Edge bundling methods reduce visual clutter of dense and occluded graphs. However, existing bundling techniques either ignore edge
properties such as direction and data attributes, or are otherwise computationally
not scalable, which makes them unsuitable for tasks
such as exploration of large trajectory datasets. We present a new
framework to generate bundled graph layouts according to any numerical
edge attributes such as directions, timestamps or weights.
We propose a GPU-based implementation linear in number of edges, which makes our algorithm applicable to large datasets. We demonstrate our method with applications in the analysis of aircraft trajectory datasets and eye-movement traces.