具有层次继承关系的信息网络上的可扩展Top-k查询
Scalable Top-k Query on Information Networks with Hierarchical Inheritance Relations
摘要：
在大规模异构信息网络中，图查询、模式挖掘和知识发现成为一个具有挑战性的问题。现有的路径传播技术主要集中在节点标签和邻域结构的推断上。然而，现实世界中的实体链接也包含着丰富的层次继承关系。例如，产品版本的漏洞可能是从其旧版本继承而来的。利用层次继承可以潜在地提高查询结果的质量。基于此，我们探讨了实体之间的层次继承关系，并提出了层次继承关系的HIN上的图查询问题。我们提出了一个图查询搜索算法，通过分解原始查询图成多个星查询，并适用于每个星查询算法。然后，为原始查询的最终前k个查询答案构建来自每个星查询结果的进一步候选。为了有效地从大规模HIN中获得图查询结果，我们设计了一种基于边界的剪枝技术，通过使用均匀代价搜索来修剪搜索空间。我们在GraphX中实现了我们的算法，以测试合成和真实世界数据集的有效性和效率。与两种常用的图查询算法相比，该算法能有效地获得更准确的查询结果，具有较强的竞争力。
英文摘要：
Graph query, pattern mining and knowledge discovery become challenging on large-scale heterogeneous information networks (HINs). State-of-the-art techniques involving path propagation mainly focus on the inference on nodes labels and neighborhood structures. However, entity links in the real world also contain rich hierarchical inheritance relations. For example, the vulnerability of a product version is likely to be inherited from its older versions. Taking advantage of the hierarchical inheritances can potentially improve the quality of query results. Motivated by this, we explore hierarchical inheritance relations between entities and formulate the problem of graph query on HINs with hierarchical inheritance relations. We propose a graph query search algorithm by decomposing the original query graph into multiple star queries and apply a star query algorithm to each star query. Further candidates from each star query result are then constructed for final top-k query answers to the original query. To efficiently obtain the graph query result from a large-scale HIN, we design a bound-based pruning technique by using uniform cost search to prune search spaces. We implement our algorithm in GraphX to test the effectiveness and efficiency on synthetic and real-world datasets. Compared with two common graph query algorithms, our algorithm can effectively obtain more accurate results and competitive performances.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
