MLSEB: Edge Bundling Using Moving Least Squares Approximation

  • Jieting Wu
  • Jianping Zeng
  • Feiyu Zhu
  • Hongfeng Yu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10692)


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.


Edge bundling Graph visualization Moving least squares Visualization quality 



This research has been sponsored by the National Science Foundation through grants IIS-1652846, IIS-1423487, and ICER-1541043.


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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • Jieting Wu
    • 1
  • Jianping Zeng
    • 1
  • Feiyu Zhu
    • 1
  • Hongfeng Yu
    • 1
  1. 1.University of Nebraska-LincolnLincolnUSA

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