mlsanityData to 14 Oct 2026

In-depth Analysis of Graph-based RAG in a Unified Framework

arXiv 2503.04338 ↗First posted 6 Mar 2025cs.IR cs.CL cs.DBcited by 46

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

Abstract (arXiv)

Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptability, interpretability, and trustworthiness. A number of graph-based RAG methods have been proposed in the literature. However, these methods have not been systematically and comprehensively compared under the same experimental settings. In this paper, we first summarize a unified framework to incorporate all graph-based RAG methods from a high-level perspective. We then extensively compare representative graph-based RAG methods over a range of questing-answering (QA) datasets -- from specific questions to abstract questions -- and examine the effectiveness of all methods, providing a thorough analysis of graph-based RAG approaches. As a byproduct of our experimental analysis, we are also able to identify new variants of the graph-based RAG methods over specific QA and abstract QA tasks respectively, by combining existing techniques, which outperform the state-of-the-art methods. Finally, based on these findings, we offer promising research opportunities. We believe that a deeper understanding of the behavior of existing methods can provide new valuable insights for future research.

Key facts. Cited by 46 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 60 of its 101 references that are in the map. First posted on arXiv 6 Mar 2025.

Builds on

60 of 101 references.

Closest ancestors · 6

Same direction, newest first

  1. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights17 Feb
  2. 2025KET-RAG: A Cost-Efficient Multi-Granular Indexing Framework for Graph-RAG13 Feb
  3. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)31 Dec
  4. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  5. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  6. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 54

  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. 2023GPT-4 Technical Reportcited by 18,396
  4. 2024The Llama 3 Herd of Modelscited by 14,715
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  6. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  7. 2024Qwen2.5 Technical Reportcited by 4,942
  8. 2023Toolformer: Language Models Can Teach Themselves to Use Toolscited by 4,182
  9. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  10. 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,839
  11. 2020ColBERT: Efficient and Effective Passage Search via Contextualized Late Interaction over BERTcited by 1,178
  12. 2022When Not to Trust Language Models: Investigating Effectiveness of Parametric and Non-Parametric Memoriescited by 1,128
38 more
  1. 2021MuSiQue: Multihop Questions via Single-hop Question Compositioncited by 916
  2. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
  3. 2023DSPy: Compiling Declarative Language Model Calls into Self-Improving Pipelinescited by 832
  4. 2022A Survey on In-context Learningcited by 721
  5. 2023Enabling Large Language Models to Generate Text with Citationscited by 595
  6. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  7. 2024Adaptive-RAG: Learning to Adapt Retrieval-Augmented Large Language Models through Question Complexitycited by 451
  8. 2023Is ChatGPT a Good NLG Evaluator? A Preliminary Studycited by 422
  9. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 385
  10. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  11. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 343
  12. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  13. 2023Think-on-Graph: Deep and Responsible Reasoning of Large Language Model on Knowledge Graphcited by 238
  14. 2023Large Language Models in Finance: A Surveycited by 226
  15. 2021QuALITY: Question Answering with Long Input Texts, Yes!cited by 208
  16. 2024Large Language Models for Education: A Survey and Outlookcited by 188
  17. 2022Can Foundation Models Wrangle Your Data?cited by 175
  18. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 152
  19. 2024Evaluation of Retrieval-Augmented Generation: A Surveycited by 146
  20. 2022Improving the Domain Adaptation of Retrieval Augmented Generation (RAG) Models for Open Domain Question Answeringcited by 127
  21. 2023Knowledge-Driven CoT: Exploring Faithful Reasoning in LLMs for Knowledge-intensive Question Answeringcited by 97
  22. 2024A Survey of Large Language Models for Financial Applications: Progress, Prospects and Challengescited by 90
  23. 2024Retrieval-Augmented Generation for Natural Language Processing: A Surveycited by 71
  24. 2023GSLB: The Graph Structure Learning Benchmarkcited by 50
  25. 2024DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literaturecited by 50
  26. 2024Large Language Models for Medicine: A Surveycited by 38
  27. 2024A Declarative System for Optimizing AI Workloadscited by 37
  28. 2025Cache-Craft: Managing Chunk-Caches for Efficient Retrieval-Augmented Generationcited by 36
  29. 2023GPTuner: A Manual-Reading Database Tuning System via GPT-Guided Bayesian Optimizationcited by 29
  30. 2023D-Bot: Database Diagnosis System using Large Language Modelscited by 28
  31. 2024AutoDCWorkflow: LLM-based Data Cleaning Workflow Auto-Generation and Benchmarkcited by 24
  32. 2023Chameleon: a Heterogeneous and Disaggregated Accelerator System for Retrieval-Augmented Language Modelscited by 23
  33. 2024The Design of an LLM-powered Unstructured Analytics Systemcited by 20
  34. 2024LLM-R2: A Large Language Model Enhanced Rule-based Rewrite System for Boosting Query Efficiencycited by 16
  35. 2024A Survey on Large Language Models from General Purpose to Medical Applications: Datasets, Methodologies, and Evaluationscited by 16
  36. 2024R-Bot: An LLM-based Query Rewrite Systemcited by 16
  37. 2024Auto-Formula: Recommend Formulas in Spreadsheets using Contrastive Learning for Table Representationscited by 12
  38. 2024Automatic Database Configuration Debugging using Retrieval-Augmented Language Modelscited by 12

50 most cited of 54 All 54 (JSON)

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 17; 2026: 29.

