mlsanityData to 7 Oct 2026

G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering

arXiv 2402.07630 ↗First posted 12 Feb 2024cs.LGcited by 217

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

Abstract (arXiv)

Given a graph with textual attributes, we enable users to `chat with their graph': that is, to ask questions about the graph using a conversational interface. In response to a user's questions, our method provides textual replies and highlights the relevant parts of the graph. While existing works integrate large language models (LLMs) and graph neural networks (GNNs) in various ways, they mostly focus on either conventional graph tasks (such as node, edge, and graph classification), or on answering simple graph queries on small or synthetic graphs. In contrast, we develop a flexible question-answering framework targeting real-world textual graphs, applicable to multiple applications including scene graph understanding, common sense reasoning, and knowledge graph reasoning. Toward this goal, we first develop a Graph Question Answering (GraphQA) benchmark with data collected from different tasks. Then, we propose our G-Retriever method, introducing the first retrieval-augmented generation (RAG) approach for general textual graphs, which can be fine-tuned to enhance graph understanding via soft prompting. To resist hallucination and to allow for textual graphs that greatly exceed the LLM's context window size, G-Retriever performs RAG over a graph by formulating this task as a Prize-Collecting Steiner Tree optimization problem. Empirical evaluations show that our method outperforms baselines on textual graph tasks from multiple domains, scales well with larger graph sizes, and mitigates hallucination.~footnoteOur codes and datasets are available at: urlhttps://github.com/XiaoxinHe/G-Retriever

Key facts. Cited by 217 papers in the map (data through 7 Oct 2026). Placed in Knowledge-graph and structured-knowledge RAG, one of 15 directions in Retrieval-augmented generation. Builds on 46 of its 59 references that are in the map. First posted on arXiv 12 Feb 2024.

Builds on

46 of 59 references.

References · 46

  1. 2021LoRA: Low-Rank Adaptation of Large Language Modelscited by 14,084
  2. 2023Llama 2: Open Foundation and Fine-Tuned Chat Modelscited by 12,865
  3. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  4. 2016Semi-Supervised Classification with Graph Convolutional Networkscited by 11,282
  5. 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
  6. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,049
  7. 2017Graph Attention Networkscited by 8,854
  8. 2023Visual Instruction Tuningcited by 7,792
  9. 2022Large Language Models are Zero-Shot Reasonerscited by 4,323
  10. 2021Prefix-Tuning: Optimizing Continuous Prompts for Generationcited by 3,878
  11. 2021The Power of Scale for Parameter-Efficient Prompt Tuningcited by 3,769
  12. 2023MiniGPT-4: Enhancing Vision-Language Understanding with Advanced Large Language Modelscited by 2,689
34 more
  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,185
  2. 2023A Survey on Hallucination in Large Language Models: Principles, Taxonomy, Challenges, and Open Questionscited by 1,582
  3. 2023LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attentioncited by 776
  4. 2023NExT-GPT: Any-to-Any Multimodal LLMcited by 610
  5. 2020Masked Label Prediction: Unified Message Passing Model for Semi-Supervised Classificationcited by 500
  6. 2023StructGPT: A General Framework for Large Language Model to Reason over Structured Datacited by 339
  7. 2023Reasoning on Graphs: Faithful and Interpretable Large Language Model Reasoningcited by 326
  8. 2023Exploring the Potential of Large Language Models (LLMs) in Learning on Graphscited by 271
  9. 2023Can Language Models Solve Graph Problems in Natural Language?cited by 249
  10. 2023One for All: Towards Training One Graph Model for All Classification Taskscited by 239
  11. 2023GraphGPT: Graph Instruction Tuning for Large Language Modelscited by 223
  12. 2023Language is All a Graph Needscited by 219
  13. 2023Large Language Models on Graphs: A Comprehensive Surveycited by 190
  14. 2023Talk like a Graph: Encoding Graphs for Large Language Modelscited by 175
  15. 2023Harnessing Explanations: LLM-to-LM Interpreter for Enhanced Text-Attributed Graph Representation Learningcited by 132
  16. 2024Let Your Graph Do the Talking: Encoding Structured Data for LLMscited by 112
  17. 2023GraphText: Graph Reasoning in Text Spacecited by 110
  18. 2023GraphLLM: Boosting Graph Reasoning Ability of Large Language Modelcited by 105
  19. 2023A Survey of Graph Meets Large Language Model: Progress and Future Directionscited by 84
  20. 2023Graph-ToolFormer: To Empower LLMs with Graph Reasoning Ability via Prompt Augmented by ChatGPTcited by 78
  21. 2023Label-free Node Classification on Graphs with Large Language Models (LLMS)cited by 72
  22. 2023GIMLET: A Unified Graph-Text Model for Instruction-Based Molecule Zero-Shot Learningcited by 70
  23. 2023GraphAdapter: Tuning Vision-Language Models With Dual Knowledge Graphcited by 69
  24. 2023Graph Neural Prompting with Large Language Modelscited by 64
  25. 2021ExplaGraphs: An Explanation Graph Generation Task for Structured Commonsense Reasoningcited by 58
  26. 2023Knowledge Graph-Augmented Language Models for Knowledge-Grounded Dialogue Generationcited by 54
  27. 2023Can Large Language Models Empower Molecular Property Prediction?cited by 43
  28. 2023Boosting Logical Reasoning in Large Language Models through a New Framework: The Graph of Thoughtcited by 41
  29. 2023Thought Propagation: An Analogical Approach to Complex Reasoning with Large Language Modelscited by 37
  30. 2023Disentangled Representation Learning with Large Language Models for Text-Attributed Graphscited by 36
  31. 2023Multimodal Graph Learning for Generative Taskscited by 27
  32. 2023Large Language Models as Topological Structure Enhancers for Text-Attributed Graphscited by 27
  33. 2023Graph Meets LLMs: Towards Large Graph Modelscited by 19
  34. 2023Integrating Graphs with Large Language Models: Methods and Prospectscited by 18

