Visualization of large graphs usually invokes simplification, aggregation, or pruning of substructures in order to reduce the visual complexity and to provide an informative overview to the user. This is especially true if additional data is visualized on top of or along with the graph, es e.g. flow values on the edges or node labels.
The best visualization of a graph crucially depends on the considered application. Often, a single graph view is not sufficient to provide the information content the user desires. So for comprehensive visual analytics, it is beneficial to provide different graph layouts for the user to choose from, as well as the possibility to specify
focus regions or to only visualize user-selected subgraphs.
But this level of flexibility poses a real challenge with respect to concistency between different graph views.
Our main goal is to develop new algorithms that allow user-customized graph views and smooth transitions between different types of graph visualizations.