Cluster deletion revisited

Dekel Tsur

doi:https://doi.org/10.1016/j.ipl.2021.106171

Cluster deletion revisited

Dekel Tsur

Department of Computer Science

Ben-Gurion University of the Negev

Research output: Contribution to journal › Article › peer-review

Abstract

In the CLUSTER DELETION problem the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k edges whose removal from G results in a graph in which every connected component is a clique. In this paper we give an algorithm for CLUSTER DELETION whose running time is O^⁎(1.404^k).

Original language	American English
Article number	106171
Journal	Information Processing Letters
Volume	173
DOIs	https://doi.org/10.1016/j.ipl.2021.106171
State	Published - 1 Jan 2022

Keywords

Branching algorithms
Graph algorithms
Parameterized complexity

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
Signal Processing
Information Systems
Computer Science Applications

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https://doi.org/10.1016/j.ipl.2021.106171

Cite this

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title = "Cluster deletion revisited",

abstract = "In the CLUSTER DELETION problem the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k edges whose removal from G results in a graph in which every connected component is a clique. In this paper we give an algorithm for CLUSTER DELETION whose running time is O⁎(1.404k).",

keywords = "Branching algorithms, Graph algorithms, Parameterized complexity",

author = "Dekel Tsur",

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year = "2022",

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AB - In the CLUSTER DELETION problem the input is a graph G and an integer k, and the goal is to decide whether there is a set of at most k edges whose removal from G results in a graph in which every connected component is a clique. In this paper we give an algorithm for CLUSTER DELETION whose running time is O⁎(1.404k).

KW - Branching algorithms

KW - Graph algorithms

KW - Parameterized complexity

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M3 - Article

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VL - 173

JO - Information Processing Letters

JF - Information Processing Letters

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ER -

Cluster deletion revisited

Abstract

Keywords

All Science Journal Classification (ASJC) codes

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