Target Set Selection Parameterized by Clique-Width and Maximum Threshold

10/02/2017
by   Tim A. Hartmann, et al.
0

The Target Set Selection problem takes as an input a graph G and a non-negative integer threshold thr(v) for every vertex v. A vertex v can get active as soon as at least thr(v) of its neighbors have been activated. The objective is to select a smallest possible initial set of vertices, the target set, whose activation eventually leads to the activation of all vertices in the graph. We show that Target Set Selection is in FPT when parameterized with the combined parameters clique-width of the graph and the maximum threshold value. This generalizes all previous FPT-membership results for the parameterization by maximum threshold, and thereby solves an open question from the literature. We stress that the time complexity of our algorithm is surprisingly well-behaved and grows only single-exponentially in the parameters.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
03/10/2020

Maximizing Happiness in Graphs of Bounded Clique-Width

Clique-width is one of the most important parameters that describes stru...
research
07/10/2020

Target set selection with maximum activation time

A target set selection model is a graph G with a threshold function τ:V→...
research
07/14/2022

Improved Parameterized Complexity of Happy Set Problems

We present fixed-parameter tractable (FPT) algorithms for two problems, ...
research
07/27/2018

Solving Target Set Selection with Bounded Thresholds Faster than 2^n

In this paper we consider the Target Set Selection problem. The problem ...
research
01/31/2022

XNLP-completeness for Parameterized Problems on Graphs with a Linear Structure

In this paper, we show several parameterized problems to be complete for...
research
05/25/2018

On some tractable and hard instances for partial incentives and target set selection

A widely studied model for influence diffusion in social networks are t...
research
05/29/2018

Rank Based Approach on Graphs with Structured Neighborhood

In this paper, we combine the rank-based approach and the neighbor-equiv...

Please sign up or login with your details

Forgot password? Click here to reset