  Here we present the entropic dynamics formalism for networks. That is, a
framework for the dynamics of graphs meant to represent a network derived from
the principle of maximum entropy and the rate of transition is obtained taking
into account the natural information geometry of probability distributions. We
apply this framework to the Gibbs distribution of random graphs obtained with
constraints on the node connectivity. The information geometry for this graph
ensemble is calculated and the dynamical process is obtained as a diffusion
equation. We compare the steady state of this dynamics to degree distributions
found on real-world networks.
