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Computing Label-Constraint Reachability in Graph Databases

机译:计算图形数据库中的标签约束可达性

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Our world today is generating huge amounts of graph data such as social networks, biological networks, and the semantic web. Many of these real-world graphs are edge-labeled graphs, i.e., each edge has a label that denotes the relationship between the two vertices connected by the edge. A fundamental research problem on these labeled graphs is how to handle the label-constraint reachability query: Can vertex u reach vertex v through a path whose edge labels are constrained by a set of labels'! In this work, we introduce a novel tree-based index framework which utilizes the directed maximal weighted spanning tree algorithm and sampling techniques to maximally compress the generalized transitive closure for the labeled graphs. An extensive experimental evaluation on both real and synthetic datasets demonstrates the efficiency of our approach in answering label-constraint reachability queries.
机译:我们今天的世界正在产生大量的图形数据,如社交网络,生物网络和语义网络。许多这些实际图形是边缘标记的图形,即,每个边缘都有一个标签,表示由边缘连接的两个顶点之间的关系。这些标记图上的基本研究问题是如何处理标签约束可达性查询:可以通过边缘标签被一组标签约束的路径到达Vertex v!在这项工作中,我们介绍了一种基于树的索引框架,它利用指向的最大加权生长树算法和采样技术来最大地压缩标记图的广义传递闭合。对实际和合成数据集的广泛实验评估展示了我们在回答标签约束可达性查询时的方法的效率。

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