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Research Article | Open Access

Polynomial time recognition of vertices contained in all (or no) maximum dissociation sets of a tree

Jianhua Tu1,2,3Lei Zhang4Junfeng Du5Rongling Lang6( )
School of Mathematics and Statistics, Beijing Technology and Business University, Beijing 100048, China
Key Laboratory of Tibetan Information Processing and Machine Translation, Qinghai Province, XiNing 810008, China
Key Laboratory of Tibetan Information Processing, Ministry of Education, XiNing 810008, China
School of Mathematics and Statistics, Beijing Institute of Technology, Beijing 100081, China
Department of Mathematics, Beijing University of Chemical Technology, Beijing 100029, China
School of Electronics and Information Engineering, Beihang University, Beijing 100191, China
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Abstract

In a graph G, a dissociation set is a subset of vertices which induces a subgraph with vertex degree at most 1. Finding a dissociation set of maximum cardinality in a graph is NP-hard even for bipartite graphs and is called the maximum dissociation set problem. The complexity of the maximum dissociation set problem in various sub-classes of graphs has been extensively studied in the literature. In this paper, we study the maximum dissociation problem from different perspectives and characterize the vertices belonging to all maximum dissociation sets, and no maximum dissociation set of a tree. We present a linear time recognition algorithm which can determine whether a given vertex in a tree is contained in all (or no) maximum dissociation sets of the tree. Thus for a tree with n vertices, we can find all vertices belonging to all (or no) maximum dissociation sets of the tree in O ( n 2 ) time.

CLC number: 05C05, 05C69, 05C85

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AIMS Mathematics
Pages 569-578

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Cite this article:
Tu J, Zhang L, Du J, et al. Polynomial time recognition of vertices contained in all (or no) maximum dissociation sets of a tree. AIMS Mathematics, 2022, 7(1): 569-578. https://doi.org/10.3934/math.2022036

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Received: 08 August 2021
Accepted: 07 October 2021
Published: 15 January 2022
©2022 the Author(s), licensee AIMS Press.

This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0)