mlsanityData to 14 Oct 2026

NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes

arXiv 2504.11544 ↗First posted 15 Apr 2025cs.AIcited by 26

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

Abstract (arXiv)

Retrieval-augmented generation (RAG) empowers large language models to access external and private corpus, enabling factually consistent responses in specific domains. By exploiting the inherent structure of the corpus, graph-based RAG methods further enrich this process by building a knowledge graph index and leveraging the structural nature of graphs. However, current graph-based RAG approaches seldom prioritize the design of graph structures. Inadequately designed graph not only impede the seamless integration of diverse graph algorithms but also result in workflow inconsistencies and degraded performance. To further unleash the potential of graph for RAG, we propose NodeRAG, a graph-centric framework introducing heterogeneous graph structures that enable the seamless and holistic integration of graph-based methodologies into the RAG workflow. By aligning closely with the capabilities of LLMs, this framework ensures a fully cohesive and efficient end-to-end process. Through extensive experiments, we demonstrate that NodeRAG exhibits performance advantages over previous methods, including GraphRAG and LightRAG, not only in indexing time, query time, and storage efficiency but also in delivering superior question-answering performance on multi-hop benchmarks and open-ended head-to-head evaluations with minimal retrieval tokens. Our GitHub repository could be seen at https://github.com/Terry-Xu-666/NodeRAG.

Key facts. Cited by 26 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 26 of its 40 references that are in the map. First posted on arXiv 15 Apr 2025.

Builds on

26 of 40 references.

Closest ancestors · 3

Same direction, newest first

  1. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)31 Dec
  2. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  3. 2024RAPTOR: Recursive Abstractive Processing for Tree-Organized Retrieval31 Jan

Other references · 23

  1. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,297
  2. 2018HotpotQA: A Dataset for Diverse, Explainable Multi-hop Question Answeringcited by 3,754
  3. 2023Lost in the Middle: How Language Models Use Long Contextscited by 3,455
  4. 2022Interleaving Retrieval with Chain-of-Thought Reasoning for Knowledge-Intensive Multi-Step Questionscited by 923
  5. 2016Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphscited by 861
  6. 2022Precise Zero-Shot Dense Retrieval without Relevance Labelscited by 517
  7. 2018From Louvain to Leiden: guaranteeing well-connected communitiescited by 385
  8. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 354
  9. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 328
  10. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  11. 2023Knowledge Graph Prompting for Multi-Document Question Answeringcited by 152
  12. 2023Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answeringcited by 119
11 more
  1. 2024LongRAG: Enhancing Retrieval-Augmented Generation with Long-context LLMscited by 108
  2. 2023Lift Yourself Up: Retrieval-augmented Text Generation with Self Memorycited by 103
  3. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 90
  4. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 56
  5. 2021Joint Passage Ranking for Diverse Multi-Answer Retrievalcited by 43
  6. 2024RAG-QA Arena: Evaluating Domain Robustness for Long-form Retrieval Augmented Question Answeringcited by 43
  7. 2024BioRAG: A RAG-LLM Framework for Biological Question Reasoningcited by 43
  8. 2021Dense Hierarchical Retrieval for Open-Domain Question Answeringcited by 34
  9. 2024FairRAG: Fair Human Generation via Fair Retrieval Augmentationcited by 34
  10. 2024TC-RAG:Turing-Complete RAG's Case study on Medical LLM Systemscited by 28
  11. 2024RAGGED: Towards Informed Design of Scalable and Stable RAG Systemscited by 24

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 12; 2026: 14.

26 papers.

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  1. 2025MemOS: A Memory OS for AI Systemcited by 138
  2. 2025A Survey of Context Engineering for Large Language Modelscited by 126
  3. 2025MemOS: An Operating System for Memory-Augmented Generation (MAG) in Large Language Modelscited by 73
  4. 2025Graph-R1: Towards Agentic GraphRAG Framework via End-to-end Reinforcement Learningcited by 52
  5. 2025Large Language Models Meet Knowledge Graphs for Question Answering: Synthesis and Opportunitiescited by 31
  6. 2026Temporal Validity in Retrieval Memory: Eliminating Stale-Fact Errors for AI Agents over Evolving Knowledgecited by 14
  7. 2025GraphSearch: An Agentic Deep Searching Workflow for Graph Retrieval-Augmented Generationcited by 3
  8. 2026HyperGraphPro: Progress-Aware Reinforcement Learning for Structure-Guided Hypergraph RAGcited by 3
  9. 2025Mitigating Hallucination in Large Language Models (LLMs): An Application-Oriented Survey on RAG, Reasoning, and Agentic Systemscited by 2
  10. 2026Retrieving Climate Change Disinformation by Narrativecited by 2
  11. 2026SAGE: A Self-Evolving Agentic Graph-Memory Engine for Structure-Aware Associative Memorycited by 2
  12. 2026Graph-Aware Late Chunking for Retrieval-Augmented Generation in Biomedical Literaturecited by 1
14 more
  1. 2025DGRAG: Distributed Graph-based Retrieval-Augmented Generation in Edge-Cloud Systemscited by 0
  2. 2025GOSU: Retrieval-Augmented Generation with Global-Level Optimized Semantic Unit-Centric Frameworkcited by 0
  3. 2025BambooKG: A Neurobiologically-inspired Frequency-Weight Knowledge Graphcited by 0
  4. 2025PROPEX-RAG: Enhanced GraphRAG using Prompt-Driven Prompt Executioncited by 0
  5. 2025Living the Novel: A System for Generating Self-Training Timeline-Aware Conversational Agents from Novelscited by 0
  6. 2026IGMiRAG: Intuition-Guided Retrieval-Augmented Generation with Adaptive Mining of In-Depth Memorycited by 0
  7. 2026DocNavRAG: Document-Structured Graph RAG with Stateful Evidence Construction for Complex Document Question Answeringcited by 0
  8. 2026VDGR-RAG: Vectors, Directories, Graphs, and Reflection Are All You Need for Unified Reasoning over Hierarchical Enterprise Knowledgecited by 0
  9. 2026GTA-RAG: Graph-Trajectory-Augmented Reinforcement Learning for Multi-Turn Retrieval-Augmented Reasoningcited by 0
  10. 2026Coarse Indexing, Fine Evidence: Decoupling Temporal Granularity in Long-Video RAGcited by 0
  11. 2026Feedback-Assisted Trust Propagation over Document Relation Graphs for Retrieval-Augmented Generationcited by 0
  12. 2026From Repetition to Recognition: Inductive Discovery of Disinformation Narrativescited by 0
  13. 2026STITCH-RAG: Spatio-Temporal Influence Tracing over Topic Hypergraphs for Multi-Hop Retrieval-Augmented Generationcited by 0
  14. 2026Foresight-over-Graph: Reasoning Beyond Local Horizons for Knowledge Base Question Answeringcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2024Graph Retrieval-Augmented Generation: A Survey
  2. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
  3. 2025In-depth Analysis of Graph-based RAG in a Unified Framework
  4. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  5. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  6. 2025MiniRAG: Towards Extremely Simple Retrieval-Augmented Generation
  7. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
  8. 2025mmRAG: A Modular Benchmark for Retrieval-Augmented Generation over Text, Tables, and Knowledge Graphs