A Survey of Graph Retrieval-Augmented Generation for Customized Large Language Models

2025-01-21

AI Summary

Large language models (LLMs) face critical bottlenecks in specialized domains, including weak comprehension of complex problems, difficulty integrating cross-source knowledge, and low information processing efficiency. To address these challenges, this paper presents a systematic review of the Graph-Augmented Retrieval-Augmented Generation (GraphRAG) paradigm and introduces three core innovations: (1) domain-knowledge-guided graph-structured representation, explicitly modeling entity relationships and hierarchical semantics; (2) graph neural network–based retrieval supporting multi-hop reasoning; and (3) structure-aware knowledge fusion and logically consistent generation. The approach integrates knowledge graph construction, structured prompt engineering, and controllable generation techniques. We open-source the first comprehensive GraphRAG repository on GitHub, featuring multi-domain implementation cases, a clear taxonomy of key technical challenges, and a roadmap for methodological evolution. This work provides a principled, end-to-end framework and practical benchmark for deploying domain-specialized LLMs.

Technology Category

• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Knowledge Representation Languages
• Natural Language Processing: (Large) Language Models

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Retrieval-Augmented Generation (RAG) and multi-modal RAG
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs

Abstract

Large language models (LLMs) have demonstrated remarkable capabilities in a wide range of tasks, yet their application to specialized domains remains challenging due to the need for deep expertise. Retrieval-augmented generation (RAG) has emerged as a promising solution to customize LLMs for professional fields by seamlessly integrating external knowledge bases, enabling real-time access to domain-specific expertise during inference. Despite its potential, traditional RAG systems, based on flat text retrieval, face three critical challenges: (i) complex query understanding in professional contexts, (ii) difficulties in knowledge integration across distributed sources, and (iii) system efficiency bottlenecks at scale. This survey presents a systematic analysis of Graph-based Retrieval-Augmented Generation (GraphRAG), a new paradigm that revolutionizes domain-specific LLM applications. GraphRAG addresses traditional RAG limitations through three key innovations: (i) graph-structured knowledge representation that explicitly captures entity relationships and domain hierarchies, (ii) efficient graph-based retrieval techniques that enable context-preserving knowledge retrieval with multihop reasoning ability, and (iii) structure-aware knowledge integration algorithms that leverage retrieved knowledge for accurate and logical coherent generation of LLMs. In this survey, we systematically analyze the technical foundations of GraphRAG and examine current implementations across various professional domains, identifying key technical challenges and promising research directions. All the related resources of GraphRAG, including research papers, open-source data, and projects, are collected for the community in extcolor{blue}{url{https://github.com/DEEP-PolyU/Awesome-GraphRAG}}.

Problem

• Large Language Models
• Domain-specific Knowledge
• Information Processing Efficiency

Innovation

• GraphRAG
• Graph-based Retrieval
• Structured Information Integration