AffiliationDepartment of Computer Science, University of Arizona
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CitationAhmed 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.
JournalLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Rights© Springer Nature Switzerland AG 2020.
Collection InformationThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at firstname.lastname@example.org.
AbstractReadability 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/. © 2020, Springer Nature Switzerland AG.
Note12 month embargo; first published online 14 February 2021
VersionFinal accepted manuscript