  A model of correlated random networks is examined, i.e. networks with
correlations between the degrees of neighboring nodes. These nodes do not
necessarily have to be direct neighbors, the maximum range of the correlations
can be arbitrarily chosen. Two different methods for the creation of such
networks are presented: one of them is a generalization of a well-known
algorithm by Maslov and Sneppen. The percolation threshold for the model is
calculated and the result is tested using analytically solvable examples and
simulations. In the end the principal importance of correlations and clustering
for the topology of networks is discussed. Using a straight-forward extension
of the network model by Barabasi and Albert, it is shown how a
clustering-coefficient independent of the network size can originate in growing
networks.
