FFTEB: Edge Bundling of Huge Graphs by the Fast Fourier Transform

Abstract : 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.
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Communication dans un congrès
PacificVis 2017, 10th IEEE Pacific Visualization Symposium, Apr 2017, Seoul, South Korea. IEEE
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Soumis le : mardi 18 juillet 2017 - 10:05:37
Dernière modification le : lundi 9 octobre 2017 - 13:18:03

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Antoine Lhuillier, Christophe Hurter, Alexandru Telea. FFTEB: Edge Bundling of Huge Graphs by the Fast Fourier Transform. PacificVis 2017, 10th IEEE Pacific Visualization Symposium, Apr 2017, Seoul, South Korea. IEEE. 〈hal-01533755〉

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