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 37; 2025: 97; 2026: 83.

217 papers.

Sort:

By area:

  1. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarizationcited by 1,411
  2. 2024Retrieval-Augmented Generation for AI-Generated Content: A Surveycited by 352
  3. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)cited by 170
  4. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoningcited by 128
  5. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 85
  6. 2024Simple Is Effective: The Roles of Graphs and Large Language Models in Knowledge-Graph-Based Retrieval-Augmented Generationcited by 78
  7. 2024GRAG: Graph Retrieval-Augmented Generationcited by 77
  8. 2024StructRAG: Boosting Knowledge Intensive Reasoning of LLMs via Inference-time Hybrid Information Structurizationcited by 70
  9. 2024CodexGraph: Bridging Large Language Models and Code Repositories via Code Graph Databasescited by 62
  10. 2024RAGChecker: A Fine-grained Framework for Diagnosing Retrieval-Augmented Generationcited by 59
  11. 2024GOFA: A Generative One-For-All Model for Joint Graph Language Modelingcited by 56
  12. 2024KG-RAG: Bridging the Gap Between Knowledge and Creativitycited by 55
38 more
  1. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corporacited by 47
  2. 2024Text-space Graph Foundation Models: Comprehensive Benchmarks and New Insightscited by 44
  3. 2024Can Graph Learning Improve Planning in LLM-based Agents?cited by 42
  4. 2025GFM-RAG: Graph Foundation Model for Retrieval Augmented Generationcited by 40
  5. 2025TS-RAG: Retrieval-Augmented Generation based Time Series Foundation Models are Stronger Zero-Shot Forecastercited by 36
  6. 2024Graph Machine Learning in the Era of Large Language Models (LLMs)cited by 33
  7. 2025A Systematic Review of Key Retrieval-Augmented Generation (RAG) Systems: Progress, Gaps, and Future Directionscited by 30
  8. 2025From Single to Multi-Granularity: Toward Long-Term Memory Association and Selection of Conversational Agentscited by 27
  9. 2024Harnessing Large Language Models for Knowledge Graph Question Answering via Adaptive Multi-Aspect Retrieval-Augmentationcited by 24
  10. 2025GraphRAG-Bench: Challenging Domain-Specific Reasoning for Evaluating Graph Retrieval-Augmented Generationcited by 24
  11. 2025Hyper-RAG: Combating LLM Hallucinations using Hypergraph-Driven Retrieval-Augmented Generationcited by 23
  12. 2025Graphs Meet AI Agents: Taxonomy, Progress, and Future Opportunitiescited by 23
  13. 2025You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structurescited by 23
  14. 2024ConfusedPilot: Confused Deputy Risks in RAG-based LLMscited by 22
  15. 2024Can Knowledge Graphs Make Large Language Models More Trustworthy? An Empirical Study Over Open-ended Question Answeringcited by 20
