MLSEB: Edge bundling using moving least squares approximation

Jieting Wu, Jianping Zeng, Feiyu Zhu, Hongfeng Yu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationGraph Drawing and Network Visualization - 25th International Symposium, GD 2017, Revised Selected Papers
EditorsKwan-Liu Ma, Fabrizio Frati
PublisherSpringer Verlag
Pages379-393
Number of pages15
ISBN (Print)9783319739144
DOIs
StatePublished - Jan 1 2018
Event25th International Symposium on Graph Drawing and Network Visualization, GD 2017 - Boston, United States
Duration: Sep 25 2017Sep 27 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10692 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other25th International Symposium on Graph Drawing and Network Visualization, GD 2017
CountryUnited States
CityBoston
Period9/25/179/27/17

Keywords

  • Edge bundling
  • Graph visualization
  • Moving least squares
  • Visualization quality

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

Fingerprint Dive into the research topics of 'MLSEB: Edge bundling using moving least squares approximation'. Together they form a unique fingerprint.

  • Cite this

    Wu, J., Zeng, J., Zhu, F., & Yu, H. (2018). MLSEB: Edge bundling using moving least squares approximation. In K-L. Ma, & F. Frati (Eds.), Graph Drawing and Network Visualization - 25th International Symposium, GD 2017, Revised Selected Papers (pp. 379-393). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10692 LNCS). Springer Verlag. https://doi.org/10.1007/978-3-319-73915-1_30