RAQ：大型网络中的知识感知图查询
RAQ: Relationship-Aware Graph Querying in Large Networks
摘要：
从各种各样的现实世界的应用程序的图形数据的显着增长，使图形查询是一个至关重要的问题。传统技术使用结构以及节点相似性来在（大）目标图中找到给定查询图的匹配。然而，几乎所有现有的技术都默认忽略了图中关系的存在，这些关系通常通过节点和边标签之间的交互来编码。在本文中，我们提出了RAQ -知识感知图查询，以弥补这一差距。给定查询图，RAQ识别目标图的$k$最佳匹配子图，该子图编码与查询图中相似的关系。为了评估RAQ作为知识发现和探索任务的图查询范例的实用性，我们在互联网电影数据库（IMDb）上进行了一项用户调查，在170名接受调查的用户中，有86%的人更喜欢关系感知匹配。执行子图同构的需要使得RAQ NP困难。通过波束叠加搜索使查询变得实际。在多个真实世界图数据集上的大量实验表明，RAQ是有效的，高效的，可扩展的。
英文摘要：
The phenomenal growth of graph data from a wide variety of real-world applications has rendered graph querying to be a problem of paramount importance. Traditional techniques use structural as well as node similarities to find matches of a given query graph in a (large) target graph. However, almost all existing techniques have tacitly ignored the presence of relationships in graphs, which are usually encoded through interactions between node and edge labels. In this paper, we propose RAQ -- Relationship-Aware Graph Querying, to mitigate this gap. Given a query graph, RAQ identifies the $k$ best matching subgraphs of the target graph that encode similar relationships as in the query graph. To assess the utility of RAQ as a graph querying paradigm for knowledge discovery and exploration tasks, we perform a user survey on the Internet Movie Database (IMDb), where an overwhelming 86% of the 170 surveyed users preferred the relationship-aware match over traditional graph querying. The need to perform subgraph isomorphism renders RAQ NP-hard. The querying is made practical through beam stack search. Extensive experiments on multiple real-world graph datasets demonstrate RAQ to be effective, efficient, and scalable.
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。
