graph.mult.scaling: Multidimensional scaling of graphs

In statGraph: Statistical Methods for Graphs

Description

graph.mult.scaling performs multidimensional scaling of graphs. It takes the Jensen-Shannon divergence between graphs (JS) and uses the 'cmdscale' function from the 'stats' package to obtain a set of points such that the distances between the points are similar to JS.

Usage

graph.mult.scaling(
  G,
  plot = TRUE,
  bandwidth = "Silverman",
  type = "n",
  main = "",
  ...
)

Arguments

G	a list of undirected graphs (igraph type) or their adjacency matrices. The adjacency matrix of an unweighted graph contains only 0s and 1s, while the weighted graph may have nonnegative real values that correspond to the weights of the edges.
plot	logical. If TRUE (default) the points chosen to represent the Jensen-Shannon divergence between graphs are plotted.
bandwidth	string showing which criterion is used to choose the bandwidth during the spectral density estimation. Choose between the following criteria: "Silverman" (default), "Sturges", "bcv", "ucv" and "SJ". "bcv" is an abbreviation of biased cross-validation, while "ucv" means unbiased cross-validation. "SJ" implements the methods of Sheather & Jones (1991) to select the bandwidth using pilot estimation of derivatives.
type	what type of plot should be drawn. The defaut value is '"n"', which indicates that the points will not be plotted (i. e. only the labels of the graphs will be plotted).
main	title of the plot (default value is "").
...	additional plotting parameters. See 'plot' function from the 'graphics' package for the complete list.

Value

A matrix in which each column corresponds to a coordinate and each row corresponds to a graph (point). Then, each row gives the coordinates of the points chosen to represent the Jensen-Shannon divergence between graphs.

References

Takahashi, D. Y., Sato, J. R., Ferreira, C. E. and Fujita A. (2012) Discriminating Different Classes of Biological Networks by Analyzing the Graph Spectra Distribution. _PLoS ONE_, *7*, e49949. doi:10.1371/journal.pone.0049949.

Silverman, B. W. (1986) _Density Estimation_. London: Chapman and Hall.

Sturges, H. A. The Choice of a Class Interval. _J. Am. Statist. Assoc._, *21*, 65-66.

Sheather, S. J. and Jones, M. C. (1991). A reliable data-based bandwidth selection method for kernel density estimation. _Journal of the Royal Statistical Society series B_, 53, 683-690. http://www.jstor.org/stable/2345597.

Examples

set.seed(1)
G <- list()
for (i in 1:5) {
  G[[i]] <- igraph::sample_gnp(50, 0.5)
}
for (i in 6:10) {
  G[[i]] <- igraph::sample_smallworld(1, 50, 8, 0.2)
}
for (i in 11:15) {
  G[[i]] <- igraph::sample_pa(50, power = 1, directed = FALSE)
}
graph.mult.scaling(G)

statGraph documentation built on May 19, 2021, 9:11 a.m.