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Title: MLSEB: Edge Bundling Using Moving Least Squares Approximation
Edge bundling methods can effectively alleviate visual clutter and reveal high-level graph structures in large graph visualization. Researchers have devoted significant efforts to improve edge bundling according to different metrics. As the edge bundling family evolve rapidly, the quality of edge bundles receives increasing attention in the literature accordingly. In this paper, we present MLSEB, a novel method to generate edge bundles based on moving least squares (MLS) approximation. In comparison with previous edge bundling methods, we argue that our MLSEB approach can generate better results based on a quantitative metric of quality, and also ensure scalability and the efficiency for visualizing large graphs.  more » « less
Award ID(s):
1652846
NSF-PAR ID:
10062338
Author(s) / Creator(s):
; ; ;
Date Published:
Journal Name:
Graph Drawing and Network Visualization: 25th International Symposium, GD 2017
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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