An algorithm for labeling edges of hierarchical drawings

Kakoulis, Konstantinos G.; Tollis, Ioannis G.

doi:10.1007/3-540-63938-1_60

Cited by 31 publications

(32 citation statements)

References 14 publications

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“…1. If the minimum weight incident edge of a node in V o connects this node to a node in V g of degree 1 then: Theorem 2 [15]. The matching produced by Algorithm Fast Matching is a maximum cardinality matching.…”

Section: Algorithm Fast Matchingmentioning

confidence: 99%

“…The problem of assigning labels to a set of points or nodes, called Node Label Placement (NLP), has been studied widely [3]. However the problem of assigning labels to a set of lines or edges, known as Edge Label Placement (ELP) has not been as well studied until recently [16,15,24,27]. Both the NLP [9,19,22] and ELP [16] problems are known to be NP-Hard.…”

Section: Automatic Label Placementmentioning

confidence: 99%

“…The implementation of the GET labeling engine is based on the algorithms presented in [15,18,17]. We have made a number of improvements, extensions and adjustments to these algorithms to integrate them into the professional visualization tool.…”

Section: Automatic Label Placementmentioning

confidence: 99%

“…• Each label position overlaps at most one other label position [15]. If two label positions overlap then they belong to the same group.…”

Section: Selecting Labelsmentioning

confidence: 99%

“…The Graph Editor Toolkit offers four different layout libraries: Circular, Hierarchical, Orthogonal, and Symmetric. Each library in the Graph Editor Toolkit is equipped with fast algorithms for automatic placement of labels which are based on [16,15,18].…”

Section: Introductionmentioning

confidence: 99%

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On labeling in graph visualization

Doğrusöz¹,

Kakoulis²,

Madden³

et al. 2007

Information Sciences

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When visualizing graphs, it is essential to communicate the meaning of each graph object via text or graphical labels. Automatic placement of labels in a graph is an NP-Hard problem, for which efficient heuristic solutions have been recently developed. In this paper, we describe a general framework for modeling, drawing, editing, and automatic placement of labels respecting user constraints. In addition, we present the interface and the basic engine of the Graph Editor Toolkit -a family of portable graph visualization libraries designed for integration into graphical user interface application programs. This toolkit produces a high quality automated placement of labels in a graph using our framework. A brief survey of automatic label placement algorithms is also presented. Finally we describe extensions to certain existing automatic label placement algorithms, allowing their integration into this visualization tool.

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Section: Algorithm Fast Matchingmentioning

confidence: 99%

Section: Automatic Label Placementmentioning

confidence: 99%

Section: Automatic Label Placementmentioning

confidence: 99%

“…• Each label position overlaps at most one other label position [15]. If two label positions overlap then they belong to the same group.…”

Section: Selecting Labelsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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On labeling in graph visualization

Doğrusöz¹,

Kakoulis²,

Madden³

et al. 2007

Information Sciences

Self Cite

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Automatic layout of statecharts

2001

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Graphical notations are widely used for system specification. The usefulness of these notations depends primarily on their readability. Hence, automatic methods are needed to obtain efficient and understandable graphical representations of requirements. In this paper, we present an algorithm that automatically generates layouts of statecharts. We assume that relevant information is stored in a structure that we call a decomposition tree, and we draw the graph that models a statechart in a hierarchical fashion. Our approach excludes diagrams with inter‐level transitions. Copyright © 2001 John Wiley & Sons, Ltd.

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Computing Labeled Orthogonal Drawings

et al. 2002

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Abstract. This paper studies the problem of computing labeled orthogonal drawings. A label is modeled as a rectangle of prescribed size and it can be associated with either a vertex or an edge. Several optimization goals are taken into account. Namely, the labeled drawing can be required to have minimum total edge length, minimum width, minimum height, or minimum area. We present ILP models to compute optimal drawings with respect to the first three objectives and an algorithm exploiting these models which computes a drawing of minimum area (the compaction problem is known to be NP-complete in general).

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An algorithm for labeling edges of hierarchical drawings

Cited by 31 publications

References 14 publications

On labeling in graph visualization

On labeling in graph visualization

Automatic layout of statecharts

Computing Labeled Orthogonal Drawings

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