graph.hclust {statGraph} | R Documentation |
Given a list of graphs, graph.hclust
builds a hierarchy of clusters
according to the Jensen-Shannon divergence between graphs.
graph.hclust(Graphs, k, clus_method = "complete", dist = "JS", ...)
Graphs |
a list of undirected graphs.
If each graph has the attribute |
k |
the number of clusters. |
clus_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). |
dist |
string indicating if you want to use the "JS" (default), "L1" or "L2" distances. "JS" means Jensen-Shannon divergence. |
... |
Other relevant parameters for |
A list containing:
hclust: |
an object of class |
cluster: |
the clustering labels for each graph. |
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.
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)