Temporal multivariate networks

Daniel Archambault, James Abello, Jessie Kennedy, Stephen Kobourov, Kwan Liu Ma, Silvia Miksch, Chris Muelder, Alexandru C. Telea

Research output: Chapter in Book/Report/Conference proceedingConference contribution

18 Scopus citations

Abstract

In previous chapters, this book has primarily concerned itself with visualization methods for static, multivariate graphs. In a static scenario, the network has a number of attributes associated with its elements. These attribute values remain fixed and the challenge is to visualize the interactions between the network(s) and these attributes. Static multivariate graphs could be viewed as graphs with an associated high dimensional data set linked to its elements. Time is simply another dimension in this multivariate data set that can interact with the vertices, edges, and attribute values of the network. However, humans perceive time differently as we know from our everyday interactions with the physical world. Thus, intuitively, this dimension is often handled differently when supporting the presentation of data that changes over time. Visualization applications and techniques have, and probably should, continue to exploit this fact, allowing for effective visualization methods of temporal multivariate graphs.

Original languageEnglish (US)
Title of host publicationMultivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions
PublisherSpringer-Verlag
Pages151-174
Number of pages24
ISBN (Print)9783319067926
DOIs
StatePublished - Jan 1 2014
Event3rd Dagstuhl Seminar on Information Visualization - Towards Multivariate Network Visualization - Saarland, Germany
Duration: May 12 2013May 17 2013

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume8380 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other3rd Dagstuhl Seminar on Information Visualization - Towards Multivariate Network Visualization
CountryGermany
CitySaarland
Period5/12/135/17/13

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Archambault, D., Abello, J., Kennedy, J., Kobourov, S., Ma, K. L., Miksch, S., Muelder, C., & Telea, A. C. (2014). Temporal multivariate networks. In Multivariate Network Visualization - Dagstuhl Seminar #13201, Revised Discussions (pp. 151-174). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 8380 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-06793-3_8