HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation
Abstract
Standard Retrieval-Augmented Generation (RAG) relies on chunk-based retrieval, whereas GraphRAG advances this approach by graph-based knowledge representation. However, existing graph-based RAG approaches are constrained by binary relations, as each edge in an ordinary graph connects only two entities, limiting their ability to represent the n-ary relations (n >= 2) in real-world knowledge. In this work, we propose HyperGraphRAG, the first hypergraph-based RAG method that represents n-ary relational facts via hyperedges. HyperGraphRAG consists of a comprehensive pipeline, including knowledge hypergraph construction, retrieval, and generation. Experiments across medicine, agriculture, computer science, and law demonstrate that HyperGraphRAG outperforms both standard RAG and previous graph-based RAG methods in answer accuracy, retrieval efficiency, and generation quality.
Cite
Text
Luo et al. "HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation." Advances in Neural Information Processing Systems, 2025.Markdown
[Luo et al. "HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation." Advances in Neural Information Processing Systems, 2025.](https://mlanthology.org/neurips/2025/luo2025neurips-hypergraphrag/)BibTeX
@inproceedings{luo2025neurips-hypergraphrag,
title = {{HyperGraphRAG: Retrieval-Augmented Generation via Hypergraph-Structured Knowledge Representation}},
author = {Luo, Haoran and E, Haihong and Chen, Guanting and Zheng, Yandan and Wu, Xiaobao and Guo, Yikai and Lin, Qika and Feng, Yu and Kuang, Zemin and Song, Meina and Zhu, Yifan and Luu, Anh Tuan},
booktitle = {Advances in Neural Information Processing Systems},
year = {2025},
url = {https://mlanthology.org/neurips/2025/luo2025neurips-hypergraphrag/}
}