FastInsight: Fast and Insightful Retrieval via Fusion Operators for Graph RAG
Seonho An, Chaejeong Hyun, Min-Soo Kim

TL;DR
FastInsight introduces a novel, efficient graph retrieval method that combines graph and vector search techniques to improve retrieval accuracy and generation quality in large language model applications.
Contribution
It proposes a new taxonomy of graph retrieval operations and introduces two fusion operators, GRanker and STeX, to address limitations in existing methods.
Findings
Significantly improves retrieval accuracy over baselines
Enhances generation quality in large language model tasks
Achieves better effectiveness-efficiency trade-offs
Abstract
Existing Graph RAG methods aiming for insightful retrieval on corpus graphs typically rely on time-intensive processes that interleave Large Language Model (LLM) reasoning. To enable time-efficient insightful retrieval, we propose FastInsight. We first introduce a graph retrieval taxonomy that categorizes existing methods into three fundamental operations: vector search, graph search, and model-based search. Through this taxonomy, we identify two critical limitations in current approaches: the topology-blindness of model-based search and the semantics-blindness of graph search. FastInsight overcomes these limitations by interleaving two novel fusion operators: the Graph-based Reranker (GRanker), which functions as a graph model-based search, and Semantic-Topological eXpansion (STeX), which operates as a vector-graph search. Extensive experiments on broad retrieval and generation…
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Taxonomy
TopicsTopic Modeling · Advanced Graph Neural Networks · Information Retrieval and Search Behavior
