Abstract
The overall aim of visualization is to obtain insight into large amounts of data. Detection of patterns as well as outliers are typical examples. For networks, such patterns can be number and position of cliques; for multivariate data this can be the correlation between attributes. The major challenge of multivariate network visualization is to understand the interplay between properties of the network and its associated data, for instance to see if the formation of cliques can be understood from attributes of nodes. © 2014 Springer International Publishing.
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CITATION STYLE
Wybrow, M., Elmqvist, N., Fekete, J. D., Von Landesberger, T., Van Wijk, J. J., & Zimmer, B. (2014). Interaction in the visualization of multivariate networks. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 8380 LNCS, pp. 97–125). Springer Verlag. https://doi.org/10.1007/978-3-319-06793-3_6
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