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Linear-Time Algorithms for Scattering Number and Hamilton-Connectivity of Interval Graphs

Linear-time algorithms for scattering number and Hamilton-connectivity of interval graphs
Authors: Hajo Broersma; Jirí Fiala 0001; Petr A. Golovach; Tomás Kaiser; Daniël Paulusma; Andrzej Proskurowski;

Linear-Time Algorithms for Scattering Number and Hamilton-Connectivity of Interval Graphs

Abstract

AbstractWe prove that for all an interval graph is ‐Hamilton‐connected if and only if its scattering number is at most k. This complements a previously known fact that an interval graph has a nonnegative scattering number if and only if it contains a Hamilton cycle, as well as a characterization of interval graphs with positive scattering numbers in terms of the minimum size of a path cover. We also give an time algorithm for computing the scattering number of an interval graph with n vertices and m edges, which improves the previously best‐known time bound for solving this problem. As a consequence of our two results, the maximum k for which an interval graph is k‐Hamilton‐connected can be computed in time.

Countries
Netherlands, United Kingdom
Keywords

FOS: Computer and information sciences, Connectivity, Eulerian and Hamiltonian graphs, EWI-24425, Hamilton-connectivity, Scattering number, minimum size of a path cover, IR-89270, 2024 OA procedure, METIS-302692, Graph algorithms (graph-theoretic aspects), Hamilton cycle, Computer Science - Data Structures and Algorithms, Interval graph, Data Structures and Algorithms (cs.DS), Linear algorithm, MSC-05C

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    Top 10%
    influence
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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!
19
Top 10%
Top 10%
Average
Green
hybrid