  Many types of pairwise interaction take the form of a fixed set of nodes with
edges that appear and disappear over time. In the case of discrete-time
evolution, the resulting evolving network may be represented by a time-ordered
sequence of adjacency matrices. We consider here the issue of representing the
system as a single, higher dimensional block matrix, built from the individual
time-slices. We focus on the task of computing network centrality measures.
From a modeling perspective, we show that there is a suitable block formulation
that allows us to recover dynamic centrality measures respecting time's arrow.
From a computational perspective, we show that the new block formulation leads
to the design of more effective numerical algorithms.
