Dynamic Random Network Models are presented as a mathematical framework for modelling and analyzing the time evolution of complex networks. Such framework allows the time analysis of several network characterizing features such as link density, clustering coefficient, degree distribution, as well as entropy-based complexity measures, providing new insight on the evolution of random networks.  Some simple dynamic models are analyzed with the aim to provide several basic reference evolution behaviors. Inference issues from real data are also discussed, together with simulation examples, to illustrate the applicability of the proposed framework.