DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation

• URL: http://arxiv.org/abs/2602.08545v1
• Date: Mon, 09 Feb 2026 11:45:13 GMT
• Title: DA-RAG: Dynamic Attributed Community Search for Retrieval-Augmented Generation
• Authors: Xingyuan Zeng, Zuohan Wu, Yue Wang, Chen Zhang, Quanming Yao, Libin Zheng, Jian Yin,
• Abstract summary: A prevalent technical approach in this context is graph-based RAG (G-RAG)<n>We propose DA-RAG, which leverages attributed community search (ACS) to extract relevant subgraphs based on the queried question dynamically.<n>We evaluate DA-RAG on multiple datasets, demonstrating that it outperforms existing RAG methods by up to 40% in head-to-head comparisons.
• Score: 35.30060374506784
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Owing to their unprecedented comprehension capabilities, large language models (LLMs) have become indispensable components of modern web search engines. From a technical perspective, this integration represents retrieval-augmented generation (RAG), which enhances LLMs by grounding them in external knowledge bases. A prevalent technical approach in this context is graph-based RAG (G-RAG). However, current G-RAG methodologies frequently underutilize graph topology, predominantly focusing on low-order structures or pre-computed static communities. This limitation affects their effectiveness in addressing dynamic and complex queries. Thus, we propose DA-RAG, which leverages attributed community search (ACS) to extract relevant subgraphs based on the queried question dynamically. DA-RAG captures high-order graph structures, allowing for the retrieval of self-complementary knowledge. Furthermore, DA-RAG is equipped with a chunk-layer oriented graph index, which facilitates efficient multi-granularity retrieval while significantly reducing both computational and economic costs. We evaluate DA-RAG on multiple datasets, demonstrating that it outperforms existing RAG methods by up to 40% in head-to-head comparisons across four metrics while reducing index construction time and token overhead by up to 37% and 41%, respectively.

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