Voxel-Based Edge Bundling Trough Direction-Aware Kernel Smoothing

Daniel Zielasko, Xiaoqing Zhao, Ali Can Demiralp, Torsten Wolfgang Kuhlen, Benjamin Weyers
Computers & Graphics

Relational data with a spatial embedding and depicted as node-link diagram is very common, e.g., in neuroscience, and edge bundling is one way to increase its readability or reveal hidden structures. This article presents a 3D extension to kernel density estimation-based edge bundling that is meant to be used in an interactive immersive analysis setting. This extension adds awareness of the edges’ direction when using kernel smoothing and thus implicitly supports both directed and undirected graphs. The method generates explicit bundles of edges, which can be analyzed and visualized individually and as sufficient as possible for a given application context, while it scales linearly with the input size.

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title = "Voxel-based edge bundling through direction-aware kernel smoothing",
journal = "Computers & Graphics",
volume = "83",
pages = "87 - 96",
year = "2019",
issn = "0097-8493",
doi = "https://doi.org/10.1016/j.cag.2019.06.008",
url = "http://www.sciencedirect.com/science/article/pii/S0097849319301025",
author = "Daniel Zielasko and Xiaoqing Zhao and Ali Can Demiralp and Torsten W. Kuhlen and Benjamin Weyers"}

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