Probabilistic networks display a wide range of high average clusteringcoefficients independent of the number of nodes in the network. In particular,the local clustering coefficient decreases with the degree of the subtendingnode in a complicated manner not explained by any current models. While anumber of hypotheses have been proposed to explain some of these observedproperties, there are no solvable models that explain them all. We propose anovel growth model for both random and scale free networks that is capable ofpredicting both tunable clustering coefficients independent of the networksize, and the inverse relationship between the local clustering coefficient andnode degree observed in most networks.
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