FFTEB: Fast Fourier Transform Edge Bundling


A. Lhuillier, C. Hurter, A. Telea pdf slides
FFTEB: Edge Bundling of Huge Graphs by the Fast Fourier Transform
Proc. IEEE PacificVis, 2017

Visual Studio C# source code (GPU-CUDA)


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.


Test Data:


US migration - 10K edges, 300K points:

Large US migration - 600K edges, 63 Billion points


Amazon Graph for different bundling resolutions - 800K edges, 100-400-1000 pixels