2026-07-26T00:38:24Z https://drops.dagstuhl.de/oai

oai:drops-oai.dagstuhl.de:13774 2024-03-06T10:52:55Z ddc:004 open_access

Parallel Five-Cycle Counting Algorithms Huang, Louisa Ruixue Shi, Jessica Shun, Julian Cycle counting parallel algorithms graph algorithms Counting the frequency of subgraphs in large networks is a classic research question that reveals the underlying substructures of these networks for important applications. However, subgraph counting is a challenging problem, even for subgraph sizes as small as five, due to the combinatorial explosion in the number of possible occurrences. This paper focuses on the five-cycle, which is an important special case of five-vertex subgraph counting and one of the most difficult to count efficiently. We design two new parallel five-cycle counting algorithms and prove that they are work-efficient and achieve polylogarithmic span. Both algorithms are based on computing low out-degree orientations, which enables the efficient computation of directed two-paths and three-paths, and the algorithms differ in the ways in which they use this orientation to eliminate double-counting. We develop fast multicore implementations of the algorithms and propose a work scheduling optimization to improve their performance. Our experiments on a variety of real-world graphs using a 36-core machine with two-way hyper-threading show that our algorithms achieves 10-46x self-relative speed-up, outperform our serial benchmarks by 10-32x, and outperform the previous state-of-the-art serial algorithm by up to 818x. Schloss Dagstuhl – Leibniz-Zentrum für Informatik Louisa Ruixue Huang and Jessica Shi and Julian Shun 2021 Is Part Of LIPIcs, Volume 190, 19th International Symposium on Experimental Algorithms (SEA 2021) InProceedings Text doc-type:ResearchArticle publishedVersion application/pdf doi:10.4230/LIPIcs.SEA.2021.2 urn:nbn:de:0030-drops-137749 https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.SEA.2021.2 eng https://creativecommons.org/licenses/by/4.0/legalcode