brainwaver (version 1.6)

reg.sw: Small-world parameters for a lattice graph

Description

Computes the degree, the minimum path length and the clustering coefficient for a lattice graph.

Usage

reg.sw(n.nodes.rand, n.edges.rand, dist.mat)

Arguments

n.nodes.rand

number of nodes of the graph

n.edges.rand

number of edges of the graph

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.

See Also

rand.sw,equadist.rand.sw

Examples

mat<-sim.reg(8,20)
result<-reg.sw(8,20,dist.mat=matrix(1,8,8))