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Configurations with few crossings in topological graphs

Authors: Christian Knauer; Étienne Schramm; Andreas Spillner 0001; Alexander Wolff 0001;

Configurations with few crossings in topological graphs

Abstract

In this paper we study the problem of computing subgraphs of a certain configuration in a given topological graph G such that the number of crossings in the subgraph is minimum. The configurations that we consider are spanning trees, s-t paths, cycles, matchings, and kappa-factors for kappa in {1,2}. We show that it is NP-hard to approximate the minimum number of crossings for these configurations within a factor of k^(1-epsilon) for any epsilon > 0, where k is the number of crossings in G. We then show that the problems are fixed-parameter tractable if we use the number of crossings in the given graph as the parameter. Finally we present a mixed-integer linear program formulation for each problem and a simple but effective heuristic for spanning trees.

Countries
Germany, Netherlands
Keywords

ddc:004, fixed-parameter algorithms, Monte-Carlo algorithms, Control and Optimization, DATA processing & computer science, Crossing minimization, crossing minimization, Fixed-parameter algorithms, Approximation algorithms, 004, Planar graphs; geometric and topological aspects of graph theory, Computer Science Applications, Topological graphs, Computational Mathematics, Computational Theory and Mathematics, Graph algorithms (graph-theoretic aspects), Graph theory (including graph drawing) in computer science, Geometry and Topology, Non-approximability, approximation algorithms, info:eu-repo/classification/ddc/004

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    popularity
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    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!
7
Average
Average
Average
Green
hybrid