Using the network random generation models from (Gustedt 2009), we simulate and analyze several characteristics (such as the number of components, the degree sequence and the clustering coefficient) of the generated networks. This is done for a variety of distributions (fixed value, Bernoulli, Poisson, binomial) that are used to control the parameters of generation process. These parameters are in particular the size of newly appearing sets of objects, the number of contexts in which new elements appear initially, the number of objects that are shared with 'parent' contexts, and, the time period inside which a context may serve as a parent context 'aging'. The results show that these models allow to fine-tune the generation process such that the graphs adopt properties as they can be found in real world graphs.