GraphSeek: Next-Generation Graph Analytics with LLMs

• URL: http://arxiv.org/abs/2602.11052v1
• Date: Wed, 11 Feb 2026 17:20:06 GMT
• Title: GraphSeek: Next-Generation Graph Analytics with LLMs
• Authors: Maciej Besta, Łukasz Jarmocik, Orest Hrycyna, Shachar Klaiman, Konrad Mączka, Robert Gerstenberger, Jürgen Müller, Piotr Nyczyk, Hubert Niewiadomski, Torsten Hoefler,
• Abstract summary: LLMs promise accessible natural language (NL) graph analytics, but they fail to process industry-scale property graphs effectively and efficiently.<n>We devise a novel abstraction for complex multi-query analytics over such graphs.<n>We use this abstraction as the basis of the first LLM-enhanced graph analytics framework called GraphSeek.
• Score: 15.668202711555749
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Graphs are foundational across domains but remain hard to use without deep expertise. LLMs promise accessible natural language (NL) graph analytics, yet they fail to process industry-scale property graphs effectively and efficiently: such datasets are large, highly heterogeneous, structurally complex, and evolve dynamically. To address this, we devise a novel abstraction for complex multi-query analytics over such graphs. Its key idea is to replace brittle generation of graph queries directly from NL with planning over a Semantic Catalog that describes both the graph schema and the graph operations. Concretely, this induces a clean separation between a Semantic Plane for LLM planning and broader reasoning, and an Execution Plane for deterministic, database-grade query execution over the full dataset and tool implementations. This design yields substantial gains in both token efficiency and task effectiveness even with small-context LLMs. We use this abstraction as the basis of the first LLM-enhanced graph analytics framework called GraphSeek. GraphSeek achieves substantially higher success rates (e.g., 86% over enhanced LangChain) and points toward the next generation of affordable and accessible graph analytics that unify LLM reasoning with database-grade execution over large and complex property graphs.

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