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Theoretical Computer Science
Article . 2018 . Peer-reviewed
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Part of book or chapter of book . 2018 . Peer-reviewed
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Gap-planar graphs

Authors: Sang Won Bae; Jean-Francois Baffier; Jinhee Chun; Peter Eades; Kord Eickmeyer; Luca Grilli; Seok-Hee Hong; +4 Authors
Abstract

We introduce the family of $k$-gap-planar graphs for $k \geq 0$, i.e., graphs that have a drawing in which each crossing is assigned to one of the two involved edges and each edge is assigned at most $k$ of its crossings. This definition is motivated by applications in edge casing, as a $k$-gap-planar graph can be drawn crossing-free after introducing at most $k$ local gaps per edge. We present results on the maximum density of $k$-gap-planar graphs, their relationship to other classes of beyond-planar graphs, characterization of $k$-gap-planar complete graphs, and the computational complexity of recognizing $k$-gap-planar graphs.

A preliminary version of this paper appeared in the Proceedings of the 25th International Symposium on Graph Drawing and Network Visualization (GD 2017)

Keywords

complete graphs, Computational Geometry (cs.CG), FOS: Computer and information sciences, \(k\)-planar graphs, recognition problem, \(k\)-quasiplanar graphs, Graph theory (including graph drawing) in computer science, Analysis of algorithms and problem complexity, Computer Science - Computational Geometry, density results, beyond planarity \(k\)-gap-planar graphs, cs.CG

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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!
22
Top 10%
Top 10%
Top 10%
Green
bronze