rand.eff: Efficiency for simulated graphs

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

Description

Computes the local, global efficiency and cost for simulated random and regular graphs.

Usage

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

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 efficiency values.

Value

eff	global efficiency for the whole graph
loc	local efficiency for th whole graph
cost	cost for th whole graph

Note

only in version 1.2 and after

Author(s)

S. Achard

References

V. Latora, M. Marchiori (2001) Efficient Behavior of Small-World Networks. Phys. Rev. Lett., Vol. 87, N. 19, pages 1-4.

V. Latora, and M. Marchiori (2003) Economic Small-World Behavior in Weighted Networks. Europ. Phys. Journ. B, Vol. 32, pages 249-263.

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

const.adj.list, small.world

Examples

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

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