A New Width Parameter of Graphs Based on Edge Cuts: α-Edge-Crossing Width
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Chang, Yeonsu | - |
dc.contributor.author | Kwon, O-joung | - |
dc.contributor.author | Lee, Myounghwan | - |
dc.date.accessioned | 2023-11-14T08:14:44Z | - |
dc.date.available | 2023-11-14T08:14:44Z | - |
dc.date.created | 2023-11-01 | - |
dc.date.issued | 2023-09 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | https://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192166 | - |
dc.description.abstract | We introduce graph width parameters, called α-edge-crossing width and edge-crossing width. These are defined in terms of the number of edges crossing a bag of a tree-cut decomposition. They are motivated by edge-cut width, recently introduced by Brand et al. (WG 2022). We show that edge-crossing width is equivalent to the known parameter tree-partition-width. On the other hand, α-edge-crossing width is a new parameter; tree-cut width and α-edge-crossing width are incomparable, and they both lie between tree-partition-width and edge-cut width. We provide an algorithm that, for a given n-vertex graph G and integers k and α, in time (Formula presented) either outputs a tree-cut decomposition certifying that the α-edge-crossing width of G is at most (Formula presented) or confirms that the α-edge-crossing width of G is more than k. As applications, for every fixed α, we obtain FPT algorithms for the List Coloring and Precoloring Extension problems parameterized by α-edge-crossing width. They were known to be W[1]-hard parameterized by tree-partition-width, and FPT parameterized by edge-cut width, and we close the complexity gap between these two parameters. | - |
dc.language | 영어 | - |
dc.language.iso | en | - |
dc.publisher | Springer Science and Business Media Deutschland GmbH | - |
dc.title | A New Width Parameter of Graphs Based on Edge Cuts: α-Edge-Crossing Width | - |
dc.type | Article | - |
dc.contributor.affiliatedAuthor | Kwon, O-joung | - |
dc.identifier.doi | 10.1007/978-3-031-43380-1_13 | - |
dc.identifier.scopusid | 2-s2.0-85174545215 | - |
dc.identifier.bibliographicCitation | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.14093, pp.172 - 186 | - |
dc.relation.isPartOf | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.title | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | - |
dc.citation.volume | 14093 | - |
dc.citation.startPage | 172 | - |
dc.citation.endPage | 186 | - |
dc.type.rims | ART | - |
dc.type.docType | Conference paper | - |
dc.description.journalClass | 1 | - |
dc.description.isOpenAccess | N | - |
dc.description.journalRegisteredClass | scopus | - |
dc.subject.keywordAuthor | FPT algorithm | - |
dc.subject.keywordAuthor | List Coloring | - |
dc.subject.keywordAuthor | α-edge-crossing width | - |
dc.identifier.url | https://link.springer.com/chapter/10.1007/978-3-031-43380-1_13 | - |
Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.
222, Wangsimni-ro, Seongdong-gu, Seoul, 04763, Korea+82-2-2220-1365
COPYRIGHT © 2021 HANYANG UNIVERSITY.
Certain data included herein are derived from the © Web of Science of Clarivate Analytics. All rights reserved.
You may not copy or re-distribute this material in whole or in part without the prior written consent of Clarivate Analytics.