FFTEB: Edge bundling of huge graphs by the Fast Fourier Transform

Edge bundling techniques provide a visual simplification of cluttered graph drawings or trail sets. While many bundling techniques exist, only few recent ones can handle large datasets and also allow selective bundling based on edge attributes. We present a new technique that improves on both above...

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Vydáno v:IEEE Pacific Visualization Symposium s. 190 - 199
Hlavní autoři: Lhuillier, Antoine, Hurter, Christophe, Telea, Alexandru
Médium: Konferenční příspěvek
Jazyk:angličtina
Vydáno: IEEE 01.04.2017
Témata:
Clutter
Convolution
Fourier transforms
Graphics processing units
I.3.3 [Computing Methodologies]: Computer Graphics-Picture/Image Generation
I.3.6 [Computing Methodologies]: Computer Graphics-Methodology and Techniques
Image edge detection
Kernel
Scalability
ISSN:2165-8773
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Shrnutí:Edge bundling techniques provide a visual simplification of cluttered graph drawings or trail sets. While many bundling techniques exist, only few recent ones can handle large datasets and also allow selective bundling based on edge attributes. We present a new technique that improves on both above points, in terms of increasing both the scalability and computational speed of bundling, while keeping the quality of the results on par with state-of-the-art techniques. For this, we shift the bundling process from the image space to the spectral (frequency) space, thereby increasing computational speed. We address scalability by proposing a data streaming process that allows bundling of extremely large datasets with limited GPU memory. We demonstrate our technique on several real-world datasets and by comparing it with state-of-the-art bundling methods.
ISSN:2165-8773
DOI:10.1109/PACIFICVIS.2017.8031594