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New Quality Metrics for Dynamic Graph Drawing

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Graph Drawing and Network Visualization (GD 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12590))

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Abstract

In this paper, we present new quality metrics for dynamic graph drawings. Namely, we present a new framework for change faithfulness metrics for dynamic graph drawings, which compare the ground truth change in dynamic graphs and the geometric change in drawings.

More specifically, we present two specific instances, cluster change faithfulness metrics and distance change faithfulness metrics. We first validate the effectiveness of our new metrics using deformation experiments. Then we compare various graph drawing algorithms using our metrics. Our experiments confirm that the best cluster (resp. distance) faithful graph drawing algorithms are also cluster (resp. distance) change faithful.

This work is supported by ARC DP grant.

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Authors and Affiliations

  1. University of Sydney, Sydney, Australia

    Amyra Meidiana, Seok-Hee Hong & Peter Eades

Authors
  1. Amyra Meidiana
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  2. Seok-Hee Hong
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  3. Peter Eades
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Corresponding author

Correspondence to Amyra Meidiana .

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Editors and Affiliations

  1. LaBRI, University of Bordeaux, Talence, France

    David Auber

  2. Charles University, Prague, Czech Republic

    Pavel Valtr

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Meidiana, A., Hong, SH., Eades, P. (2020). New Quality Metrics for Dynamic Graph Drawing. In: Auber, D., Valtr, P. (eds) Graph Drawing and Network Visualization. GD 2020. Lecture Notes in Computer Science(), vol 12590. Springer, Cham. https://doi.org/10.1007/978-3-030-68766-3_35

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  • DOI: https://doi.org/10.1007/978-3-030-68766-3_35

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68765-6

  • Online ISBN: 978-3-030-68766-3

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