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.

翻译：图结构在各领域具有基础性地位，但缺乏深厚专业知识则难以有效使用。大语言模型（LLM）为实现自然语言图分析提供了可能，然而当前方法无法高效处理工业级属性图：此类数据集规模庞大、高度异构、结构复杂且动态演化。为解决该问题，我们设计了一种面向复杂多查询图分析的新型抽象框架。其核心思想是通过基于语义目录的规划机制，替代传统直接从自然语言生成图查询的脆弱方法，该目录同时描述图模式与图操作。具体而言，该框架构建了语义平面与执行平面的清晰分离：语义平面负责LLM的规划与广义推理，执行平面则对完整数据集及工具实现进行确定性的数据库级查询执行。该设计即使在小上下文LLM中也能显著提升令牌效率与任务有效性。我们基于此抽象框架开发了首个LLM增强的图分析系统GraphSeek。该系统实现了显著更高的任务成功率（例如较增强版LangChain提升至86%），并为新一代经济可用的图分析技术指明了方向——通过将LLM推理能力与大规模复杂属性图的数据库级执行相融合。