---
title: "Retrieval-Augmented Generation with Graphs (GraphRAG) (2025)"
description: "The paper introduces a unified framework of five components and reviews typical techniques for… 573 references, cited by 57 papers here."
authors: ["Haoyu Han", "Yu Wang", "Harry Shomer", "Kai Guo", "Jiayuan Ding", "Yongjia Lei", "Mahantesh Halappanavar", "Ryan A. Rossi", "Subhabrata Mukherjee", "Xianfeng Tang", "Qi He", "Zhigang Hua", "Bo Long", "Tong Zhao", "Neil Shah", "Amin Javari", "Yinglong Xia", "Jiliang Tang"]
year: 2025
venue: "arXiv:2501.00309"
source: "https://arxiv.org/abs/2501.00309"
doi: "10.48550/arxiv.2501.00309"
arxiv: "2501.00309"
licence: "arXiv — non-exclusive license to distribute"
licence_url: "http://arxiv.org/licenses/nonexclusive-distrib/1.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/han-2025-retrieval-augmented-generation-graphs-graphrag/"
---
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# Retrieval-Augmented Generation with Graphs (GraphRAG)

**Authors:** [Haoyu Han](https://md.research.elamur.ai/free/en/authors/haoyu-han/), [Yu Wang](https://md.research.elamur.ai/free/en/authors/yu-wang/), [Harry Shomer](https://md.research.elamur.ai/free/en/authors/harry-shomer/), [Kai Guo](https://md.research.elamur.ai/free/en/authors/kai-guo/), Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, [Ryan A. Rossi](https://md.research.elamur.ai/free/en/authors/ryan-a-rossi/), [Subhabrata Mukherjee](https://md.research.elamur.ai/free/en/authors/subhabrata-mukherjee/), [Xianfeng Tang](https://md.research.elamur.ai/free/en/authors/xianfeng-tang/), [Qi He](https://md.research.elamur.ai/free/en/authors/qi-he/), Zhigang Hua, Bo Long, [Tong Zhao](https://md.research.elamur.ai/free/en/authors/tong-zhao/), Neil Shah, Amin Javari, Yinglong Xia and [Jiliang Tang](https://md.research.elamur.ai/free/en/authors/jiliang-tang/) · **Year:** 2025 · **Venue:** arXiv:2501.00309 · **Licence:** [arXiv — non-exclusive license to distribute](http://arxiv.org/licenses/nonexclusive-distrib/1.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2501.00309) · [PDF](https://arxiv.org/pdf/2501.00309) · [DOI 10.48550/arxiv.2501.00309](https://doi.org/10.48550/arxiv.2501.00309) · [arXiv 2501.00309](https://arxiv.org/abs/2501.00309) · [OpenAlex](https://openalex.org/W4406031095)

**Citations:** 30 citations (OpenAlex, 2026-10-14) · [57](https://md.research.elamur.ai/free/en/papers/han-2025-retrieval-augmented-generation-graphs-graphrag/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
Conventional retrieval-augmented generation (RAG) stores knowledge as separate text or image chunks and finds them through lexical or embedding similarity. This approach overlooks how pieces of information relate to one another. Han, Wang et al. (Michigan State University, with partners including Meta, Amazon, Adobe and Snap) survey GraphRAG, the family of methods that retrieve from graph-structured sources. They argue that graphs need dedicated RAG designs for three reasons: graph data comes in many formats, its elements depend on each other, and relational patterns vary sharply across domains.

The paper introduces a unified framework of five components and reviews typical techniques for each, such as entity linking, BFS/DFS traversal, community detection, GNN encoders and graph pruning. It then works through application domains one by one, listing tasks, example queries and representative studies. A central observation is that existing work focuses heavily on knowledge and document graphs, while areas such as infrastructure graphs receive little attention. The survey closes with open challenges, offering a reference map for building retrieval over relational data.

## Key points
- The framework has five parts: a source of graph data, a module that processes incoming queries, a retriever, an organizer of retrieved content, and a generator.
- Three contrasts with standard RAG motivate the survey:
  - unified vs. diverse data formats;
  - independent vs. interdependent information;
  - domain-invariant vs. domain-specific relations.
- In a motivating biomedical query (drugs for epithelioid sarcoma that also act on the EZH2 gene product), the answer requires following *indication* and *target* edges and intersecting the results. BM25 or embedding search alone misses this.
- The domains covered are Knowledge, Document, Scientific (molecules), Social, Reasoning & Planning, Tabular, Infrastructure, Biological, Scene and Random graphs. Example tasks include KG QA, KG completion, document summarization, molecule property prediction, table QA, fraud detection and gene imputation.
- The paper is a preprint (arXiv:2501.00309v2, January 2025). Its repository is maintained at github.com/Graph-RAG/GraphRAG.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Amazon](https://md.research.elamur.ai/free/en/companies/amazon/) — Company, Seattle, United States · 128 works · 41,700 citations
- [Adobe Systems](https://md.research.elamur.ai/free/en/companies/adobe-systems/) — Company, San Jose, United States · 40 works · 4,251 citations
- [University of Oregon](https://md.research.elamur.ai/free/en/universities/university-of-oregon/) — University, Eugene, United States · 26 works · 3,555 citations
- [Michigan State University](https://md.research.elamur.ai/free/en/universities/michigan-state-university/) — University, East Lansing, United States · 30 works · 1,375 citations
- [Snap](https://md.research.elamur.ai/free/en/companies/snap/) — Company, Los Angeles, United States · 6 works · 7,639 citations
- [Pacific Northwest National Laboratory](https://md.research.elamur.ai/free/en/labs/pacific-northwest-national-laboratory/) — Research institute, Richland, United States · 4 works · 1,537 citations

Also on the byline: [Home Depot](https://ror.org/031603425) · [Hippocration General Hospital](https://ror.org/05v5wwy67).

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🧭 Topics (1): [🔍 Information retrieval & search](https://md.research.elamur.ai/free/en/topics/information-retrieval/) · 552 works

## Cite

Cite as: Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He, Zhigang Hua, Bo Long, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia and Jiliang Tang. 2025. Retrieval-Augmented Generation with Graphs (GraphRAG). arXiv:2501.00309.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{han2025retrieval,
  title = {{Retrieval-Augmented Generation with Graphs (GraphRAG)}},
  author = {Haoyu Han, Yu Wang, Harry Shomer, Kai Guo, Jiayuan Ding, Yongjia Lei, Mahantesh Halappanavar, Ryan A. Rossi, Subhabrata Mukherjee, Xianfeng Tang, Qi He, Zhigang Hua, Bo Long, Tong Zhao, Neil Shah, Amin Javari, Yinglong Xia and Jiliang Tang},
  year = {2025},
  doi = {10.48550/arxiv.2501.00309},
  eprint = {2501.00309},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2501.00309}
}
```

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*Updated 2 November 2026*