rand.eff: Efficiency for simulated graphs

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

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.

Examples

Run this code


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