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Recolector de Ciencia Abierta, RECOLECTA
Article . 2026 . Peer-reviewed
License: CC BY
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Algorithmica
Article . 2025
License: CC BY
DBLP
Preprint . 2023
Data sources: DBLP
DBLP
Conference object
Data sources: DBLP
Pure Utrecht University
Conference object . 2024
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Geometric thickness of multigraphs is ¿R-Complete

Authors: Henry Förster; Philipp Kindermann; Tillmann Miltzow; Irene Parada; Soeren Terziadis; Birgit Vogtenhuber;

Geometric thickness of multigraphs is ¿R-Complete

Abstract

We say that a (multi)graph has geometric thickness t if there exists a straight-line drawing and a t-coloring of its edges where no two edges sharing a point in their relative interior have the same color. The Geometric Thickness problem asks whether a given multigraph has geometric thickness at most t. This problem was shown to be NP-hard for (Durocher et al. Comput Geom 56:1–18, 2016. https://doi.org/10.1016/j.comgeo.2016.03.003). In this paper, we settle the computational complexity of Geometric Thickness by showing that it is -complete already for thickness 30. Moreover, our reduction shows that the problem is -complete for 4392-planar graphs, where a graph is k-planar if it admits a topological drawing with at most k crossings per edge. In the course of our paper we answer previous questions on geometric thickness and on other related problems, in particular that simultaneous graph embeddings of 31 edge-disjoint graphs and pseudo-segment stretchability with chromatic number 30 are -complete.

Peer Reviewed

Countries
Spain, Netherlands
Keywords

Àrees temàtiques de la UPC::Matemàtiques i estadística::Geometria, Existential theory of the reals, Geometric thickness, Geometric simultaneous embedding, Colorability, Segment stretchability

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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!
0
Average
Average
Average
Green
hybrid