  16. 2024Decoding on Graphs: Faithful and Sound Reasoning on Knowledge Graphs through Generation of Well-Formed Chainscited by 20
  17. 2024A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applicationscited by 17
  18. 2025Optimizing the Interface Between Knowledge Graphs and LLMs for Complex Reasoningcited by 17
  19. 2025RAS: Retrieval-And-Structuring for Knowledge-Intensive LLM Generationcited by 16
  20. 2025RTL++: Graph-enhanced LLM for RTL Code Generationcited by 16
  21. 2025Graph RAG-Tool Fusioncited by 15
  22. 2025LLM/Agent-as-Data-Analyst: A Surveycited by 15
  23. 2025Don't Let It Hallucinate: Premise Verification via Retrieval-Augmented Logical Reasoningcited by 14
  24. 2025Domain-RAG: Retrieval-Guided Compositional Image Generation for Cross-Domain Few-Shot Object Detectioncited by 14
  25. 2024Towards LifeSpan Cognitive Systemscited by 13
  26. 2025Spatial-RAG: Spatial Retrieval Augmented Generation for Real-World Geospatial Reasoning Questionscited by 13
  27. 2025DeepSieve: Information Sieving via LLM-as-a-Knowledge-Routercited by 13
  28. 2026Graph is a Substrate Across Data Modalitiescited by 13
  29. 2024Graph of Records: Boosting Retrieval Augmented Generation for Long-context Summarization with Graphscited by 12
  30. 2024Human-inspired Perspectives: A Survey on AI Long-term Memorycited by 12
  31. 2024KG-Retriever: Efficient Knowledge Indexing for Retrieval-Augmented Large Language Modelscited by 12
  32. 2025Retrieval-augmented Large Language Models for Financial Time Series Forecastingcited by 12
  33. 2025GraphRAG-R1: Graph Retrieval-Augmented Generation with Process-Constrained Reinforcement Learningcited by 12
  34. 2024GraphXAIN: Narratives to Explain Graph Neural Networkscited by 11
  35. 2025BioMaze: Benchmarking and Enhancing Large Language Models for Biological Pathway Reasoningcited by 10
  36. 2025Contextualizing biological perturbation experiments through languagecited by 10
  37. 2025SRAG: Structured Retrieval-Augmented Generation for Multi-Entity Question Answering over Wikipedia Graphcited by 10
  38. 2025Set-LLM: A Permutation-Invariant LLMcited by 10

50 most cited of 217 All 217 (JSON)

Similar, not cited

Close in wording, no citation either way

  1. 2026GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation
  2. 2025BYOKG-RAG: Multi-Strategy Graph Retrieval for Knowledge Graph Question Answering
  3. 2025MIXRAG : Mixture-of-Experts Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering
  4. 2025Query-Aware Graph Neural Networks for Enhanced Retrieval-Augmented Generation
  5. 2025GPR: Empowering Generation with Graph-Pretrained Retriever
  6. 2025GRIL: Knowledge Graph Retrieval-Integrated Learning with Large Language Models
  7. 2023Graph Reasoning for Question Answering with Triplet Retrieval
  8. 2024RAGraph: A General Retrieval-Augmented Graph Learning Framework