1 research outputs found
A model for generating tunable clustering coefficients independent of the number of nodes in scale free and random networks
Probabilistic networks display a wide range of high average clustering
coefficients independent of the number of nodes in the network. In particular,
the local clustering coefficient decreases with the degree of the subtending
node in a complicated manner not explained by any current models. While a
number of hypotheses have been proposed to explain some of these observed
properties, there are no solvable models that explain them all. We propose a
novel growth model for both random and scale free networks that is capable of
predicting both tunable clustering coefficients independent of the network
size, and the inverse relationship between the local clustering coefficient and
node degree observed in most networks