New algorithms for girth and cycle detection
Liam Roditty, Plia Trabelsi

TL;DR
This paper introduces a flexible randomized algorithm for detecting short cycles in graphs, generalizing previous methods by allowing real-valued parameters to optimize cycle length and runtime trade-offs.
Contribution
It extends existing cycle detection algorithms by replacing integer parameters with real-valued ones, providing greater flexibility and improved trade-offs, especially for sparse graphs.
Findings
Generalized cycle detection algorithm with real-valued parameters
Achieved better trade-offs for sparse graphs
Built upon and extended previous algorithms by Kadria et al.
Abstract
Let be an unweighted undirected graph with vertices and edges. Let be the girth of , that is, the length of a shortest cycle in . We present a randomized algorithm with a running time of that returns a cycle of length at most where is an integer and , for every graph with . Our algorithm generalizes an algorithm of Kadria \etal{} [SODA'22] that computes a cycle of length at most in time. Kadria \etal{} presented also an algorithm that finds…
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Taxonomy
TopicsStructural Health Monitoring Techniques
