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.
Fast Fourier Transform Edge Bundling (FFTEB) REF improved the scalability of density estimation by transforming the density space to the frequency space.
FFTEB: Edge bundling of huge graphs by the Fast Fourier Transform
2017 IEEE Pacific Visualization Symposium (PacificVis)
2017 IEEE Pacific Visualization Symposium (PacificVis)
Computer Science