Morteza Haghir Chehreghani, Mostafa Haghir Chehreghani
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 realworld
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.
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