Attribute-driven edge bundling for general graphs with applications in trail analysis

Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge properties such as direction and data attributes, or are otherwise computationally not scalable, which makes them unsuitable for tasks such as exploration of large traje...

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Bibliographic Details
Published in:IEEE Pacific Visualization Symposium pp. 39 - 46
Main Authors: Peysakhovich, Vsevolod, Hurter, Christophe, Telea, Alexandru
Format: Conference Proceeding
Language:English
Published: IEEE 01.04.2015
Subjects:
Aircraft
Clutter
I.3.3 [Computing Methodologies]: Computer Graphics-Picture/Image Generation
I.3.6 [Computing Methodologies]: Computer Graphics-Methodology and Techniques
Image edge detection
Instruments
Kernel
Trajectory
Visualization
ISSN:2165-8765
Online Access:Get full text
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Summary:Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge properties such as direction and data attributes, or are otherwise computationally not scalable, which makes them unsuitable for tasks such as exploration of large trajectory datasets. We present a new framework to generate bundled graph layouts according to any numerical edge attributes such as directions, timestamps or weights. We propose a GPU-based implementation linear in number of edges, which makes our algorithm applicable to large datasets. We demonstrate our method with applications in the analysis of aircraft trajectory datasets and eye-movement traces.
ISSN:2165-8765
DOI:10.1109/PACIFICVIS.2015.7156354