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Multivariate Network Visualization pp 151–174Cite as

Temporal Multivariate Networks

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

Abstract

Networks that evolve over time, or dynamic graphs, have been of interest to the areas of information visualization and graph drawing for many years. Typically, the structure of the dynamic graph evolves as vertices and edges are added or removed from the graph. In a multivariate scenario, however, attributes play an important role and can also evolve over time. In this chapter, we characterize and survey methods for visualizing temporal multivariate networks. We also explore future applications and directions for this emerging area in the fields of information visualization and graph drawing.

Keywords

  • IEEE Transaction
  • Software Engineering
  • Computer Graphic
  • Large Graph
  • Information Visualization

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Authors
  1. Daniel Archambault
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  2. James Abello
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  3. Jessie Kennedy
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  4. Stephen Kobourov
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  5. Kwan-Liu Ma
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  6. Silvia Miksch
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  7. Chris Muelder
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  8. Alexandru C. Telea
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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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Archambault, D. et al. (2014). Temporal Multivariate Networks. 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_8

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