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Edge-Path Bundling: A Less Ambiguous Edge Bundling Approach

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.

Publication metadata

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


DOI: 10.1109/TVCG.2021.3114795

PubMed id: 34587038


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Funder referenceFunder name
Academy of Finland
Vienna Science and Technology Fund