In-depth Analysis of Graph-based RAG in a Unified Framework

2025/03/06 by Yingli Zhou, Yang Su, Zhou, Yingli +18 · 12 citations
Computer Science · #Advanced Graph Neural Networks #Computation and Language (cs.CL) #Databases (cs.DB) #FOS: Computer and information sciences #Information Retrieval (cs.IR) #Multimodal Machine Learning Applications #Topic Modeling

paper · pdf · doi:10.48550/arxiv.2503.04338

openalex publication_date 2025/03/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

Graph-based Retrieval-Augmented Generation (RAG) has proven effective in integrating external knowledge into large language models (LLMs), improving their factual accuracy, adaptability, interpretability, and trustworthiness. A number of graph-based RAG methods have been proposed in the literature. However, these methods have not been systematically and comprehensively compared under the same experimental settings. In this paper, we first summarize a unified framework to incorporate all graph-based RAG methods from a high-level perspective. We then extensively compare representative graph-based RAG methods over a range of questing-answering (QA) datasets -- from specific questions to abstract questions -- and examine the effectiveness of all methods, providing a thorough analysis of graph-based RAG approaches. As a byproduct of our experimental analysis, we are also able to identify new variants of the graph-based RAG methods over specific QA and abstract QA tasks respectively, by combining existing techniques, which outperform the state-of-the-art methods. Finally, based on these findings, we offer promising research opportunities. We believe that a deeper understanding of the behavior of existing methods can provide new valuable insights for future research.

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• BookRAG: A Hierarchical Structure-aware Index-based Approach for Retrieval-Augmented Generation on Complex Documents
• LiCoMemory: Lightweight and Cognitive Agentic Memory for Efficient Long-Term Reasoning
• All-in-one Graph-based Indexing for Hybrid Search on GPUs
• AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs
• Prior Knowledge Makes It Possible: From Sublinear Graph Algorithms to LLM Test-Time Methods
• LinearRAG: Linear Graph Retrieval Augmented Generation on Large-scale Corpora
• You Don't Need Pre-built Graphs for RAG: Retrieval Augmented Generation with Adaptive Reasoning Structures
• Beyond Chunks and Graphs: Retrieval-Augmented Generation through Triplet-Driven Thinking
• MemGraphRAG: Memory-based Multi-Agent System for Graph Retrieval-Augmented Generation
• Test-Time-Matching: Decouple Personality, Memory, and Linguistic Style in LLM-based Role-Playing Language Agent

Related

• Graph-based Approaches and Functionalities in Retrieval-Augmented Generation: A Comprehensive Survey
• Clue-RAG: Towards Accurate and Cost-Efficient Graph-based RAG via Multi-Partite Graph and Query-Driven Iterative Retrieval
• Graph Retrieval-Augmented Generation: A Survey
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• AGRAG: Advanced Graph-based Retrieval-Augmented Generation for LLMs