Target Set Selection in Dense Graph Classes

TL;DR

This paper investigates the computational complexity of the Target Set Selection problem in dense graphs, providing fixed-parameter algorithms for specific parameters and proving hardness results for others.

Contribution

It introduces parameterized algorithms for Majority Target Set Selection based on neighborhood diversity and twin cover, and establishes hardness results for modular-width and unrestricted thresholds.

Findings

Algorithms for Majority Target Set Selection with neighborhood diversity and twin cover

Hardness proofs for modular-width parameterization

W[1]-hardness results for unrestricted thresholds

Abstract

In this paper, we study the Target Set Selection problem from a parameterized complexity perspective. Here for a given graph and a threshold for each vertex, the task is to find a set of vertices (called a target set) which activates the whole graph during the following iterative process. A vertex outside the active set becomes active if the number of so far activated vertices in its neighborhood is at least its threshold. We give two parameterized algorithms for a special case where each vertex has the threshold set to the half of its neighbors (the so-called Majority Target Set Selection problem) for parameterizations by the neighborhood diversity and the twin cover number of the input graph. We complement these results from the negative side. We give a hardness proof for the Majority Target Set Selection problem when parameterized by (a restriction of) the modular-width - a natural generalization of both previous structural parameters. We also show the Target Set Selection problem parameterized by the neighborhood diversity or by the twin cover number is W[1]-hard when there is no restriction on the thresholds.