提出图模式关联规则，用于评估图数据的合理性与扩展性
Graph Pattern-based Association Rules Evaluated Under No-repeated-anything Semantics in the Graph Transactional Setting
- 基于无重复任何元素的语义，对图模式进行概率评估
- 定义置信度、提升度等指标，支持图结构的生成与判断
- 适用于知识图谱等复杂图数据的分析与推理
我们为有向带标签多重图（如RDF图）提出了基于图模式的关联规则（GPARs），支持生成任务（扩展图）和评估任务（判断图的合理性）。该框架超越了图函数依赖、图实体依赖、关系关联规则、图关联规则、多关系与路径关联规则及霍恩规则等已有形式化方法。在给定一组图的情况下，采用“无重复任何元素”的语义来评估图模式，更有效地利用图的拓扑结构。我们构建了一个概率空间，推导出置信度、提升度、杠杆率和信念度等指标。进一步分析这些指标与经典项集基对应物的关系，并识别出其特性得以保留的条件。
原文摘要 · Abstract (English)
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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