Current systems for viewing network activity do not work well on mobile, dynamic, ad-hoc networks with varying topologies that are increasingly common in the world around us.  Better viewing tools are required that provide a greater understanding of network behavior as the network dynamically evolves. 

Our research has developed a suite of intuitive, continuous and visually persistent representations that are robust in the presence of messy and changing network data.  We provide a fully integrated and linked set of higher-level task-specific views, along with lower-level detail views.

Most network viewing systems focus on showing the basic node-link network data in a diagrammatic fashion or embedded in a representation of the battle space.  This will overwhelm the user with visual clutter for all but the smallest of networks.  In contrast, our system is designed to scale well with increasing network size and topological complexity, by reducing the dependencies on showing high-frequency clutter.  It also allows the user to seamlessly switch between different logical views of the network depending on the task needs of the user(s).

There are numerous potential applications for this technology, including: network simulation and modeling, network monitoring and analysis, intelligence analysis (financial, social networks) and traffic management.