DeepAI

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

In this paper we consider the Target Set Selection problem. The problem naturally arises in many fields like economy, sociology, medicine. In the Target Set Selection problem one is given a graph G with a function thr: V(G) →N∪{0} and integers k, ℓ. The goal of the problem is to activate at most k vertices initially so that at the end of the activation process there is at least ℓ activated vertices. The activation process occurs in the following way: (i) once activated, a vertex stays activated forever; (ii) vertex v becomes activated if at least thr(v) of its neighbours are activated. The problem and its different special cases were extensively studied from approximation and parameterized points of view. For example, parameterizations by the following parameters were studied: treewidth, feedback vertex set, diameter, size of target set, vertex cover, cluster editing number and others. Despite the extensive study of the problem it is still unknown whether the problem can be solved in O^*((2-ϵ)^n) time for some ϵ >0. We partially answer this question by presenting several faster-than-trivial algorithms that work in cases of constant thresholds, constant dual thresholds or when the threshold value of each vertex is bounded by one-third of its degree. Also, we show that the problem parameterized by ℓ is W[1]-hard even when all thresholds are constant.

• 4 publications
• 9 publications
11/11/2021

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We study the problem of deciding reconfigurability of target sets of a g...
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Inspired by the famous Target Set Selection problem, we propose a new di...
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Let G = (V,E) be a graph on n vertices, where d_v denotes the degree of ...