mlsanityData to 14 Oct 2026

XGraphRAG: Interactive Visual Analysis for Graph-based Retrieval-Augmented Generation

arXiv 2506.13782 ↗First posted 10 Jun 2025cs.IR cs.AIcited by 2

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

Abstract (arXiv)

Graph-based Retrieval-Augmented Generation (RAG) has shown great capability in enhancing Large Language Model (LLM)'s answer with an external knowledge base. Compared to traditional RAG, it introduces a graph as an intermediate representation to capture better structured relational knowledge in the corpus, elevating the precision and comprehensiveness of generation results. However, developers usually face challenges in analyzing the effectiveness of GraphRAG on their dataset due to GraphRAG's complex information processing pipeline and the overwhelming amount of LLM invocations involved during graph construction and query, which limits GraphRAG interpretability and accessibility. This research proposes a visual analysis framework that helps RAG developers identify critical recalls of GraphRAG and trace these recalls through the GraphRAG pipeline. Based on this framework, we develop XGraphRAG, a prototype system incorporating a set of interactive visualizations to facilitate users' analysis process, boosting failure cases collection and improvement opportunities identification. Our evaluation demonstrates the effectiveness and usability of our approach. Our work is open-sourced and available at https://github.com/Gk0Wk/XGraphRAG.

Key facts. Cited by 2 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 28 of its 42 references that are in the map. First posted on arXiv 10 Jun 2025.

Builds on

28 of 42 references.

Closest ancestors · 8

Same direction, newest first

  1. 2024LightRAG: Simple and Fast Retrieval-Augmented Generation8 Oct
  2. 2024KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation10 Sep
  3. 2024Graph Retrieval-Augmented Generation: A Survey15 Aug
  4. 2024Think-on-Graph 2.0: Deep and Faithful Large Language Model Reasoning with Knowledge-guided Retrieval Augmented Generation15 Jul
  5. 2024Retrieval-Augmented Generation with Knowledge Graphs for Customer Service Question Answering26 Apr
  6. 2024From Local to Global: A Graph RAG Approach to Query-Focused Summarization24 Apr
  7. 2024G-Retriever: Retrieval-Augmented Generation for Textual Graph Understanding and Question Answering12 Feb
  8. 2023FABULA: Intelligence Report Generation Using Retrieval-Augmented Narrative Construction20 Oct

Other references · 20

  1. 2023Retrieval-Augmented Generation for Large Language Models: A Surveycited by 2,212
  2. 2023BloombergGPT: A Large Language Model for Financecited by 939
  3. 2024A Survey on RAG Meeting LLMs: Towards Retrieval-Augmented Large Language Modelscited by 452
  4. 2024HippoRAG: Neurobiologically Inspired Long-Term Memory for Large Language Modelscited by 281
  5. 2024MultiHop-RAG: Benchmarking Retrieval-Augmented Generation for Multi-Hop Queriescited by 257
  6. 2023FinGPT: Open-Source Financial Large Language Modelscited by 253
  7. 2024HybridRAG: Integrating Knowledge Graphs and Vector Retrieval Augmented Generation for Efficient Information Extractioncited by 93
  8. 2024Medical Graph RAG: Towards Safe Medical Large Language Model via Graph Retrieval-Augmented Generationcited by 90
  9. 2023ChainForge: A Visual Toolkit for Prompt Engineering and LLM Hypothesis Testingcited by 88
  10. 2022Interactive and Visual Prompt Engineering for Ad-hoc Task Adaptation with Large Language Modelscited by 70
  11. 2023PromptMagician: Interactive Prompt Engineering for Text-to-Image Creationcited by 58
  12. 2024DALK: Dynamic Co-Augmentation of LLMs and KG to answer Alzheimer's Disease Questions with Scientific Literaturecited by 50
8 more
  1. 2023Sensecape: Enabling Multilevel Exploration and Sensemaking with Large Language Modelscited by 48
  2. 2024AgentCoord: Visually Exploring Coordination Strategy for LLM-based Multi-Agent Collaborationcited by 26
  3. 2024AgentLens: Visual Analysis for Agent Behaviors in LLM-based Autonomous Systemscited by 23
  4. 2023XNLI: Explaining and Diagnosing NLI-based Visual Data Analysiscited by 19
  5. 2024JailbreakLens: Visual Analysis of Jailbreak Attacks Against Large Language Modelscited by 7
  6. 2024UniOQA: A Unified Framework for Knowledge Graph Question Answering with Large Language Modelscited by 5
  7. 2023SPROUT: an Interactive Authoring Tool for Generating Programming Tutorials with the Visualization of Large Language Modelscited by 4
  8. 2024DepsRAG: Towards Agentic Reasoning and Planning for Software Dependency Managementcited by 3

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 2.

2 papers.

  1. 2026RAGExplorer: A Visual Analytics System for the Comparative Diagnosis of RAG Systemscited by 2
  2. 2026FootprintRAG: Visual Analytics for Evidence Context Refinement in RAG-based Scientific Literature Explorationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025RAG vs. GraphRAG: A Systematic Evaluation and Key Insights
  2. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
  3. 2024Retrieval-Augmented Generation with Graphs (GraphRAG)
  4. 2026Use Graph When It Needs: Efficiently and Adaptively Integrating Retrieval-Augmented Generation with Graphs
  5. 2025NodeRAG: Structuring Graph-based RAG with Heterogeneous Nodes
  6. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  7. 2025PathRAG: Pruning Graph-based Retrieval Augmented Generation with Relational Paths
  8. 2025LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora