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 datasets demonstrate that FastInsight significantly improves both retrieval accuracy and generation quality compared to state-of-the-art baselines, achieving a substantial Pareto improvement in the trade-off between effectiveness and efficiency.

翻译：现有面向语料图进行洞察检索的图RAG方法通常依赖于交织大型语言模型推理的耗时过程。为实现高效的时序洞察检索，本文提出FastInsight。我们首先提出一种图检索分类法，将现有方法归纳为三种基本操作：向量搜索、图搜索和基于模型的搜索。通过该分类法，我们识别出现有方法的两大关键局限：基于模型搜索的拓扑盲目性与图搜索的语义盲目性。FastInsight通过交织两种新型融合算子克服了这些局限：作为图模型搜索运行的基于图的重排序器，以及作为向量-图搜索运行的语义-拓扑扩展器。在广泛检索与生成数据集上的大量实验表明，相比最先进的基线方法，FastInsight在检索准确率和生成质量上均取得显著提升，在效果与效率的权衡中实现了显著的帕累托改进。