Algorithms and complexity for geodetic sets on planar and chordal graphs

TL;DR

This paper investigates the computational complexity of the geodetic number problem on various graph classes, providing new algorithms for some subclasses and proving NP-hardness for others, advancing understanding of this problem in planar and chordal graphs.

Contribution

It introduces a linear-time algorithm for MGS on solid grids, shows NP-hardness on subcubic partial grids, and proves fixed parameter tractability for chordal graphs, while establishing NP-hardness on interval graphs.

Findings

Linear-time algorithm for MGS on solid grids

NP-hardness of MGS on subcubic partial grids

Fixed parameter tractability of MGS on chordal graphs

Abstract

We study the complexity of finding the \emph{geodetic number} on subclasses of planar graphs and chordal graphs. A set $S$ of vertices of a graph $G$ is a \emph{geodetic set} if every vertex of $G$ lies in a shortest path between some pair of vertices of $S$. The \textsc{Minimum Geodetic Set (MGS)} problem is to find a geodetic set with minimum cardinality of a given graph. The problem is known to remain NP-hard on bipartite graphs, chordal graphs, planar graphs and subcubic graphs. We first study \textsc{MGS} on restricted classes of planar graphs: we design a linear-time algorithm for \textsc{MGS} on solid grids, improving on a $3$-approximation algorithm by Chakraborty et al. (CALDAM, 2020) and show that it remains NP-hard even for subcubic partial grids of arbitrary girth. This unifies some results in the literature. We then turn our attention to chordal graphs, showing that \textsc{MGS} is fixed parameter tractable for inputs of this class when parameterized by its \emph{tree-width} (which equals its clique number). This implies a polynomial-time algorithm for $k$-trees, for fixed $k$. Then, we show that \textsc{MGS} is NP-hard on interval graphs, thereby answering a question of Ekim et al. (LATIN, 2012). As interval graphs are very constrained, to prove the latter result we design a rather sophisticated reduction technique to work around their inherent linear structure.