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dc.contributor.authorAhmed, Reyan
dc.contributor.authorDe Luca, Felice
dc.contributor.authorDevkota, Sabin
dc.contributor.authorKobourov, Stephen G
dc.contributor.authorLi, Mingwei
dc.date.accessioned2022-03-23T23:20:27Z
dc.date.available2022-03-23T23:20:27Z
dc.date.issued2022
dc.identifier.citationAhmed, R., De Luca, F., Devkota, S., Kobourov, S. G., & Li, M. (2022). Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, (SGD)^2. IEEE Transactions on Visualization and Computer Graphics.en_US
dc.identifier.issn1077-2626
dc.identifier.doi10.1109/tvcg.2022.3155564
dc.identifier.urihttp://hdl.handle.net/10150/663769
dc.description.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, Multicriteria Scalable Graph Drawing via Stochastic Gradient Descent, (SGD)^2, that can handle multiple readability criteria. (SGD)^2 can optimize any criterion that can be described by a differentiable function. 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., node resolution, angular resolution, aspect ratio). The approach is scalable and can handle large graphs. A variation of the underlying approach can also be used to optimize many desirable properties in planar graphs, while maintaining planarity. Finally, we provide quantitative and qualitative evidence of the effectiveness of (SGD)^2: we analyze the interactions between criteria, measure the quality of layouts generated from (SGD)^2 as well as the runtime behavior, and analyze the impact of sample sizes. The source code is available on github and we also provide an interactive demo for small graphs.en_US
dc.description.sponsorshipNational Science Foundationen_US
dc.language.isoenen_US
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en_US
dc.rights© 2021 IEEE.en_US
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en_US
dc.subjectgradient descenten_US
dc.subjectGraph drawingen_US
dc.subjectPlant layout -- Mathematical models.en_US
dc.subjectLinear programmingen_US
dc.subjectMinimizationen_US
dc.subjectOptimizationen_US
dc.subjectquality metricsen_US
dc.subjectStandardsen_US
dc.subjectStressen_US
dc.titleMulticriteria Scalable Graph Drawing via Stochastic Gradient Descent, (SGD)^2en_US
dc.typeArticleen_US
dc.identifier.eissn1941-0506
dc.contributor.departmentUniversity of Arizona Department of Computer Scienceen_US
dc.identifier.journalIEEE Transactions on Visualization and Computer Graphicsen_US
dc.description.noteImmediate accessen_US
dc.description.collectioninformationThis 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 repository@u.library.arizona.edu.en_US
dc.eprint.versionFinal accepted manuscripten_US
dc.source.journaltitleIEEE Transactions on Visualization and Computer Graphics
dc.source.beginpage1
dc.source.endpage1
refterms.dateFOA2022-03-23T23:20:28Z


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