Graph Pattern-based Association Rules Evaluated Under No-repeated-anything Semantics in the Graph Transactional Setting

• URL: http://arxiv.org/abs/2512.15308v1
• Date: Wed, 17 Dec 2025 10:52:15 GMT
• Title: Graph Pattern-based Association Rules Evaluated Under No-repeated-anything Semantics in the Graph Transactional Setting
• Authors: Basil Ell,
• Abstract summary: We introduce graph pattern-based association rules (GPARs) for directed labeled multigraphs such as RDF graphs.<n>GPARs support both generative tasks, where a graph is extended, and evaluative tasks, where the plausibility of a graph is assessed.
• Score: 0.6599344783327054
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We introduce graph pattern-based association rules (GPARs) for directed labeled multigraphs such as RDF graphs. GPARs support both generative tasks, where a graph is extended, and evaluative tasks, where the plausibility of a graph is assessed. The framework goes beyond related formalisms such as graph functional dependencies, graph entity dependencies, relational association rules, graph association rules, multi-relation and path association rules, and Horn rules. Given a collection of graphs, we evaluate graph patterns under no-repeated-anything semantics, which allows the topology of a graph to be taken into account more effectively. We define a probability space and derive confidence, lift, leverage, and conviction in a probabilistic setting. We further analyze how these metrics relate to their classical itemset-based counterparts and identify conditions under which their characteristic properties are preserved.

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