Empowering Users in Graph Rule Mining via Large Language Models

2026-10-07

AI Summary

This study addresses the high technical barrier in graph rule mining, which conventionally demands specialized expertise in graph theory and query languages. To overcome this limitation, we propose a zero-code interactive framework leveraging large language models (LLMs). By employing prompt engineering to bridge user intent with property graph mining workflows, this work introduces a novel mechanism enabling LLMs to directly generate MINE GRAPH RULE queries, thereby facilitating the automated generation, optimization, and interpretation of complex relational rules. This research effectively translates expert-level graph mining capabilities into natural language interactions, substantially lowering the technical threshold for practitioners. Consequently, the proposed framework significantly enhances both the efficiency and accuracy of complex graph association rule mining for non-expert users, democratizing access to advanced graph analytics.

Abstract

In the era of interconnected data, graphs have emerged as an effective abstraction for modeling complex systems in an intuitive format, especially with the rise of Property Graphs, which offer an intuitive and scalable way of navigating non-intuitive structures. In this context, graph mining techniques have been developed for testing complex graph-based rules, as the MINE GRAPH RULE operator, which, however, require users to have prior expertise both in graph theory and formal query language. In this work, we propose to bridge the gap between users and the graph-association rule-mining process by showing how Large Language Models (LLMs) can be easily prompted to formulate, refine, and interpret complex relational rules, directly producing MINE GRAPH RULE queries.

Problem

• Graph Rule Mining
• Large Language Models
• Property Graphs
• User Empowerment

Innovation

• Large Language Models
• Graph Rule Mining
• Property Graphs
• Query Generation
• Association Rules