SearcharxivSearch

arXiv · 2607.26936

The Parameterized Complexity of Problems on Outer k-Planar Graphs

Abstract

A graph is outer k-planar if it admits a straight-line drawing in which all vertices lie on a circle and every edge is crossed by at most k other edges. We study the parameterized complexity of a broad collection of graph problems on outer k-planar graphs, with k as the parameter. Many graph problems are known to be XALP-hard when parameterized by treewidth or outerplanarity, and XNLP-hard when parameterized by pathwidth. We show that only a few such problems, including Binary CSP and Scattered Set, remain intractable on outer k-planar graphs, whereas a large class of the others become fixed-parameter tractable in this setting, assuming that an outer k-planar drawing of the input graph is given. These include List Coloring, Capacitated Dominating Set, Capacitated Vertex Cover, Target Outdegree Orientation, and Target Set Selection, among others. In addition to the algorithmic and complexity results, we establish several structural results. We show that outer k-planar graphs have mim-width at most k+2, that graphs of cut-width at most k are outer 2k-planar, and that graphs of feedback edge set number at most k are outer 6k-planar. We also show that many graph parameters are incomparable with outer k-planarity, thereby clarifying its position within the graph parameter hierarchy.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Xiaobin Ren, Hans L. Bodlaender. 2026-07-29. The Parameterized Complexity of Problems on Outer k-Planar Graphs. https://arxiv.org/abs/2607.26936

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