NetworkDistance (version 0.3.6)

nd.dsd: Discrete Spectral Distance

Description

Discrete Spectral Distance (DSD) is defined as the Euclidean distance between the spectra of various matrices, such as adjacency matrix \(A\)("Adj"), (unnormalized) Laplacian matrix \(L=D-A\)("Lap"), signless Laplacian matrix \(|L|=D+A\)("SLap"), or normalized Laplacian matrix \(\tilde{L}=D^{-1/2}LD^{-1/2}\).

Usage

nd.dsd(A, out.dist = TRUE, type = c("Lap", "SLap", "NLap", "Adj"))

Value

a named list containing

D

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

spectra

an \((N\times M-1)\) matrix where each row is top-\(M-1\) vibrational spectra.

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.

type

type of target structure. One of "Lap","SLap","NLap","Adj" as defined above.

References

wilson_study_2008NetworkDistance

Examples

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

## compute distance matrix
output <- nd.dsd(gr.small, out.dist=FALSE)

## 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)
# }