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.