NetworkDistance (version 0.3.6)

nd.wsd: Distance with Weighted Spectral Distribution

Description

Normalized Laplacian matrix contains topological information of a corresponding network via its spectrum. nd.wsd adopts weighted spectral distribution of eigenvalues and brings about a metric via binning strategy.

Usage

nd.wsd(A, out.dist = TRUE, K = 50, wN = 4)

Value

a named list containing

D

an \((N\times N)\) matrix or dist object containing pairwise distance measures.

spectra

an \((N\times M)\) matrix of rows being eigenvalues for each graph.

Arguments

A

a list of length \(N\) containing \((M\times M)\) adjacency matrices.

out.dist

a logical; TRUE for computed distance matrix as a dist object.

K

the number of bins for the spectrum interval \([0,2].\)

wN

a decaying exponent; default is \(4\) set by authors.

References

fay_weighted_2010NetworkDistance

Examples

## load example data and extract a few
data(graph20)
gr.small = graph20[c(1:5,11:15)]

## compute distance matrix
output = nd.wsd(gr.small, out.dist=FALSE, K=10)

## visualize
opar = par(no.readonly=TRUE)
par(pty="s")
image(output$D[,10:1], main="two group case", axes=FALSE, col=gray(0:32/32))
par(opar)