1 Introduction  
2 Comparison with Related Techniques and Surveys  
2.1 RAG  
2.2 LLMs on Graphs  
2.3 KBQA  
3 Preliminaries  
3.1 Text-Attributed Graphs  
3.2 Graph Neural Networks  
3.3 Language Models  
4 Overview of GraphRAG  
5 Graph-Based Indexing  
5.1 Graph Data  
5.2 Indexing  
6 Graph-Guided Retrieval  
6.1 Retriever  
6.2 Retrieval Paradigm  
6.3 Retrieval Granularity  
6.4 Retrieval Enhancement  
7 Graph-Enhanced Generation  
7.1 Generators  
7.2 Graph Formats  
7.3 Generation Enhancement  
8 Training  
8.1 Training Strategies of Retriever  
8.2 Training of Generator  
8.3 Joint Training  
9 Applications and Evaluation  
9.1 Downstream Tasks  
9.2 Application Domains  
9.3 Benchmarks and Metrics  
9.4 GraphRAG in Industry  
10 Future Prospects  
10.1 Dynamic and Adaptive Graphs  
10.2 Multi-Modality Information Integration  
10.3 Scalable and Efficient Retrieval Mechanisms  
10.4 Combination with Graph Foundation Model  
10.5 Lossless Compression of Retrieved Context  
10.6 Standard Benchmarks  
10.7 Broader Applications  
11 Conclusion