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Title: (Sub)linear kernels for edge modification problems toward structured graph classes
Authors: Bathie, G
Bousquet, N
Cao, Y 
Ke, Y 
Pierron, T
Issue Date: Nov-2022
Source: Algorithmica, Nov. 2022, v. 84, p. 3338-3364
Abstract: In a (parameterized) graph edge modification problem, we are given a graph G, an integer k and a (usually well-structured) class G of graphs, and asked whether it is possible to transform G into a graph G′∈G by adding and/or removing at most k edges. Parameterized graph edge modification problems received considerable attention in the last decades. In this paper, we focus on finding small kernels for edge modification problems. One of the most studied problems is the CLUSTER EDITING problem, in which the goal is to partition the vertex set into a disjoint union of cliques. Even if a 2k-vertex kernel exists for CLUSTER EDITING, this kernel does not reduce the size of the instance in most cases. Therefore, we explore the question of whether linear kernels are a theoretical limit in edge modification problems, in particular when the target graph class is very structured (such as a partition into cliques for instance). We prove, as far as we know, the first sublinear kernel for an edge modification problem. Namely, we show that CLIQUE + INDEPENDENT SET DELETION, which is a restriction of CLUSTER DELETION, admits a kernel of size O(k/logk) . We also obtain small kernels for several other edge modification problems. We first show that CLUSTER DELETION admits a 2k-vertex kernel as CLUSTER EDITING, improving the previous 4k-vertex kernel. We prove that (PSEUDO-)SPLIT COMPLETION (and the equivalent (PSEUDO-)SPLIT DELETION) admits a linear kernel, improving the existing quadratic kernel. We also prove that TRIVIALLY PERFECT COMPLETION admits a quadratic kernel (improving the cubic kernel), and finally prove that its triangle-free version (STARFOREST DELETION) admits a linear kernel, which is optimal under the Exponential Time Hypothesis.
Keywords: Kernelization
Graph editing
Split graphs
(Sub)linear kernels
Publisher: Springer
Journal: Algorithmica 
ISSN: 0178-4617
EISSN: 1432-0541
DOI: 10.1007/s00453-022-00969-1
Rights: © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2022
This version of the article has been accepted for publication, after peer review (when applicable) and is subject to Springer Nature’s AM terms of use(https://www.springernature.com/gp/open-research/policies/accepted-manuscript-terms), but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: http://dx.doi.org/10.1007/s00453-022-00969-1.
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