nd_dsd: Discrete Spectral Distance

In kisungyou/NetworkDistance: Distance Measures for Networks

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"))

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.

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.

References

\insertRef

wilson_study_2008NetworkDistance

Examples

## 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)

kisungyou/NetworkDistance documentation built on Aug. 23, 2023, 8:53 p.m.