Results 21 to 30 of about 157,734 (242)

Clustered Planarity Testing Revisited [PDF]

open access: yesThe Electronic Journal of Combinatorics, 2014
The Hanani–Tutte theorem is a classical result proved for the first time in the 1930s that characterizes planar graphs as graphs that admit a drawing in the plane in which every pair of edges not sharing a vertex cross an even number of times. We generalize this result to clustered graphs with two disjoint clusters, and show that a straightforward ...
Radoslav Fulek   +3 more
openaire   +5 more sources

A Note on Obstructions to Clustered Planarity [PDF]

open access: yesThe Electronic Journal of Combinatorics, 2011
A planar digraph $D$ is clustered planar if in some planar embedding of $D$ we have at each vertex the in-arcs occurring sequentially in the local rotation. By supplementing the operations used to form the usual minors in Kuratowski's theorem, clustered planar digraphs are characterised.
Sneddon, Jamie, Bonnington, Paul
openaire   +4 more sources

Planarity-Preserving Clustering and Embedding for Large Planar Graphs [PDF]

open access: yesComputational Geometry, 1999
zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christian A. Duncan   +2 more
openaire   +4 more sources

Planarization of Clustered Graphs [PDF]

open access: yes, 2002
We propose a planarization algorithm for clustered graphs and experimentally test its efficiency and effectiveness. Further, we integrate our planarization strategy into a complete topology-shape-metrics algorithm for drawing clustered graphs in the orthogonal drawing convention.
Di Battista G.   +2 more
openaire   +3 more sources

Synchronized Planarity with Applications to Constrained Planarity Problems [PDF]

open access: yes, 2021
We introduce the problem Synchronized Planarity. Roughly speaking, its input is a loop-free multi-graph together with synchronization constraints that, e.g., match pairs of vertices of equal degree by providing a bijection between their edges ...
Fink, Simon D.   +2 more
core   +1 more source

C-planarity testing of embedded clustered graphs with bounded dual carving-width [PDF]

open access: yes, 2019
For a clustered graph, i.e, a graph whose vertex set is recursively partitioned into clusters, the C-Planarity Testing problem asks whether it is possible to find a planar embedding of the graph and a representation of each cluster as a region ...
Goodrich Michael   +3 more
core   +2 more sources

Two Simple and Reliable Metrics of Molecular Planarity: Molecular Planarity Parameter (MPP) and Span of Deviation from Plane (SDP) [PDF]

open access: yes, 2021
Planarity is a very important structural character of molecules, which is closely related to many molecular properties. However, there is currently no simple, universal, and robust way to measure molecular planarity. In order to fill this evident gap, we
Tian, Lu
core   +1 more source

Synchronized Planarity with Applications to Constrained Planarity Problems [PDF]

open access: yes, 2020
We introduce the problem Synchronized Planarity. Roughly speaking, its input is a loop-free multi-graph together with synchronization constraints that, e.g., match pairs of vertices of equal degree by providing a bijection between their edges ...
Fink, Simon D.   +2 more
core   +1 more source

On the Complexity of Clustered-Level Planarity and T-Level Planarity

open access: yesCoRR, 2014
In this paper we study two problems related to the drawing of level graphs, that is, T-LEVEL PLANARITY and CLUSTERED-LEVEL PLANARITY. We show that both problems are NP-complete in the general case and that they become polynomial-time solvable when restricted to proper instances.
Patrizio Angelini   +4 more
openaire   +2 more sources

Planar Clusters

open access: yes, 2004
We provide upper and lower bounds on the least-perimeter way to enclose and separate n regions of equal area in the plane. Along the way, inside the hexagonal honeycomb, we provide minimizers for each n .
Heppes, Aladar, Morgan, Frank
openaire   +2 more sources

Home - About - Disclaimer - Privacy