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Scalability Considerations for Multivariate Graph Visualization

  • Chapter
Multivariate Network Visualization

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 8380))

Abstract

Real-world, multivariate datasets are frequently too large to show in their entirety on a visual display. Still, there are many techniques we can employ to show useful partial views-sufficient to support incremental exploration of large graph datasets. In this chapter, we first explore the cognitive and architectural limitations which restrict the amount of visual bandwidth available to multivariate graph visualization approaches. These limitations afford several design approaches, which we systematically explore. Finally, we survey systems and studies that exhibit these design strategies to mitigate these perceptual and architectural limitations.

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Authors
  1. T. J. Jankun-Kelly
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  2. Tim Dwyer
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  3. Danny Holten
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  4. Christophe Hurter
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  5. Martin Nöllenburg
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  6. Chris Weaver
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  7. Kai Xu
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Editors and Affiliations

  1. Faculty of Technology (FTK), Department of Computer Science, Linnaeus University, Vejdes Plats 7, 351 95, Växjö, Sweden

    Andreas Kerren

  2. School of Computing Science, University of Glasgow, 18 Lilybank Gardens, G12 1RZ, Glasgow, UK

    Helen C. Purchase

  3. Department of Computer Science, Worcester Polytechnic Institute, 100 Institute Road, 01609-2280, Worcester, MA, USA

    Matthew O. Ward

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Jankun-Kelly, T.J. et al. (2014). Scalability Considerations for Multivariate Graph Visualization. In: Kerren, A., Purchase, H.C., Ward, M.O. (eds) Multivariate Network Visualization. Lecture Notes in Computer Science, vol 8380. Springer, Cham. https://doi.org/10.1007/978-3-319-06793-3_10

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