FFTEB: Edge bundling of huge graphs by the Fast Fourier Transform
FFTEB: Edge bundling of huge graphs by the Fast Fourier Transform
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DOI：
10.1109/pacificvis.2017.8031594
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发表时间：
2017-04
期刊：
影响因子：
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通讯作者：
Antoine Lhuillier;C. Hurter;A. Telea
中科院分区：
文献类型：
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作者：
Antoine Lhuillier;C. Hurter;A. Telea
边捆绑技术提供了一个直观的简化混乱的图形绘图或线索集。虽然存在许多捆绑技术，但只有少数最近的技术可以处理大型数据集，并且还允许基于边缘属性的选择性捆绑。我们提出了一种新的技术，提高了上述两点，在增加的可扩展性和计算速度的捆绑，同时保持质量的结果与国家的最先进的技术。为此，我们将捆绑过程从图像空间转移到频谱（频率）空间，从而提高计算速度。我们通过提出一个数据流处理，允许捆绑非常大的数据集与有限的GPU内存来解决可扩展性。我们在几个真实世界的数据集上展示了我们的技术，并将其与最先进的捆绑方法进行了比较。
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.