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Theoretical Computer Science
Article . 2019 . Peer-reviewed
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https://dx.doi.org/10.48550/ar...
Article . 2018
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Tractability of König edge deletion problems

Authors: Diptapriyo Majumdar; Rian Neogi; Venkatesh Raman; S. Vaishali;

Tractability of König edge deletion problems

Abstract

A graph is said to be a Konig graph if the size of its maximum matching is equal to the size of its minimum vertex cover. The Konig Edge Deletion problem asks if in a given graph there exists a set of at most k edges whose deletion results in a Konig graph. While the vertex version of the problem (Konig vertex deletion) has been shown to be fixed-parameter tractable more than a decade ago, the fixed-parameter-tractability of the Konig Edge Deletion problem has been open since then, and has been conjectured to be W[1]-hard in several papers. In this paper, we settle the conjecture by proving it W[1]-hard. We prove that a variant of this problem, where we are given a graph G and a maximum matching M and we want a k-sized Konig edge deletion set that is disjoint from M, is fixed-parameter-tractable.

Accepted for publication in Theoretical Computer Science. Major revisions from the previous version were incorporated based on the comments from anonymous reviewers

Keywords

König edge deletion, FOS: Computer and information sciences, Discrete Mathematics (cs.DM), Parameterized complexity, tractability and kernelization, 68Q25, W-hardness, vertex cover, Edge subsets with special properties (factorization, matching, partitioning, covering and packing, etc.), König graphs, Computer Science - Data Structures and Algorithms, Computational difficulty of problems (lower bounds, completeness, difficulty of approximation, etc.), Data Structures and Algorithms (cs.DS), parameterized complexity, Computer Science - Discrete Mathematics

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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!
2
Average
Average
Average
Green
bronze