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Journal of Graph Algorithms and Applications
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On the Maximum Crossing Number

On the maximum crossing number
Authors: Markus Chimani; Stefan Felsner; Stephen G. Kobourov; Torsten Ueckerdt; Pavel Valtr 0001; Alexander Wolff 0001;

On the Maximum Crossing Number

Abstract

Research about crossings is typically about minimization. In this paper, we consider maximizing the number of crossings over all possible ways to draw a given graph in the plane. Alpert et al. [Electron. J. Combin., 2009] conjectured that any graph has a convex straight-line drawing, that is, a drawing with vertices in convex position, that maximizes the number of edge crossings. We disprove this conjecture by constructing a planar graph on twelve vertices that admits a non-convex drawing with more crossings than any convex drawing. Bald et al. [Proc. COCOON, 2016] showed that it is NP-hard to compute the maximum number of crossings of a geometric graph and that the weighted geometric case is NP-hard to approximate. We strengthen these results by showing hardness of approximation even for the unweighted geometric case. We also prove that the unweighted topological case is NP-hard.

Country
Germany
Keywords

ddc:510, Computational Geometry (cs.CG), FOS: Computer and information sciences, Extremal problems in graph theory, I.3.5, Graph representations (geometric and intersection representations, etc.), G.2.1, G.2.2, 510, graph drawing, G.2.1; G.2.2; I.3.5, 68R10, 68U05, Computer Science - Computational Geometry, Mathematics, info:eu-repo/classification/ddc/510

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    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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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!
4
Average
Average
Average
Green
gold