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Graph Drawing via Gradient Descent, \((GD)^2\)

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Graph Drawing and Network Visualization (GD 2020)

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Abstract

Readability criteria, such as distance or neighborhood preservation, are often used to optimize node-link representations of graphs to enable the comprehension of the underlying data. With few exceptions, graph drawing algorithms typically optimize one such criterion, usually at the expense of others. We propose a layout approach, Graph Drawing via Gradient Descent, \((GD)^2\), that can handle multiple readability criteria. \((GD)^2\) can optimize any criterion that can be described by a smooth function. If the criterion cannot be captured by a smooth function, a non-smooth function for the criterion is combined with another smooth function, or auto-differentiation tools are used for the optimization. Our approach is flexible and can be used to optimize several criteria that have already been considered earlier (e.g., obtaining ideal edge lengths, stress, neighborhood preservation) as well as other criteria which have not yet been explicitly optimized in such fashion (e.g., vertex resolution, angular resolution, aspect ratio). We provide quantitative and qualitative evidence of the effectiveness of \((GD)^2\) with experimental data and a functional prototype: http://hdc.cs.arizona.edu/~mwli/graph-drawing/.

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Acknowledgments

This work was supported in part by NSF grants CCF-1740858, CCF-1712119, and DMS-1839274.

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Authors and Affiliations

  1. Department of Computer Science, University of Arizona, Tucson, USA

    Reyan Ahmed, Felice De Luca, Sabin Devkota, Stephen Kobourov & Mingwei Li

Authors
  1. Reyan Ahmed
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  2. Felice De Luca
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  3. Sabin Devkota
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  4. Stephen Kobourov
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  5. Mingwei Li
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Corresponding author

Correspondence to Reyan Ahmed .

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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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Ahmed, R., De Luca, F., Devkota, S., Kobourov, S., Li, M. (2020). Graph Drawing via Gradient Descent, \((GD)^2\). 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_1

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  • DOI: https://doi.org/10.1007/978-3-030-68766-3_1

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