In recent years, the increased interest in application areas such as social networks has resulted in a rising popularity of graph-based approaches for storing and processing large amounts of interconnected data. To extract useful information from the growing network structures, efficient querying techniques are required.

In this paper, we propose an approach for graph pattern matching that allows a uniform handling of arbitrary constraints over the query vertices. Our technique builds on a previously introduced matching algorithm, which takes concrete host graph information into account to dynamically adapt the employed search plan during query execution. The dynamic algorithm is combined with an existing static approach for search plan generation, resulting in a hybrid technique which we further extend by a more sophisticated handling of filtering effects caused by constraint checks. We evaluate the presented concepts empirically based on an implementation for our graph pattern matching tool, the Story Diagram Interpreter, with queries and data provided by the LDBC Social Network Benchmark. Our results suggest that the hybrid technique may improve search efficiency in several cases, and rarely reduces efficiency.

近年来，人们对诸如社交网络之类的应用程序领域的兴趣不断增长，导致基于图的方法用于存储和处理大量互连数据的情况日​​益普及。为了从不断增长的网络结构中提取有用的信息，需要有效的查询技术。

在本文中，我们提出了一种图形模式匹配方法，该方法允许对查询顶点上的任意约束进行统一处理。我们的技术基于先前引入的匹配算法，该算法将具体的宿主图信息考虑在内，以在查询执行过程中动态调整所采用的搜索计划。动态算法与现有的静态方法相结合，用于生成搜索计划，从而产生了一种混合技术，通过对约束检查所导致的过滤效果的更复杂处理，我们进一步扩展了这种混合技术。我们基于LDBC社交网络基准测试提供的查询和数据，基于我们的图形模式匹配工具（故事图解释器）的实现，对所提出的概念进行经验评估。