Visualizing Evolving Trees

Kathryn Gray, Mingwei Li, Reyan Ahmed, Stephen Kobourov

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


Evolving trees arise in many real-life scenarios from computer file systems and dynamic call graphs, to fake news propagation and disease spread. Most layout algorithms for static trees do not work well in an evolving setting (e.g., they are not designed to be stable between time steps). Dynamic graph layout algorithms are better suited to this task, although they often introduce unnecessary edge crossings. With this in mind we propose two methods for visualizing evolving trees that guarantee no edge crossings, while optimizing (1) desired edge length realization, (2) layout compactness, and (3) stability. We evaluate the two new methods, along with five prior approaches (three static and two dynamic), on real-world datasets using quantitative metrics: stress, desired edge length realization, layout compactness, stability, and running time. The new methods are fully functional and available on github. (This work was supported in part by NSF grants CCF-1740858, CCF-1712119, and DMS-1839274.)

Original languageEnglish (US)
Title of host publicationGraph Drawing and Network Visualization - 30th International Symposium, GD 2022, Revised Selected Papers
EditorsPatrizio Angelini, Reinhard von Hanxleden
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages17
ISBN (Print)9783031222023
StatePublished - 2023
Event30th International Symposium on Graph Drawing and Network Visualization, GD 2022 - Tokyo, Japan
Duration: Sep 13 2022Sep 16 2022

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume13764 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference30th International Symposium on Graph Drawing and Network Visualization, GD 2022

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)


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