Visualizing static networks is already difficult, but exploring dynamic
networks is even more challenging due to the complexity of the tasks
involved; one visual encoding will hardly fit all tasks effectively, hence
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 operations to
decompose the Matrix Cube and interact with the resulting views.