mlsanityData to 7 Oct 2026

GraphSeek: Next-Generation Graph Analytics with LLMs

arXiv 2602.11052 ↗First posted 11 Feb 2026cs.DB cs.AI cs.CLcited by 1

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Abstract (arXiv)

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.

Key facts. Cited by 1 paper in the map (data through 7 Oct 2026). Builds on 90 of its 199 references that are in the map. First posted on arXiv 11 Feb 2026.

Builds on

90 of 199 references.

References · 90

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38 more
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50 most cited of 90 All 90 (JSON)

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Performance Foundations of Parallel & Distributed Reasoning Language Modelscited by 0

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  5. 2026Are Large Language Models Suitable for Graph Computation? Progress and Prospects
  6. 2026Evaluating LLMs on Large-Scale Graph Property Estimation via Random Walks
  7. 2025Multi-Agent GraphRAG: A Text-to-Cypher Framework for Labeled Property Graphs
  8. 2024GraphInstruct: Empowering Large Language Models with Graph Understanding and Reasoning Capability