An important source of high clustering coefficient in real-world networks is
transitivity. However, existing approaches for modeling transitivity suffer
from at least one of the following problems: i) they produce graphs from a
specific class like bipartite graphs, ii) they do not give an analytical
argument for the high clustering coefficient of the model, and iii) their
clustering coefficient is still significantly lower than real-world networks.
In this paper, we propose a new model for complex networks which is based on
adding transitivity to scale-free models. We theoretically analyze the model
and provide analytical arguments for its different properties. In particular,
we calculate a lower bound on the clustering coefficient of the model which is
independent of the network size, as seen in real-world networks. More than
theoretical analysis, the main properties of the model are evaluated
empirically and it is shown that the model can precisely simulate real-world
networks from different domains with and different specifications.Comment: 16 pages, 4 figures, 3 table