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Lookup NU author(s): Dr Daniel ArchambaultORCiD
This is the authors' accepted manuscript of an article that has been published in its final definitive form by IEEE Computer Society, 2022.
For re-use rights please refer to the publisher's terms and conditions.
© 1995-2012 IEEE. Edge bundling techniques cluster edges with similar attributes (i.e. similarity in direction and proximity) together to reduce the visual clutter. All edge bundling techniques to date implicitly or explicitly cluster groups of individual edges, or parts of them, together based on these attributes. These clusters can result in ambiguous connections that do not exist in the data. Confluent drawings of networks do not have these ambiguities, but require the layout to be computed as part of the bundling process. We devise a new bundling method, Edge-Path bundling, to simplify edge clutter while greatly reducing ambiguities compared to previous bundling techniques. Edge-Path bundling takes a layout as input and clusters each edge along a weighted, shortest path to limit its deviation from a straight line. Edge-Path bundling does not incur independent edge ambiguities typically seen in all edge bundling methods, and the level of bundling can be tuned through shortest path distances, Euclidean distances, and combinations of the two. Also, directed edge bundling naturally emerges from the model. Through metric evaluations, we demonstrate the advantages of Edge-Path bundling over other techniques.
Author(s): Wallinger M, Archambault D, Auber D, Nöllenburg M, Peltonen J
Publication type: Article
Publication status: Published
Journal: IEEE Transactions on Visualization and Computer Graphics
Year: 2022
Volume: 28
Issue: 1
Pages: 313-323
Print publication date: 01/01/2022
Online publication date: 29/09/2021
Acceptance date: 08/08/2021
Date deposited: 18/09/2023
ISSN (print): 1077-2626
ISSN (electronic): 1941-0506
Publisher: IEEE Computer Society
URL: https://doi.org/10.1109/TVCG.2021.3114795
DOI: 10.1109/TVCG.2021.3114795
PubMed id: 34587038
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