  The field of complex networks studies a wide variety of interacting systems
by representing them as networks. To understand their properties and mutual
relations, the randomisation of network connections is a commonly used tool.
However, information theoretic-based randomisation methods with
well-established foundations mostly provide a stationary description of these
systems, while stochastic randomisation methods that account for their dynamic
nature lack such general foundations and require extensive repetition of the
stochastic process to measure statistical properties. In this work, we extend
the applicability of information-theoretic methods beyond stationary network
models. By using the information-theoretic principle of maximum caliber we
construct dynamic network ensemble distributions based on constraints
representing statistical properties with known values throughout the evolution.
We focus on the particular cases of dynamics constrained by the average number
of connections of the whole network and each node, comparing each evolution to
simulations of stochastic randomisation that obey the same constraints. We find
that ensemble distributions estimated from simulations match those calculated
with maximum caliber and that the equilibrium distributions to which they
converge agree with known results of maximum entropy given the same
constraints. Finally, we discuss further the connections to other maximum
entropy approaches to network dynamics and conclude by proposing some possible
avenues of future research.
