Hop-constrained s-t simple path (HC-s-t path) enumeration is a fundamental problem in graph analysis. Existing solutions for this problem focus on optimizing the processing performance of a single query. However, in practice, it is more often that multiple HC-s-t path queries are issued simultaneously and processed as a batch. Therefore, we study the problem of batch HC-s-t path query processing in this paper and aim to compute the results of all queries concurrently and efficiently as a batch. To achieve this goal, we first propose the concept of HC-s path query which can precisely characterize the common computation among different queries.We then devise a two-phase HC-s path query detection algorithm to identify the common HC-s path queries for the given HC-s-t path queries. Based on the detected HC-s path queries, we further devise an efficient HC-s-t path enumeration algorithm in which the common computation represented by HC-s path queries are effectively shared. We conduct extensive experiments on real-world graphs and the experimental results demonstrate that our proposed algorithm is efficient and scalable regarding processing multiple HC-s-t path queries in large graphs at billion-scale.

翻译：跳约束s-t简单路径（HC-s-t路径）枚举是图分析中的基础问题。现有解决方案主要关注单条查询的处理性能优化。然而在实践中，更常见的情况是同时发出多条HC-s-t路径查询并作为批量处理。为此，本文研究批量HC-s-t路径查询处理问题，旨在以批量方式高效并发计算所有查询结果。为实现该目标，我们首先提出HC-s路径查询概念，该概念能精确刻画不同查询间的共同计算。随后设计两阶段HC-s路径查询检测算法，用于识别给定HC-s-t路径查询中的共同HC-s路径查询。基于检测到的HC-s路径查询，进一步设计高效的HC-s-t路径枚举算法，该算法可有效共享由HC-s路径查询表征的共同计算。我们在真实世界图上开展大量实验，实验结果表明所提算法在十亿级规模大图上处理多条HC-s-t路径查询时具有高效性和可扩展性。