Algorithmic trade-offs for girth approximation in undirected graphs

Avi Kadria, Liam Roditty, Aaron Sidford, Virginia Vassilevska Williams, Uri Zwick

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

4 Scopus citations

Abstract

We present several new effcient algorithms for approximating the girth, g, of weighted and unweighted n-vertex, m-edge undirected graphs. For undirected graphs with polynomially bounded, integer, non-negative edge weights, we provide an algorithm that for every integer k 1, runs in eO(m + n1+1=k log g) time and returns a cycle of length at most 2kg. For unweighted, undirected graphs we present an algorithm that for every k 1, runs in eO (n1+1=k) time and returns a cycle of length at most 2kdg=2e, an almost k-approximation. Both algorithms provide trade-off-s between the running time and the quality of the approximation. We also obtain faster algorithms for approximation factors better than 2, and improved approximations when the girth is odd or small (e.g., 3 and 4).

Original languageEnglish
Title of host publicationACM-SIAM Symposium on Discrete Algorithms, SODA 2022
PublisherAssociation for Computing Machinery
Pages1471-1492
Number of pages22
ISBN (Electronic)9781611977073
StatePublished - 2022
Event33rd Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2022 - Alexander, United States
Duration: 9 Jan 202212 Jan 2022

Publication series

NameProceedings of the Annual ACM-SIAM Symposium on Discrete Algorithms
Volume2022-January

Conference

Conference33rd Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2022
Country/TerritoryUnited States
CityAlexander
Period9/01/2212/01/22

Funding

FundersFunder number
National Science FoundationCCF-1955039, CCF-1844855
Microsoft Research
Google
Massachusetts Institute of TechnologyCCF-1909429, CCF-2129139, 1651838
Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology
United States-Israel Binational Science Foundation2016365, 2020356

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