nd_wsd: Distance with Weighted Spectral Distribution
In NetworkDistance: Distance Measures for Networks

Description Usage Arguments Value References Examples

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.

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

`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.

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.

\insertRef

fay_weighted_2010NetworkDistance

## 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)