prefer to view part or all of the network at varying levels of detail. Hierarchical clustering provides a framework for viewing the network at different levels of detail by superimposing a hierarchy on it. Nodes are grouped into clusters, and clusters are themselves placed into other clusters. Users can then navigate these clusters until an appropriate level of detail is reached. This article describes an experiment comparing two methods for viewing hierarchically clustered networks. Traditional full-zoom techniques provide details of only the current level of the hierarchy. In contrast, fisheye views, generated by the "variable-zoom" algorithm described in this article, provide information about higher levels as well. Subjects using both viewing methods were given problem-solving tasks requiring them to navigate a network, in this case, a simulated telephone system, and to reroute links in it. Results suggest that the greater context provided by fisheye views significantly improved user performance. Users were quicker to complete their task and made fewer unnecessary navigational steps through the hierarchy. This validation of fisheye views is important for designers of interfaces to complicated monitoring systems, such as control rooms for supervisory control and data acquisition systems, where efficient human performance is often critical. However, control room operators remained concerned about the size and visibility tradeoffs between the fine detail provided by full-zoom techniques and the global context supplied by fisheye views. Specific interface features are required to reconcile the differences.
Schaffer et al REF compare full-zoom navigation techniques and the fisheye view for drawing clustered graphs.
Navigating hierarchically clustered networks through fisheye and full-zoom methods
TCHI
Computer Science