Validating graph drawing aesthetics

Purchase, Helen C.; Cohen, Robert F.; James, Murray

doi:10.1007/bfb0021827

Cited by 141 publications

(123 citation statements)

References 5 publications

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“…The biologists on this project initially envisaged data sets of up to 10,000 markers -however this figure then leapt to 250,000 and will only rise as technology advances. Similarly, node-link representations are useful for showing simple pedigree structures, as exampled by one of the figures in this paper, but edge crossings proliferate if we look above a small number of relationships, making the view increasingly intractable for users [13].…”

Section: Related Workmentioning

confidence: 99%

Visual cleaning of genotype data

Kennedy

Graham

Paterson

et al. 2013

2013 IEEE Symposium on Biological Data Visualization (BioVis)

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While some data cleaning tasks can be performed automatically, many more require expert human guidance to steer the cleaning process, especially if erroneous or unclean data is a product of relationships between entities. An example is pedigree genotype data: inheritance hierarchies in which the correctness of genotype data for any individual is judged on comparison to their relations' genotypes, as individuals should inherit DNA from their assumed ancestors. Thus, cleaning this data must consider the relationships between individuals; sometimes this means more data must be cleaned than first assumed, while in other situations it means errors across many individuals can be remedied by cleaning the data of a shared relation. Such judgements require a domain expert to hypothesise the effect changing particular data has on the wider data set.Using a visualization tool with the ability to undertake what-if interactions can assist a user in correctly cleaning such data. We achieve this by closely coupling an existing pedigree visualisation technique, VIPER, with a genotype cleaning algorithm, and then develop necessary extensions to the visualization to allow interactive data cleaning. A comparative user evaluation with biologists shows the advantages of this visualisation design over an existing cleaning tool and we discuss the challenges in the design of visual cleaning tools in which errors may be transitive.

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Section: Related Workmentioning

confidence: 99%

Visual cleaning of genotype data

Kennedy

Graham

Paterson

et al. 2013

2013 IEEE Symposium on Biological Data Visualization (BioVis)

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“…Further important criteria believed to determine the aesthetic appearance of a drawing include edge crossings and symmetries. Statistical evidence to support the former claim has recently beengiven in [14], while the data on the latter is not yet conclusive. Two important observation on spring-embedder algorithms can be made.…”

Section: Layoutmentioning

confidence: 99%

Fast interactive 3-D graph visualization

Bruß¹,

Frick²

1996

Graph Drawing

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Abstract. We present a 3-D version of GEM [6], a randomized adaptive layout algorithm for nicely drawing undirected graphs, based on the spring-embedder paradigm [4]. The new version, GEM-3D, contains several improvements besides the adaptation to 3-D geometry. The main result of this work is that for the first time, 3-D layout and presentation techniques are combined available at interactive speed. Even large real-life graphs with hundreds of vertices can be meaningfully displayed by enhancing the presentation with additional visual clues (color, perspective and light) and the possibility of interactive user navigation. In the demonstration, we interactively visualize many graphs (artificial and realworld) of different size and complexity to support our claims. We show that GEM-3D is capable of producing a textbook-like drawing of the PETERSEN graph, a notoriously hard case for automatic drawing tools. To the best of our knowledge, this has not been achieved before by automatic layout algorithms purely based on heuristics.

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“…This aesthetics has been proven to have a measurable effect on the "readability" of the graph [9,10], hence it may play a major role in the user interface aspect of the information visualization application. This, added to the fact that the barycentre method works in reasonable time when the size of the graph does not exceed some bounds, seems to make the barycentre method a real option.…”

Section: Aestheticsmentioning

confidence: 99%

DAG Drawing from an Information Visualization Perspective

Mélançon

Herman

2000

Eurographics

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When dealing with a graph, any visualization strategy must rely on a layout procedure at least to initiate the process. Because the visualization process evolves within an interactive environment the choice of this layout procedure is critical and will often be based on efficiency.This paper compares two popular layout strategies, one based on the extraction of a spanning tree, the other based on edge crossing minimization of directed acyclic graphs. The comparison is made based on a large number of experimental evidence gathered through random graph generation. The main conclusion of these experiments is that, contrary to the popular belief, usage of edge crossing minimization algorithms may be extremely useful and advantageous, even under the heavy requirements of information visualization.

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Validating graph drawing aesthetics

Cited by 141 publications

References 5 publications

Visual cleaning of genotype data

Visual cleaning of genotype data

Fast interactive 3-D graph visualization

DAG Drawing from an Information Visualization Perspective

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