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    Node-Link or Adjacency Matrices: Old Question, New Insights

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    NL-AM-TVCG18.pdf
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    Description:
    Final Accepted Manuscript
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    Author
    Okoe, Mershack
    Jianu, Radu
    Kobourov, Stephen
    Affiliation
    Univ Arizona
    Issue Date
    2019-10
    Keywords
    evaluation
    user study
    graphs
    networks
    node-link
    adjacency matrices
    
    Metadata
    Show full item record
    Publisher
    IEEE COMPUTER SOC
    Citation
    Okoe, M., Jianu, R., & Kobourov, S. G. (2018). Node-link or Adjacency Matrices: Old Question, New Insights. IEEE transactions on visualization and computer graphics.
    Journal
    IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS
    Rights
    © 2018 IEEE.
    Collection Information
    This 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.
    Abstract
    Visualizing network data is applicable in domains such as biology, engineering, and social sciences. We report the results of a study comparing the effectiveness of the two primary techniques for showing network data: node-link diagrams and adjacency matrices. Specifically, an evaluation with a large number of online participants revealed statistically significant differences between the two visualizations. Our work adds to existing research in several ways. First, we explore a broad spectrum of network tasks, many of which had not been previously evaluated. Second, our study uses two large datasets, typical of many real-life networks not explored by previous studies. Third, we leverage crowdsourcing to evaluate many tasks with many participants. This paper is an expanded journal version of a Graph Drawing (GD'17) conference paper. We evaluated a second dataset, added a qualitative feedback section, and expanded the procedure, results, discussion, and limitations sections.
    ISSN
    1077-2626
    EISSN
    1941-0506
    PubMed ID
    30130228
    DOI
    10.1109/TVCG.2018.2865940
    Version
    Final accepted manuscript
    Sponsors
    NSFNational Science Foundation (NSF) [CCF-1740858, CCF-1712119]
    ae974a485f413a2113503eed53cd6c53
    10.1109/TVCG.2018.2865940
    Scopus Count
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    UA Faculty Publications

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