mlsanityData to 14 Oct 2026

Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey

arXiv 2504.10499 ↗First posted 8 Apr 2025cs.IR cs.CLcited by 18

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

Abstract (arXiv)

Large language models (LLMs) struggle with the factual error during inference due to the lack of sufficient training data and the most updated knowledge, leading to the hallucination problem. Retrieval-Augmented Generation (RAG) has gained attention as a promising solution to address the limitation of LLMs, by retrieving relevant information from external source to generate more accurate answers to the questions. Given the pervasive presence of structured knowledge in the external source, considerable strides in RAG have been made to employ the techniques related to graphs and achieve more complex reasoning based on the topological information between knowledge entities. However, there is currently neither unified review examining the diverse roles of graphs in RAG, nor a comprehensive resource to help researchers navigate and contribute to this evolving field. This survey offers a novel perspective on the functionality of graphs within RAG and their impact on enhancing performance across a wide range of graph-structured data. It provides a detailed breakdown of the roles that graphs play in RAG, covering database construction, algorithms, pipelines, and tasks. Finally, it identifies current challenges and outline future research directions, aiming to inspire further developments in this field. Our graph-centered analysis highlights the commonalities and differences in existing methods, setting the stage for future researchers in areas such as graph learning, database systems, and natural language processing.

Key facts. Cited by 18 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 171 of its 258 references that are in the map. First posted on arXiv 8 Apr 2025.

Builds on

171 of 258 references.

Closest ancestors · 9

Same direction, newest first

  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models21 Jan
  2. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)31 Dec
  3. 2024RAGraph: A General Retrieval-Augmented Graph Learning Framework31 Oct
  4. 2024Retrieval Augmented Generation for Dynamic Graph Modeling26 Aug
  5. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  6. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning30 May
  7. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  8. 2023ATLANTIC: Structure-Aware Retrieval-Augmented Language Model for Interdisciplinary Science21 Nov
  9. 2023FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction20 Oct

Other references · 162

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,372
  4. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 13,028
  5. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,361
  6. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  7. 2017Graph Attention Networkscited by 8,923
  8. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  9. 2017TriviaQA: A Large Scale Distantly Supervised Challenge Dataset for Reading Comprehensioncited by 2,955
  10. 2019PIQA: Reasoning about Physical Commonsense in Natural Languagecited by 2,883
  11. 2023Sparks of Artificial General Intelligence: Early experiments with GPT-4cited by 2,592
  12. 2023A Survey of Large Language Modelscited by 2,576
38 more
  1. 2018Can a Suit of Armor Conduct Electricity? A New Dataset for Open Book Question Answeringcited by 2,164
  2. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  3. 2018CommonsenseQA: A Question Answering Challenge Targeting Commonsense Knowledgecited by 1,822
  4. 2023Is Your Code Generated by ChatGPT Really Correct? Rigorous Evaluation of Large Language Models for Code Generationcited by 1,391
  5. 2020What Disease does this Patient Have? A Large-scale Open Domain Question Answering Dataset from Medical Examscited by 1,356
  6. 2019PubMedQA: A Dataset for Biomedical Research Question Answeringcited by 1,288
  7. 2022Measuring and Narrowing the Compositionality Gap in Language Modelscited by 1,075
  8. 2020Domain-Specific Language Model Pretraining for Biomedical Natural Language Processingcited by 1,009
  9. 2023Graph of Thoughts: Solving Elaborate Problems with Large Language Modelscited by 886
  10. 2018Hierarchical Graph Representation Learning with Differentiable Poolingcited by 787
  11. 2023Large Language Modelscited by 780
  12. 2017Convolutional 2D Knowledge Graph Embeddingscited by 730
  13. 2020TUDataset: A collection of benchmark datasets for learning with graphscited by 728
  14. 2023Siren's Song in the AI Ocean: A Survey on Hallucination in Large Language Modelscited by 698
  15. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 692
  16. 2023Unifying Large Language Models and Knowledge Graphs: A Roadmapcited by 668
  17. 2023A Comprehensive Overview of Large Language Modelscited by 648
  18. 2022Towards Reasoning in Large Language Models: A Surveycited by 596
  19. 2020Heterogeneous Graph Transformercited by 582
  20. 2020KILT: a Benchmark for Knowledge Intensive Language Taskscited by 573
  21. 2024Large Language Models: A Surveycited by 525
  22. 2023Active Retrieval Augmented Generationcited by 516
  23. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 506
  24. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  25. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
  26. 2018The Web as a Knowledge-base for Answering Complex Questionscited by 410
  27. 2021QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answeringcited by 388
  28. 2023A Survey of Hallucination in Large Foundation Modelscited by 368
  29. 2020Unbiased Scene Graph Generation from Biased Trainingcited by 356
  30. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 343
  31. 2023Large Language Models for Information Retrieval: A Surveycited by 331
  32. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
  33. 2024HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Modelscited by 281
  34. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 274
  35. 2024A Comprehensive Survey of Hallucination Mitigation Techniques in Large Language Modelscited by 269
  36. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  37. 2023Can Language Models Solve Graph Problems in Natural Language?cited by 252
  38. 2023Large Language Model Alignment: A Surveycited by 243

50 most cited of 162 All 162 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 10; 2026: 8.

18 papers.

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  1. 2025A Survey of Context Engineering for Large Language Modelscited by 126
  2. 2025LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoningcited by 38
  3. 2025Mindscape-Aware Retrieval Augmented Generation for Improved Long Context Understandingcited by 5
  4. 2026S-Path-RAG: Semantic-Aware Shortest-Path Retrieval Augmented Generation for Multi-Hop Knowledge Graph Question Answeringcited by 3
  5. 2025PRoH: Dynamic Planning and Reasoning over Knowledge Hypergraphs for Retrieval-Augmented Generationcited by 2
  6. 2025CID-GraphRAG: Enhancing Multi-Turn Dialogue Systems through Dual-Pathway Retrieval of Conversation Flow and Context Semanticscited by 1
  7. 2025Enhancing Agentic Textual Graph Retrieval with Synthetic Stepwise Supervisioncited by 1
  8. 2025Integrating Sequential and Relational Modeling for User Events: Datasets and Prediction Taskscited by 1
  9. 2025TagRAG: Tag-guided Hierarchical Knowledge Graph Retrieval-Augmented Generationcited by 1
  10. 2025Uncertainty-Aware Dynamic Knowledge Graphs for Reliable Question Answeringcited by 1
  11. 2026Toward Robust GraphRAG: Mitigating Retrieval Drift and Hallucination from Imperfect Knowledge Graphscited by 1
  12. 2025Matching Game Preferences Through Dialogical Large Language Models: A Perspectivecited by 0
6 more
  1. 2026BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphscited by 0
  2. 2026Security and Privacy in Retrieval-Augmented Generation: Architectures, Threats, Defenses, and Future Directions for Building Trustworthy Systemscited by 0
  3. 2026GRACE-RAG: Governed Retrieval Architecture for Canonical Evidence Synthesis, Enabling Lightweight Deployment in Closed-Domain Institutional Settingscited by 0
  4. 2026Memory-Orchestrated Semantic System (MOSS): An Auditable Agentic Memory Architecturecited by 0
  5. 2026Optimizing Hypergraph-Based RAG: Toward Better Fact Extraction and Chunk Retrievalcited by 0
  6. 2026Potential for Enhanced Learning in Machine Learning Classes by Using Wiki LLM Indexingcited by 0

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  2. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  3. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
  4. 2025Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
  5. 2023Retrieval-Augmented Generation for Large Language Models: A Survey
  6. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generation
  7. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  8. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation