nd.wsd | R Documentation |
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.
nd.wsd(A, out.dist = TRUE, K = 50, wN = 4)
A |
a list of length |
out.dist |
a logical; |
K |
the number of bins for the spectrum interval |
wN |
a decaying exponent; default is |
a named list containing
an (N\times N)
matrix or dist
object containing pairwise distance measures.
an (N\times M)
matrix of rows being eigenvalues for each graph.
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)
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