statGraph (version 0.5.1)

graph.hclust: Hierarchical cluster analysis on a list of graphs.

Description

Given a list of graphs, graph.hclust builds a hierarchy of clusters according to the Jensen-Shannon divergence between graphs.

Usage

graph.hclust(G, k, method = "complete", bandwidth = "Silverman")

Value

A list containing:

hclust

an object of class *hclust* which describes the tree produced by the clustering process.

cluster

the clustering labels for each graph.

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.

k

the number of clusters.

method

the agglomeration method to be used. This should be (an unambiguous abbreviation of) one of '"ward.D"', '"ward.D2"', '"single"', '"complete"', '"average"' (= UPGMA), '"mcquitty"' (= WPGMA), '"median"' (= WPGMC) or '"centroid"' (= UPGMC).

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.

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.hclust(G, 3)