Description Usage Arguments Value References Examples
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}.
1 |
A |
a list of length N containing (M\times M) adjacency matrices. |
out.dist |
a logical; |
type |
type of target structure. One of |
a named list containing
an (N\times N) matrix or dist
object containing pairwise distance measures.
an (N\times M-1) matrix where each row is top-M-1 vibrational spectra.
wilson_study_2008NetworkDistance
1 2 3 4 5 6 7 8 9 10 11 12 | ## 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)
|
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