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Efficient Association Discovery with Keyword-based Constraints on Large Graph Data

机译:大图数据上基于关键字的约束的高效关联发现

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In many domains, such as social networks and chem-informatics. data can be represented naturally in graph model, with nodes being data entries and edges the relationships between them. We study the application requirements in these domains and find that discovering Constrained Acyclic Paths (CAP) is highly in demand. In this paper, we define the CAP search problem and introduce a set of quantitative metrics for describing keyword-based constraints. We propose a series of algorithms to efficiently evaluate CAP queries on large-scale graph data. Extensive experiments illustrate that our algorithms are both efficient and scalable.
机译:在许多领域,例如社交网络和化学信息学。数据可以自然地用图模型表示,节点是数据条目,并且使它们之间的关系更趋边缘化。我们研究了这些领域中的应用需求,发现发现有约束的非循环路径(CAP)的需求很高。在本文中,我们定义了CAP搜索问题,并引入了一组定量指标来描述基于关键字的约束。我们提出了一系列算法,可以有效地评估大规模图形数据上的CAP查询。大量实验表明,我们的算法既高效又可扩展。

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