Interaction in the visualization of multivariate networks

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.