gclust.centralgraph {sna} | R Documentation |
Calculates central graphs associated with particular graph clusters (as indicated by the k
partition of h
).
gclust.centralgraph(h, k, mats, ...)
h |
An hclust object, the based on a graph set |
k |
The number of groups to evaluate |
mats |
A graph stack containing the adjacency matrices for the graphs on which the clustering was performed |
... |
Additional arguments to centralgraph |
gclust.centralgraph
uses cutree
to cut the hierarchical clustering in h
into k
groups. centralgraph
is then called on each cluster, and the results are returned as a graph stack. This is a useful tool for interpreting clusters of (labeled) graphs, with the resulting central graphs being subsequently analyzed using standard SNA methods.
An array containing the stack of central graph adjacency matrices
Carter T. Butts buttsc@uci.edu
Butts, C.T., and Carley, K.M. (2001). ``Multivariate Methods for Interstructural Analysis.'' CASOS working paper, Carnegie Mellon University.
hclust
, centralgraph
, gclust.boxstats
, gdist.plotdiff
, gdist.plotstats
#Create some random graphs g<-rgraph(10,20,tprob=c(rbeta(10,15,2),rbeta(10,2,15))) #Find the Hamming distances between them g.h<-hdist(g) #Cluster the graphs via their Hamming distances library(mva) #Load the mva library g.c<-hclust(as.dist(g.h)) #Now find central graphs by cluster for a two cluster solution g.cg<-gclust.centralgraph(g.c,2,g) #Plot the central graphs gplot(g.cg[1,,]) gplot(g.cg[2,,])