FFTEB

International audience; 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.