O’Reach: Even Faster Reachability in Large Graphs

Q2 Mathematics Journal of Experimental Algorithmics Pub Date : 2022-08-17 DOI:10.1145/3556540
Kathrin Hanauer, Christian Schulz, Jonathan Trummer
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引用次数: 1

Abstract

One of the most fundamental problems in computer science is the reachability problem: Given a directed graph and two vertices s and t, can sreach t via a path? We revisit existing techniques and combine them with new approaches to support a large portion of reachability queries in constant time using a linear-sized reachability index. Our new algorithm O’Reach can be easily combined with previously developed solutions for the problem or run standalone. In a detailed experimental study, we compare a variety of algorithms with respect to their index-building and query times as well as their memory footprint on a diverse set of instances. Our experiments indicate that the query performance often depends strongly not only on the type of graph but also on the result, i.e., reachable or unreachable. Furthermore, we show that previous algorithms are significantly sped up when combined with our new approach in almost all scenarios. Surprisingly, due to cache effects, a higher investment in space doesn’t necessarily pay off: Reachability queries can often be answered even faster than single memory accesses in a precomputed full reachability matrix.
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O 'Reach:在大图形中更快的可达性
计算机科学中最基本的问题之一是可达性问题:给定一个有向图和两个顶点s和t,是否可以通过路径访问t?我们重新审视现有技术,并将其与新方法相结合,使用线性大小的可达性索引在恒定时间内支持大部分可达性查询。我们的新算法O'Reach可以很容易地与以前开发的问题解决方案相结合,也可以单独运行。在一项详细的实验研究中,我们比较了各种算法的索引构建和查询时间,以及它们在不同实例集上的内存占用。我们的实验表明,查询性能通常不仅与图的类型密切相关,还与结果密切相关,即可达或不可达。此外,我们还表明,在几乎所有场景中,当与我们的新方法相结合时,以前的算法都会显著加快。令人惊讶的是,由于缓存效应,对空间的更高投资并不一定会得到回报:在预先计算的完全可达性矩阵中,可达性查询的回答速度通常甚至比单个内存访问更快。
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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来源期刊
Journal of Experimental Algorithmics
Journal of Experimental Algorithmics Mathematics-Theoretical Computer Science
CiteScore
3.10
自引率
0.00%
发文量
29
期刊介绍: The ACM JEA is a high-quality, refereed, archival journal devoted to the study of discrete algorithms and data structures through a combination of experimentation and classical analysis and design techniques. It focuses on the following areas in algorithms and data structures: ■combinatorial optimization ■computational biology ■computational geometry ■graph manipulation ■graphics ■heuristics ■network design ■parallel processing ■routing and scheduling ■searching and sorting ■VLSI design
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