MINE GRAPH RULE: A New Cypher-like Operator for Mining Association Rules on Property Graphs

2024-06-27

arXiv.org

AI Summary

Existing graph mining methods primarily focus on topological subgraph discovery and lack a unified mechanism for jointly modeling syntactic and semantic aspects of association rules over attributed graphs. This paper proposes the MINE GRAPH RULE operator, the first to enable integrated syntactic–semantic expression of graph association rules in an attributed graph database—implemented as an extension to Neo4j. Syntactically, conditions are specified via Cypher-like queries; semantically, rule quality is evaluated using support and confidence metrics. The operator tightly couples graph structure with attribute semantics, leverages Neo4j’s native query optimization, and incorporates relational association rule pruning strategies to ensure efficiency and portability. Experiments demonstrate strong scalability across multidimensional parameters. An open-source plugin implementing the operator significantly enhances both the expressiveness and practical utility of graph association rule mining.

Technology Category

• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community
• Machine Learning: Graph-based Machine Learning
• Reasoning under Uncertainty: Graphical Models

Application Category

• Graph Algorithms and Modeling for the Web: Querying, indexing, and retrieval in Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Web Mining and Content Analysis: Sentiment analysis and opinion mining

Abstract

Mining information from graph databases is becoming overly important. To approach this problem, current methods focus on identifying subgraphs with specific topologies; as of today, no work has been focused on expressing jointly the syntax and semantics of mining operations over rich property graphs. We define MINE GRAPH RULE, a new operator for mining association rules from graph databases, by extending classical approaches used in relational databases and exploited by recommending systems. We describe the syntax and semantics of the operator, which is based on measuring the support and confidence of each rule, and then we provide several examples of increasing complexity on top of a realistic example; our operator embeds Cypher for expressing the mining conditions. MINE GRAPH RULE is implemented on top of Neo4j, the most successful graph database system; it takes advantage of built-in optimizations of the Neo4j engine, as well as optimizations that are defined in the context of relational association rules. Our implementation is available as a portable Neo4j plugin. At the end of our paper, we show the execution performance in a variety of settings, by varying the operators, the size of the graph, the ratio between node types, the method for creating relationships, and maximum support and confidence.

Problem

• Defining MINE GRAPH RULE operator for property graph mining
• Expressing syntax and semantics for graph association rules
• Implementing and evaluating operator on Neo4j with real-world data

Innovation

• Introduces MINE GRAPH RULE operator
• Defines syntax and semantics for rules
• Implements Neo4j plugin for real-world use