Democratizing GraphRAG: Linear, CPU-Only Graph Retrieval for Multi-Hop QA

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1. Verfasser: Wang, Qizhi
Format: Preprint
Veröffentlicht: 2025
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_version_ 1866910034657542144
author Wang, Qizhi
author_facet Wang, Qizhi
contents GraphRAG systems improve multi-hop retrieval by modeling structure, but many approaches rely on expensive LLM-based graph construction and GPU-heavy inference. We present SPRIG (Seeded Propagation for Retrieval In Graphs), a CPU-only, linear-time, token-free GraphRAG pipeline that replaces LLM graph building with lightweight NER-driven co-occurrence graphs and uses Personalized PageRank (PPR) for 28% with negligible Recall@10 changes. The results characterize when CPU-friendly graph retrieval helps multi-hop recall and when strong lexical hybrids (RRF) are sufficient, outlining a realistic path to democratizing GraphRAG without token costs or GPU requirements.
format Preprint
id arxiv_https___arxiv_org_abs_2602_23372
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Democratizing GraphRAG: Linear, CPU-Only Graph Retrieval for Multi-Hop QA
Wang, Qizhi
Information Retrieval
Artificial Intelligence
Computation and Language
68T50 (Primary) 68P20, 68T05 (Secondary)
H.3.3; I.2.7; I.2.6
GraphRAG systems improve multi-hop retrieval by modeling structure, but many approaches rely on expensive LLM-based graph construction and GPU-heavy inference. We present SPRIG (Seeded Propagation for Retrieval In Graphs), a CPU-only, linear-time, token-free GraphRAG pipeline that replaces LLM graph building with lightweight NER-driven co-occurrence graphs and uses Personalized PageRank (PPR) for 28% with negligible Recall@10 changes. The results characterize when CPU-friendly graph retrieval helps multi-hop recall and when strong lexical hybrids (RRF) are sufficient, outlining a realistic path to democratizing GraphRAG without token costs or GPU requirements.
title Democratizing GraphRAG: Linear, CPU-Only Graph Retrieval for Multi-Hop QA
topic Information Retrieval
Artificial Intelligence
Computation and Language
68T50 (Primary) 68P20, 68T05 (Secondary)
H.3.3; I.2.7; I.2.6
url https://arxiv.org/abs/2602.23372