46 papers.

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  1. 2025A Survey of Context Engineering for Large Language Modelscited by 126
  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. 2025LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoningcited by 38
  5. 2025GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generationcited by 24
  6. 2025You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structurescited by 23
  7. 2026A Comprehensive Survey on Agent Skills: Taxonomy, Techniques, and Applicationscited by 18
  8. 2025BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documentscited by 16
  9. 2025EraRAG: Efficient and Incremental Retrieval Augmented Generation for Growing Corporacited by 15
  10. 2026Memory in the LLM Era: Modular Architectures and Strategies in a Unified Frameworkcited by 12
  11. 2025DyG-RAG: Dynamic Graph Retrieval-Augmented Generation with Event-Centric Reasoningcited by 8
  12. 2026SkillRAE: Agent Skill-Based Context Compilation for Retrieval-Augmented Executioncited by 8
34 more
  1. 2025AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMscited by 7
  2. 2025Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinkingcited by 6
  3. 2026Relink: Constructing Query-Driven Evidence Graph On-the-Fly for GraphRAGcited by 6
  4. 2025Towards Transparent RAG: Fostering Evidence Traceability in LLM Generation via Reinforcement Learningcited by 4
  5. 2025Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrievalcited by 4
  6. 2026H-Mem: A Novel Memory Mechanism for Evolving and Retrieving Agent Memory via a Hybrid Structurecited by 4
  7. 2026Query-Efficient Agentic Graph Extraction Attacks on GraphRAG Systemscited by 3
  8. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphscited by 3
  9. 2026Retrieving Minimal and Sufficient Reasoning Subgraphs with Graph Foundation Models for Path-aware GraphRAGcited by 2
  10. 2026Beyond Explicit Edges: Robust Reasoning over Noisy and Sparse Knowledge Graphscited by 2
  11. 2026EHRAG: Bridging Semantic Gaps in Lightweight GraphRAG via Hybrid Hypergraph Construction and Retrievalcited by 2
  12. 2025Test-Time-Matching: Decouple Personality, Memory, and Linguistic Style in LLM-based Role-Playing Language Agentcited by 1
  13. 2026HyperRAG: Reasoning N-ary Facts over Hypergraphs for Retrieval Augmented Generationcited by 1
  14. 2026Agentic Graph Retrieval-Augmented Generation for Auditable Commercial Registry Analysiscited by 1
  15. 2026MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generationcited by 1
  16. 2025Less is More: Denoising Knowledge Graphs For Retrieval Augmented Generationcited by 0
  17. 2025Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methodscited by 0
  18. 2025All-in-one Graph-based Indexing for Hybrid Search on GPUscited by 0
  19. 2026DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generationcited by 0
  20. 2026BubbleRAG: Evidence-Driven Retrieval-Augmented Generation for Black-Box Knowledge Graphscited by 0
  21. 2026Graphs RAG at Scale: Beyond Retrieval-Augmented Generation With Labeled Property Graphs and Resource Description Framework for Complex and Unknown Search Spacescited by 0
  22. 2026LLM+Graph@VLDB'2025 Workshop Summarycited by 0
  23. 2026ASTRA-QA: A Benchmark for Abstract Question Answering over Documentscited by 0
  24. 2026EMA: Approximate Nearest Neighbor Search with General Attribute Filtering and Dynamic Updatescited by 0
  25. 2026IA-RAG: Interval-Algebra-Driven Temporal Reasoning for Dynamic Knowledge Retrievalcited by 0
  26. 2026LLMs+Graphs: Toward Graph-Native, Synergistic AI Systemscited by 0
  27. 2026When Cognitive Graphs Meet LLMs: BDEI Cognitive Pathways for Panic Emotional Arousal Predictioncited by 0
  28. 2026DocMaster: A Hierarchical Structure-Aware System for Document Analysiscited by 0
  29. 2026GraphContainer: A Unified Platform for Comparing and Debugging Graph RAG Methodscited by 0
  30. 2026R2Adapter: A Routing and Rewriting Adapter for Efficient Hybrid RAGcited by 0
  31. 2026VikingRAG: Accurate and Token-efficient Retrieval-augmented Generation over Structured Documentscited by 0
  32. 2026RAFT: A Stateful Retrieval-Augmented Framework for Troubleshooting Agentscited by 0
  33. 2026Potential for Enhanced Learning in Machine Learning Classes by Using Wiki LLM Indexingcited by 0
  34. 2026TopoGraphRAG-Bench: Evaluating Multimodal GraphRAG on Layout-Grounded Evidence Reasoningcited by 0

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  3. 2025Synergizing RAG and Reasoning: A Systematic Review
  4. 2024A Comprehensive Survey of Retrieval-Augmented Generation (RAG): Evolution, Current Landscape and Future Directions
  5. 2025Query-Centric Graph Retrieval Augmented Generation
  6. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directions
  7. 2024SimGRAG: Leveraging Similar Subgraphs for Knowledge Graphs Driven Retrieval-Augmented Generation
  8. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs