Time evolving Random Network Models are presented as a mathematical framework for\nmodelling and analyzing the evolution of complex networks. This framework allows the analysis\nover time of several network characterizing features such as link density, clustering coefficient, degree\ndistribution, as well as entropy-based complexity measures, providing new insight on the evolution\nof random networks. First, some simple dynamic network models, based only on edge density, are\nanalyzed to serve as a baseline reference for assessing more complex models. Then, a model that\ndepends on network structure with the aim of reflecting some characteristics of real networks is also\nanalyzed. Such model shows a more sophisticated behavior with two different regimes, one of them\nleading to the generation of high clustering coefficient/link density ratio values when compared\nwith the baseline values, as it happens in many real networks. Simulation examples are discussed to\nillustrate the behavior of the proposed models.
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