SUFF: Accelerating Subgraph Matching with Historical Data

Xun Jian, Zhiyuan Li, Lei Chen
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引用次数: 1

Abstract

Subgraph matching is a fundamental problem in graph theory and has wide applications in areas like sociology, chemistry, and social networks. Due to its NP-hardness, the basic approach is a brute-force search over the whole search space. Some pruning strategies have been proposed to reduce the search space. However, they are either space-inefficient or based on assumptions that the graph has specific properties. In this paper, we propose SUFF, a general and powerful structure filtering framework, which can accelerate most of the existing approaches with slight modifications. Specifically, it builds a set of filters using matching results of past queries, and uses them to prune the search space for future queries. By fully utilizing the relationship between matches of two queries, it ensures that such pruning is sound. Furthermore, several optimizations are proposed to reduce the computation and space cost for building, storing, and using filters. Extensive experiments are conducted on multiple real-world data sets and representative existing approaches. The results show that SUFF can achieve up to 15X speedup with small overheads.
加速子图与历史数据的匹配
子图匹配是图论中的一个基本问题,在社会学、化学和社会网络等领域有着广泛的应用。由于其np -硬度,基本方法是在整个搜索空间内进行蛮力搜索。为了减少搜索空间,提出了一些修剪策略。然而,它们要么是空间低效的,要么是基于图具有特定属性的假设。在本文中,我们提出了一个通用的、强大的结构滤波框架SUFF,它可以在稍加修改的情况下加速大多数现有的方法。具体来说,它使用过去查询的匹配结果构建一组过滤器,并使用它们为未来的查询修剪搜索空间。通过充分利用两个查询的匹配之间的关系,它确保了这种修剪是合理的。此外,还提出了一些优化方法,以减少构建、存储和使用过滤器的计算和空间成本。在多个真实世界数据集和具有代表性的现有方法上进行了广泛的实验。结果表明,SUFF可以以较小的开销实现高达15倍的加速。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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