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

In brainwaver: Basic wavelet analysis of multivariate time series with a visualisation and parametrisation using graph theory.

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.

Author(s)

S. Achard

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.

See Also

equadist.rand.sw,reg.sw

Examples

mat<-sim.rand(8,20)

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

brainwaver documentation built on May 2, 2019, 10:23 a.m.