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Publications of SPCL

M. Besta, L. Jarmocik, O. Hrycyna, S. Klaiman, K. Mączka, R. Gerstenberger, J. Müller, P. Nyczyk, H. Niewiadomski, T. Hoefler:

 GraphSeek: Next-Generation Graph Analytics with LLMs

(arXiv:2602.11052. Feb. 2026)

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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BibTeX

@article{besta2026graphseek,
  author={Maciej Besta and Lukasz Jarmocik and Orest Hrycyna and Shachar Klaiman and Konrad Mączka and Robert Gerstenberger and Jürgen Müller and Piotr Nyczyk and Hubert Niewiadomski and Torsten Hoefler},
  title={{GraphSeek: Next-Generation Graph Analytics with LLMs}},
  journal={arXiv:2602.11052},
  year={2026},
  month={02},
  doi={10.48550/arXiv.2602.11052},
}