Graph visualization has many areas of application. Systems which can be represented as graphs include hierarchies of files, taxonomies of species, Web site maps, evolutionary trees, and genetic maps. As the information is not always purely hierarchical, more general graphs than trees need to be studied. A key issue when visualising graphs in information visualization is the size of the data. Many applications require analysis of graphs with several thousand nodes and edges.
Large graphs pose several difficult problems.
CWI has developed innovative techniques to navigate, to filter, and to create abstractions from such graphs, in order to make them usable in practice. A key feature is the use of metrics, as a measure associated with a node or an edge. The idea is to define the relative importance of a node or an edge with respect to some semantics. Such metric values can be translated into visual attributes like colour and saturation. A technique that we find useful renders an edge with continuously shaded colour reflecting the metric values of the nodes at its endpoints.