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Split Vertex Deletion meets Vertex Cover: New fixed-parameter and exact exponential-time algorithms

Authors: Marek Cygan; Marcin Pilipczuk;

Split Vertex Deletion meets Vertex Cover: New fixed-parameter and exact exponential-time algorithms

Abstract

In the Split Vertex Deletion problem, given a graph G and an integer k, we ask whether one can delete k vertices from the graph G to obtain a split graph (i.e., a graph, whose vertex set can be partitioned into two sets: one inducing a clique and the second one inducing an independent set). In this paper we study exact (exponential-time) and fixed-parameter algorithms for Split Vertex Deletion.*We show that, up to a factor quasipolynomial in k and polynomial in n, the Split Vertex Deletion problem can be solved in the same time as the well-studied Vertex Cover problem. By plugging in the currently best fixed-parameter algorithm for Vertex Cover due to Chen et al. [Theor. Comput. Sci. 411 (40-42) (2010) 3736-3756], we obtain an algorithm that solves Split Vertex Deletion in time O(1.2738^kk^O^(^l^o^g^k^)+n^3). *We show that all maximal induced split subgraphs of a given n-vertex graph can be listed in O(3^n^/^3n^O^(^l^o^g^n^)) time. To achieve our goals, we prove the following structural result that may be of independent interest: for any graph G we may compute a family P of size n^O^(^l^o^g^n^) containing partitions of V(G) into two parts, such that for any two disjoint sets X"C,X"I@?V(G) where G[X"C] is a clique and G[X"I] is an independent set, there is a partition in P which contains all vertices of X"C on one side and all vertices of X"I on the other. Moreover, the family P can be enumerated in O(n^O^(^l^o^g^n^)) time.

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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
16
Average
Top 10%
Average
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