rand.sw: Small-world parameters for simulated random graphs

Description

Computes the degree, the minimum path length and the clustering coefficient for simulated random graphs.

Usage

rand.sw(nsim, n.nodes.rand, n.edges.rand, dist.mat, dat = "reduced")

Arguments

nsim

number of simulated graphs to use for the computation of the small-world parameters.

dat

character string specifying if all the small-world parameters have to be returned. If "reduced", only the mean of the parameters for the whole graph is returned.

n.nodes.rand

number of nodes of the simulated graphs

n.edges.rand

number of edges of the simulated graphs

dist.mat

matrix with a distance associated to each pair of nodes of the graph to take into account in the computation of the small-world parameters.

Value

in.degree: mean of the degree for the whole graph.
Lp.rand: mean of the minimum path length for the whole graph.
Cp.rand: mean of the clustering coefficient for the whole graph.
in.degree.all: vector of the degree of each node of the graph.
Lp.rand.all: vector of the minimum path length of each node of the graph.
Cp.rand.all: vector of the clustering coefficient of each node of the graph.

References

S. H. Strogatz (2001) Exploring complex networks. Nature, Vol. 410, pages 268-276.

S. Achard, R. Salvador, B. Whitcher, J. Suckling, Ed Bullmore (2006) A Resilient, Low-Frequency, Small-World Human Brain Functional Network with Highly Connected Association Cortical Hubs. Journal of Neuroscience, Vol. 26, N. 1, pages 63-72.

Examples

Run this code

mat<-sim.rand(8,20)

result<-rand.sw(10,8,20,dist.mat=matrix(1,8,8))