Peacock bundles: Bundle coloring for graphs with globality-locality trade-off

Jaakko Peltonen*, Ziyuan Lin

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

1 Citation (Scopus)


Bundling of graph edges (node-to-node connections) is a common technique to enhance visibility of overall trends in the edge structure of a large graph layout, and a large variety of bundling algorithms have been proposed. However, with strong bundling, it becomes hard to identify origins and destinations of individual edges. We propose a solution: we optimize edge coloring to differentiate bundled edges. We quantify strength of bundling in a flexible pairwise fashion between edges, and among bundled edges, we quantify how dissimilar their colors should be by dissimilarity of their origins and destinations. We solve the resulting nonlinear optimization, which is also interpretable as a novel dimensionality reduction task. In large graphs the necessary compromise is whether to differentiate colors sharply between locally occurring strongly bundled edges (“local bundles”), or also between the weakly bundled edges occurring globally over the graph (“global bundles”); we allow a user-set global-local tradeoff.We call the technique “peacock bundles”. Experiments show the coloring clearly enhances comprehensibility of graph layouts with edge bundling.

Original languageEnglish
Title of host publicationGraph Drawing and Network Visualization - 24th International Symposium, GD 2016, Revised Selected Papers
PublisherSpringer Verlag
Number of pages13
Volume9801 LNCS
ISBN (Print)9783319501055, 978-3-319-50106-2
Publication statusPublished - 2016
MoE publication typeA4 Article in a conference publication
EventInternational Symposium on Graph Drawing and Network Visualization - Athens, Greece
Duration: 19 Sep 201621 Sep 2016
Conference number: 24

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume9801 LNCS
ISSN (Print)03029743
ISSN (Electronic)16113349


ConferenceInternational Symposium on Graph Drawing and Network Visualization
Abbreviated titleGD
Internet address


  • Dimensionality reduction
  • Graph visualization
  • Machine learning
  • Network data

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