Kernelization for Finding Lineal Topologies (Depth-First Spanning Trees) with Many or Few Leaves

TL;DR

This paper develops polynomial kernelization algorithms for the problems of finding depth-first spanning trees with many or few leaves in graphs, improving the efficiency of fixed-parameter algorithms for these problems.

Contribution

It proves that the extsc{Min-LLT} and extsc{Max-LLT} problems admit polynomial kernels of size extit{O}(k^3), enabling more practical fixed-parameter algorithms.

Findings

Polynomial kernels of size O(k^3) for both problems.

FPT algorithms with runtime k^{O(k)}·n^{O(1)}.

Kernelization based on a vertex cover structure.

Abstract

For a given graph $G$, a depth-first search (DFS) tree $T$ of $G$ is an $r$-rooted spanning tree such that every edge of $G$ is either an edge of $T$ or is between a \textit{descendant} and an \textit{ancestor} in $T$. A graph $G$ together with a DFS tree is called a \textit{lineal topology} $\mathcal{T} = (G, r, T)$. Sam et al. (2023) initiated study of the parameterized complexity of the \textsc{Min-LLT} and \textsc{Max-LLT} problems which ask, given a graph $G$ and an integer $k\geq 0$, whether $G$ has a DFS tree with at most $k$ and at least $k$ leaves, respectively. Particularly, they showed that for the dual parameterization, where the tasks are to find DFS trees with at least $n-k$ and at most $n-k$ leaves, respectively, these problems are fixed-parameter tractable when parameterized by $k$. However, the proofs were based on Courcelle's theorem, thereby making the running times a tower of exponentials. We prove that both problems admit polynomial kernels with $\Oh(k^3)$ vertices. In particular, this implies FPT algorithms running in $k^{\Oh(k)}\cdot n^{O(1)}$ time. We achieve these results by making use of a $\Oh(k)$-sized vertex cover structure associated with each problem. This also allows us to demonstrate polynomial kernels for \textsc{Min-LLT} and \textsc{Max-LLT} for the structural parameterization by the vertex cover number.