Tighter bounds for weighted and unweighted shortest cycle approximation

2026-07-01

AI Summary

This work addresses the problem of approximating the shortest cycle (girth) in both weighted and unweighted graphs. For graphs with non-negative real edge weights, it proposes an algorithm achieving a $2k/3$-approximation in $\widetilde{O}(m + n^{1+2/k})$ time, thereby extending the best-known time–approximation trade-offs from unweighted graphs to weighted graphs for all integers $k \geq 2$. Additionally, the paper establishes new conditional lower bounds for the unweighted case through fine-grained complexity analysis. The approach integrates graph traversal, distance estimation, and a hybrid sparse–dense strategy within a unified framework, improving upon existing results and strengthening theoretical limits for girth approximation.

Abstract

We study the problem of approximating the length of a shortest cycle in a given graph, known as the girth of the graph. The state-of-the-art approximation algorithms for unweighted graphs by Kadria et al. [SODA'22] and Roditty and Trabelsi [arXiv'25] achieve the following trade-off: for every integer $k\geq 2$, there is an $\tilde{O}(n^{1+2/k})$ time algorithm that achieves a $(2k/3)$-approximation for the girth in unweighted $n$-node graphs. The first result of this paper is to achieve the same trade-off for $m$-edge, $n$-node graphs with non-negative real edge weights: a $2k/3$-approximation algorithm running in $\tilde{O}(m+n^{1+2/k})$ time. The dependence on $m$ is unavoidable in weighted graphs. Our result improves on the work of Kadria et al.~[SODA'23] and Ducoffe [ICALP'19 and SIDMA'21], who were only able to achieve such a trade-off for some values of $k$. We also prove new fine-grained lower bounds for girth approximation and related problems in unweighted graphs.

Problem

• girth
• shortest cycle
• approximation
• weighted graphs
• unweighted graphs

Innovation

• girth approximation
• weighted graphs
• fine-grained lower bounds
• shortest cycle
• graph algorithms