Algorithms and complexity for monitoring edge-geodetic sets in graphs

TL;DR

This paper investigates the computational complexity of monitoring edge-geodetic sets in graphs, establishing hardness results, algorithmic solutions for specific graph classes, and approximation algorithms for the general problem.

Contribution

It proves NP-hardness and intractability results for the problem, and provides polynomial-time, fixed-parameter tractable, and approximation algorithms for various graph classes.

Findings

NP-hardness for 2-apex 3-degenerate graphs

No subexponential algorithm under ETH for 3-degenerate graphs

Polynomial-time algorithm for interval graphs

Abstract

A monitoring edge-geodetic set of a graph is a subset $M$ of its vertices such that for every edge $e$ in the graph, deleting $e$ increases the distance between at least one pair of vertices in $M$. We study the following computational problem \textsc{MEG-set}: given a graph $G$ and an integer $k$, decide whether $G$ has a monitoring edge geodetic set of size at most $k$. We prove that the problem is NP-hard even for 2-apex 3-degenerate graphs, improving a result by Haslegrave (Discrete Applied Mathematics 2023). Additionally, we prove that the problem cannot be solved in subexponential-time, assuming the Exponential-Time Hypothesis, even for 3-degenerate graphs. Further, we prove that the optimization version of the problem is APX-hard, even for 4-degenerate graphs. Complementing these hardness results, we prove that the problem admits a polynomial-time algorithm for interval graphs, a fixed-parameter tractable algorithm for general graphs with clique-width plus diameter as the parameter, and a fixed-parameter tractable algorithm for chordal graphs with treewidth as the parameter. We also provide an approximation algorithm with factor $\ln m\cdot OPT$ and $\sqrt{n\ln m}$ for the optimization version of the problem, where $m$ is the number of edges, $n$ the number of vertices, and $OPT$ is the size of a minimum monitoring edge-geodetic set of the input graph.