  In statistical physics any given system can be either at an equilibrium or
away from it. Networks are not an exception. Most network models can be
classified as either equilibrium or growing. Here we show that under certain
conditions there exists an equilibrium formulation for any growing network
model, and vice versa. The equivalence between the equilibrium and
nonequilibrium formulations is exact not only asymptotically, but even for any
finite system size. The required conditions are satisfied in random geometric
graphs in general and causal sets in particular, and to a large extent in some
real networks.
