SearcharxivSearch

arXiv · 2506.23215

Near-Optimal Vertex Fault-Tolerant Labels for Steiner Connectivity

Abstract

We present a compact labeling scheme for determining whether a designated set of terminals in a graph remains connected after any $f$ (or less) vertex failures occur. An $f$-FT Steiner connectivity labeling scheme for an $n$-vertex graph $G=(V,E)$ with terminal set $U \subseteq V$ provides labels to the vertices of $G$, such that given only the labels of any subset $F \subseteq V$ with $|F| \leq f$, one can determine if $U$ remains connected in $G-F$. The main complexity measure is the maximum label length. The special case $U=V$ of global connectivity has been recently studied by Jiang, Parter, and Petruschka, who provided labels of $n^{1-1/f} \cdot \mathrm{poly}(f,\log n)$ bits. This is near-optimal (up to $\mathrm{poly}(f,\log n)$ factors) by a lower bound of Long, Pettie and Saranurak. Our scheme achieves labels of $|U|^{1-1/f} \cdot \mathrm{poly}(f, \log n)$ for general $U \subseteq V$, which is near-optimal for any given size $|U|$ of the terminal set. To handle terminal sets, our approach differs from Jiang et al. We use a well-structured Steiner tree for $U$ produced by a decomposition theorem of Duan and Pettie, and bypass the need for Nagamochi-Ibaraki sparsification.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Koustav Bhanja, Asaf Petruschka. 2025-06-29. Near-Optimal Vertex Fault-Tolerant Labels for Steiner Connectivity. https://arxiv.org/abs/2506.23215

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Quasi-Monte Carlo Beyond Hardy-Krause II: $(1 + \varepsilon)n$ Samples Suffice

Numerical integration studies how well one can estimate the integral of a function $f$ over $[0,1)^d$ using $n$ sample points. The two classical methods, Monte Carlo (MC) and quasi-Monte Carlo (QMC), have complementary strengths and weaknesses, and a fundamental question is to design an approach that combines the benefits of both. Recently, building on the transference principle in discrepancy theory, Bansal and Jiang~\cite{BJ25a} gave a randomized QMC method that bridges MC and QMC guarantees using only i.i.d.\ samples. Their method also goes beyond the classical Koksma--Hlawka inequality: it achieves integration error $\widetilde{O}_d(\sigma_{\mathsf{SO}}(f)/n)$, where the smoothed-out variation $\sigma_{\mathsf{SO}}(f)$ can be substantially smaller than the Hardy--Krause variation that governs the classical bound. However, their algorithm requires $n^2$ i.i.d.\ samples as input, and this quadratic blowup is inherent to any method based on the transference principle. In this work, we bypass the quadratic blowup: for any constant $\varepsilon > 0$, we show that $(1+\varepsilon)n$ i.i.d.\ samples suffice to both obtain the beyond-Hardy--Krause guarantee of~\cite{BJ25a}, resolving an open problem posed there, and to produce low-discrepancy point sequences. Our algorithms are variants of the online Haar-thinning method of Dwivedi, Feldheim, Gurel-Gurevich, and Ramdas~\cite{DFG+19}.

cs.DS

Single-Exponential Algorithms and a Polynomial Kernel for Strong Connectivity Augmentation

Strong Connectivity Augmentation (SCA) asks whether a directed acyclic graph can be made strongly connected by adding at most $k$ prescribed links whose total weight is within a given budget. Klinkby, Misra, and Saurabh (SODA 2021) gave an $O^*(2^{O(k\log k)})$-time algorithm and asked whether the problem admits a single-exponential parameterized algorithm and a polynomial kernel. We answer both questions affirmatively: SCA can be solved in $O^*(9^k)$ time and admits a polynomial kernel with $O(k^4)$ vertices and $O(k^{16})$ bits. For unweighted SCA, we obtain $O^*(4^k)$ time and a kernel with $O(k^3)$ vertices. Our algorithms are based on a particularly simple reduction to Strongly Connected Spanning Subgraph with two edge costs.

cs.DS