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A New Width Parameter of Graphs Based on Edge Cuts: α-Edge-Crossing Width

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dc.contributor.authorChang, Yeonsu-
dc.contributor.authorKwon, O-joung-
dc.contributor.authorLee, Myounghwan-
dc.date.accessioned2023-11-14T08:14:44Z-
dc.date.available2023-11-14T08:14:44Z-
dc.date.created2023-11-01-
dc.date.issued2023-09-
dc.identifier.issn0302-9743-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/192166-
dc.description.abstractWe 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.language영어-
dc.language.isoen-
dc.publisherSpringer Science and Business Media Deutschland GmbH-
dc.titleA New Width Parameter of Graphs Based on Edge Cuts: α-Edge-Crossing Width-
dc.typeArticle-
dc.contributor.affiliatedAuthorKwon, O-joung-
dc.identifier.doi10.1007/978-3-031-43380-1_13-
dc.identifier.scopusid2-s2.0-85174545215-
dc.identifier.bibliographicCitationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), v.14093, pp.172 - 186-
dc.relation.isPartOfLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.titleLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.citation.volume14093-
dc.citation.startPage172-
dc.citation.endPage186-
dc.type.rimsART-
dc.type.docTypeConference paper-
dc.description.journalClass1-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscopus-
dc.subject.keywordAuthorFPT algorithm-
dc.subject.keywordAuthorList Coloring-
dc.subject.keywordAuthorα-edge-crossing width-
dc.identifier.urlhttps://link.springer.com/chapter/10.1007/978-3-031-43380-1_13-
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