Get Central Graphs Associated with Graph Clusters
Calculates central graphs associated with particular graph clusters (as indicated by the k partition of h).
gclust.centralgraph(h, k, dat, ...)
h |
an |
k |
the number of groups to evaluate. |
dat |
one or more graphs (on which the clustering was performed). |
... |
additional arguments to |
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.
#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 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,,])
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