Multivariate Graph Drawing using Parallel Coordinate Visualisations

Graph drawing is increasingly considering the embedding and drawing of multivariate or highly attributed graphs. The direct application of classical layout methods is difficult due to limited space and encoding options as the number of attributes (dimensions) related to nodes of information increases. Data from domains including bioinformatics (metabolic networks, protein-protein interaction) and social science (social networks, phone-call networks, disease transmission networks) consists of relational data which also possess a large number of individual attributes. Here we present our visualisation method featuring a combination of a graph drawing coupled with an adapted parallel coordinates visualisation. This technique makes the relations between multivariate data explicit, while preserving the expressiveness of existing techniques. These layout methods are implemented in an interactive Java-based visualisation tool. Examples of the use of this technique are shown with their application to interactive visual data analysis of a social network data set.