  The recently introduced concept of dynamic communicability is a valuable tool
for ranking the importance of nodes in a temporal network. Two metrics,
broadcast score and receive score, were introduced to measure the centrality of
a node with respect to a model of contagion based on time-respecting walks.
This article examines the temporal and structural factors influencing these
metrics by considering a versatile stochastic temporal network model. We
analytically derive formulae to accurately predict the expectation of the
broadcast and receive scores when one or more columns in a temporal edge-list
are shuffled. These methods are then applied to two publicly available
data-sets and we quantify how much the centrality of each individual depends on
structural or temporal influences. From our analysis we highlight two practical
contributions: a way to control for temporal variation when computing dynamic
communicability, and the conclusion that the broadcast and receive scores can,
under a range of circumstances, be replaced by the row and column sums of the
matrix exponential of a weighted adjacency matrix given by the data.
