mlsanityData to 14 Oct 2026

Advanced RAG Models with Graph Structures: Optimizing Complex Knowledge Reasoning and Text Generation

arXiv 2411.03572 ↗First posted 6 Nov 2024cs.IRcited by 13

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

Abstract (arXiv)

This study aims to optimize the existing retrieval-augmented generation model (RAG) by introducing a graph structure to improve the performance of the model in dealing with complex knowledge reasoning tasks. The traditional RAG model has the problem of insufficient processing efficiency when facing complex graph structure information (such as knowledge graphs, hierarchical relationships, etc.), which affects the quality and consistency of the generated results. This study proposes a scheme to process graph structure data by combining graph neural network (GNN), so that the model can capture the complex relationship between entities, thereby improving the knowledge consistency and reasoning ability of the generated text. The experiment used the Natural Questions (NQ) dataset and compared it with multiple existing generation models. The results show that the graph-based RAG model proposed in this paper is superior to the traditional generation model in terms of quality, knowledge consistency, and reasoning ability, especially when dealing with tasks that require multi-dimensional reasoning. Through the combination of the enhancement of the retrieval module and the graph neural network, the model in this study can better handle complex knowledge background information and has broad potential value in multiple practical application scenarios.

Key facts. Cited by 13 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 16 of its 22 references that are in the map. First posted on arXiv 6 Nov 2024.

Builds on

16 of 22 references.

Closest ancestors · 1

Same direction, newest first

  1. 2024GNN-RAG: Graph Neural Retrieval for Large Language Model Reasoning30 May

Other references · 15

  1. 2024Blended RAG: Improving RAG (Retriever-Augmented Generation) Accuracy with Semantic Search and Hybrid Query-Based Retrieverscited by 84
  2. 2024Transforming Multidimensional Time Series into Interpretable Event Sequences for Advanced Data Miningcited by 47
  3. 2024Self-Supervised Graph Neural Networks for Enhanced Feature Extraction in Heterogeneous Information Networkscited by 33
  4. 2024Breast Cancer Image Classification Method Based on Deep Transfer Learningcited by 12
  5. 2024Dynamic Hypergraph-Enhanced Prediction of Sequential Medical Visitscited by 12
  6. 2024Wasserstein Distance-Weighted Adversarial Network for Cross-Domain Credit Risk Assessmentcited by 11
  7. 2024Optimizing YOLOv5s Object Detection through Knowledge Distillation algorithmcited by 11
  8. 2024Efficient and Aesthetic UI Design with a Deep Learning-Based Interface Generation Tree Algorithmcited by 11
  9. 2024Algorithm Research of ELMo Word Embedding and Deep Learning Multimodal Transformer in Image Descriptioncited by 10
  10. 2024Adversarial Neural Networks in Medical Imaging Advancements and Challenges in Semantic Segmentationcited by 10
  11. 2024Unveiling the Potential of Graph Neural Networks in SME Credit Risk Assessmentcited by 9
  12. 2024A Recommendation Model Utilizing Separation Embedding and Self-Attention for Feature Miningcited by 8
3 more
  1. 2024Convolutional Neural Networks for Predictive Modeling of Lung Diseasecited by 7
  2. 2024Deep Learning-Based Channel Squeeze U-Structure for Lung Nodule Detection and Segmentationcited by 6
  3. 2024Predicting Liquidity Coverage Ratio with Gated Recurrent Units: A Deep Learning Model for Risk Managementcited by 6

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Citing papers per year: 2024: 6; 2025: 7.

13 papers.

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  1. 2025A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Modelscited by 88
  2. 2025HM-RAG: Hierarchical Multi-Agent Multimodal Retrieval Augmented Generationcited by 31
  3. 2025What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyondcited by 22
  4. 2024Graph Neural Network-Based Entity Extraction and Relationship Reasoning in Complex Knowledge Graphscited by 9
  5. 2024Enhancing Few-Shot Learning with Integrated Data and GAN Model Approachescited by 9
  6. 2025Can Knowledge-Graph-based Retrieval Augmented Generation Really Retrieve What You Need?cited by 8
  7. 2024Leveraging Semi-Supervised Learning to Enhance Data Mining for Image Classification under Limited Labeled Datacited by 7
  8. 2024Accurate Medical Named Entity Recognition Through Specialized NLP Modelscited by 7
  9. 2024Leveraging Generative Adversarial Networks for Addressing Data Imbalance in Financial Market Supervisioncited by 6
  10. 2025AgentAda: Skill-Adaptive Data Analytics for Tailored Insight Discoverycited by 6
  11. 2024Enhancing Recommendation Systems with GNNs and Addressing Over-Smoothingcited by 5
  12. 2025IaC Generation with LLMs: An Error Taxonomy and A Study on Configuration Knowledge Injectioncited by 2
1 more
  1. 2025LMAR: Language Model Augmented Retriever for Domain-specific Knowledge Indexingcited by 1

Similar, not cited

Close in wording, no citation either way

  1. 2025GFM-RAG: Graph Foundation Model for Retrieval Augmented Generation
  2. 2025Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
  3. 2025In-depth Analysis of Graph-based RAG in a Unified Framework
  4. 2026NGM-RAG: Neural Graph Matching based Retrieval-Augmented Generation
  5. 2025Evaluating Knowledge Graph Based Retrieval Augmented Generation Methods under Knowledge Incompleteness
  6. 2026GLM-RAG: Graph Language Models for Graph-Based Retrieval-Augmented Generation
  7. 2025When to use Graphs in RAG: A Comprehensive Analysis for Graph Retrieval-Augmented Generation
  8. 2025Pseudo-Knowledge Graph: Meta-Path Guided Retrieval and In-Graph Text for RAG-Equipped LLM