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An important source of high clustering coefficient in real-world networks istransitivity. However, existing approaches for modeling transitivity sufferfrom at least one of the following problems: i) they produce graphs from aspecific class like bipartite graphs, ii) they do not give an analyticalargument for the high clustering coefficient of the model, and iii) theirclustering coefficient is still significantly lower than real-world networks.In this paper, we propose a new model for complex networks which is based onadding transitivity to scale-free models. We theoretically analyze the modeland provide analytical arguments for its different properties. In particular,we calculate a lower bound on the clustering coefficient of the model which isindependent of the network size, as seen in real-world networks. More thantheoretical analysis, the main properties of the model are evaluatedempirically and it is shown that the model can precisely simulate real-worldnetworks from different domains with and different specifications.

Modeling Transitivity in Complex Networks