Visualizing Dense Dynamic Networks with Matrix Cubes

Visualizing static networks is already difficult, but exploring dynamic networks is even more challenging due to the complexity of the tasks involved. Some require aggregation over time; others require observing the topology at one given time or tracking the evolution of edge weights. Due to this wide spectrum of tasks, one visual encoding will hardly fit all tasks effectively; multiple complementary views are needed. We introduce the Matrix Cube, a visualization and navigation model for dynamic networks that results from stacking adjacency matrices, one for each time step in the network. It builds on our familiarity with cubes in the physical world and offers intuitive ways to look at, manipulate and decompose them. We describe a set of direct manipulation operations to decompose the Matrix Cube and interact with the resulting views.