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  5. On structural parameterizations of firefighting
 
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On structural parameterizations of firefighting

Date Issued
2017-11-01
Abstract
The Firefighting problem is defined as follows. At time t=0, a fire breaks out at a vertex of a graph. At each time step t?0, a firefighter permanently defends (protects) an unburned vertex, and the fire then spread to all undefended neighbors from the vertices on fire. This process stops when the fire cannot spread anymore. The goal is to find a sequence of vertices for the firefighter that maximizes the number of saved (non burned) vertices. The Firefighting problem turns out to be NP-hard even when restricted to bipartite graphs or trees of maximum degree three. We study the parameterized complexity of the Firefighting problem for various structural parameterizations. All our parameters measure the distance to a graph class (in terms of vertex deletion) on which the firefighting problem admits a polynomial time algorithm. Specifically, for a graph class F and a graph G, a vertex subset S is called a modulator to F if G?S belongs to F. The parameters we consider are the sizes of modulators to graph classes such as threshold graphs, bounded diameter graphs, disjoint unions of stars, and split graphs. To begin with, we show that the problem is W[1]-hard when parameterized by the size of a modulator to diameter at most two graphs and split graphs. In contrast to the above intractability results, we show that Firefighting is fixed parameter tractable (FPT) when parameterized by the size of a modulator to threshold graphs and disjoint unions of stars, which are subclasses of diameter at most two graphs. We further investigate the kernelization complexity of these problems to find that firefighting admits a polynomial kernel when parameterized by the size of a modulator to a clique, while it is unlikely to admit a polynomial kernel when parameterized by the size of a modulator to a disjoint union of stars.
URI
http://arxiv.org/abs/1711.10227
https://d8.irins.org/handle/IITG2025/19744
Subjects
Data Structures and Algorithms
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