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    Detangler: Visual Analytics for Multiplex Networks

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    Date
    2015
    Author
    Renoust, Benjamin
    Melancon, Guy
    Munzner, Tamara ORCID
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    Abstract
    A multiplex network has links of different types, allowing it to express many overlapping types of relationships. A core task in network analysis is to evaluate and understand group cohesion; that is, to explain why groups of elements belong together based on the underlying structure of the network. We present Detangler, a system that supports visual analysis of group cohesion in multiplex networks through dual linked views. These views feature new data abstractions derived from the original multiplex network: the substrate network and the catalyst network. We contribute two novel techniques that allow the user to analyze the complex structure of the multiplex network without the extreme visual clutter that would result from simply showing it directly. The harmonized layout visual encoding technique provides spatial stability between the substrate and catalyst views. The pivot brushing interaction technique supports linked highlighting between the views based on computations in the underlying multiplex network to leapfrog between subsets of catalysts and substrates. We present results from the motivating application domain of annotated news documents with a usage scenario and preliminary expert feedback. A second usage scenario presents group cohesion analysis of the social network of the early American independence movement.
    BibTeX
    @article {10.1111:cgf.12644,
    journal = {Computer Graphics Forum},
    title = {{Detangler: Visual Analytics for Multiplex Networks}},
    author = {Renoust, Benjamin and Melancon, Guy and Munzner, Tamara},
    year = {2015},
    publisher = {The Eurographics Association and John Wiley & Sons Ltd.},
    DOI = {10.1111/cgf.12644}
    }
    URI
    http://dx.doi.org/10.1111/cgf.12644
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    • 34-Issue 3
    • EuroVis15: Eurographics Conference on Visualization

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    Eurographics Association copyright © 2013 - 2023 
    Send Feedback | Contact - Imprint | Data Privacy Policy | Disable Google Analytics
    Theme by @mire NV
    System hosted at  Graz University of Technology.
    TUGFhA