Tuning clustering in random networks with arbitrary degree distributions
M Ángeles Serrano, M Boguná - … Review E—Statistical, Nonlinear, and Soft …, 2005 - APS
We present a generator of random networks where both the degree-dependent clustering
coefficient and the degree distribution are tunable. Following the same philosophy as in the
configuration model, the degree distribution and the clustering coefficient for each class of
nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding
topologies by applying first a closure of triangles and second the classical closure of
remaining free stubs. The procedure unveils an universal relation among clustering and …
coefficient and the degree distribution are tunable. Following the same philosophy as in the
configuration model, the degree distribution and the clustering coefficient for each class of
nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding
topologies by applying first a closure of triangles and second the classical closure of
remaining free stubs. The procedure unveils an universal relation among clustering and …
Tuning clustering in random networks with arbitrary degree distributions
MÁMÁ Serrano Moral, M Boguñá - Physical Review E, 2005, vol. 72 …, 2005 - diposit.ub.edu
We present a generator of random networks where both the degree-dependent clustering
coefficient and the degree distribution are tunable. Following the same philosophy as in the
configuration model, the degree distribution and the clustering coefficient for each class of
nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding
topologies by applying first a closure of triangles and second the classical closure of
remaining free stubs. The procedure unveils an universal relation among clustering and …
coefficient and the degree distribution are tunable. Following the same philosophy as in the
configuration model, the degree distribution and the clustering coefficient for each class of
nodes of degree k are fixed ad hoc and a priori. The algorithm generates corresponding
topologies by applying first a closure of triangles and second the classical closure of
remaining free stubs. The procedure unveils an universal relation among clustering and …
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