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DBLP
Doctoral thesis
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Crossings in clustered level graphs.

Authors: Forster, Michael;

Crossings in clustered level graphs.

Abstract

Cluster-Graphen sind ein erweitertes Graph-Modell mit einem rekursiven Clustering der Knoten entsprechend einer gegebenen Inklusionsrelation. Diese bedeutende Technik um Zusammengehörigkeit bestimmter Teile des Graphen auszudrücken wird in vielen Anwendungen benutzt, etwa biochemischen Reaktionsnetzen oder UML Klassendiagrammen. Für gerichtete Cluster-Graphen werden üblicherweise Level-Zeichnungen verwendet, was zu Cluster-Level-Graphen führt. Diese Arbeit analysiert den Zusammenhang zwischen Clustern und Level und deren Auswirkungen auf Kantenkreuzungen und Cluster/Kanten-Kreuzungen.

Clustered graphs are an enhanced graph model with a recursive clustering of the vertices according to a given nesting relation. This prime technique for expressing coherence of certain parts of the graph is used in many applications, such as biochemical pathways and UML class diagrams. For directed clustered graphs usually level drawings are used, leading to clustered level graphs. In this thesis we analyze the interrelation of clusters and levels and their influence on edge crossings and cluster/edge crossings.

Country
Germany
Related Organizations
Keywords

ddc:004, Azyklischer gerichteter Graph, Planarer Graph, Constraint-Erfüllung, Cluster, Kreuzungszahl, Kreuzung, Graphenzeichnen, Minimierung

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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