026CogniVerse: Revolutionizing Multi-Modal Retrieval-Augmented Generation with Cognitive Reflection and Geometric Reasoningcited by 3
  27. 2026Graph Engineering in the Era of LLM Agents: From Individual Intelligence to System Intelligencecited by 3
  28. 2025How Significant Are the Real Performance Gains? An Unbiased Evaluation Framework for GraphRAGcited by 2
  29. 2025Inference Scaled GraphRAG: Improving Multi Hop Question Answering on Knowledge Graphscited by 2
  30. 2025Benchmarking Vector, Graph and Hybrid Retrieval Augmented Generation (RAG) Pipelines for Open Radio Access Networks (ORAN)cited by 2
  31. 2025Comparing RAG and GraphRAG for Page-Level Retrieval Question Answering on a Math Textbookcited by 2
  32. 2025HyperbolicRAG: Enhancing Retrieval-Augmented Generation with Hyperbolic Representationscited by 2
  33. 2026Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAGcited by 2
  34. 2026SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memorycited by 2
  35. 2026In-Context Optimization for Retrieval-Augmented Generation: A Gradient-Descent Perspectivecited by 2
  36. 2025Towards Temporal Knowledge-Base Creation for Fine-Grained Opinion Analysis with Language Modelscited by 1
  37. 2025Multilevel Analysis of Cryptocurrency News using RAG Approach with Fine-Tuned Mistral Large Language Modelcited by 1
  38. 2025Query-Centric Graph Retrieval Augmented Generationcited by 1

50 most cited of 88 All 88 (JSON)

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  1. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  2. 2025GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models
  3. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation
  4. 2025FRAG: A Flexible Modular Framework for Retrieval-Augmented Generation based on Knowledge Graphs
  5. 2025DSRAG: A Domain-Specific Retrieval Framework Based on Document-derived Multimodal Knowledge Graph
  6. 2025MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
  7. 2024Retrieval-Augmented Generation for Natural Language Processing: A Survey
  8. 2025A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Models