blockmodel | R Documentation |
Given a set of equivalence classes (in the form of an equiv.clust
object, hclust
object, or membership vector) and one or more graphs, blockmodel
will form a blockmodel of the input graph(s) based on the classes in question, using the specified block content type.
blockmodel(dat, ec, k=NULL, h=NULL, block.content="density", plabels=NULL, glabels=NULL, rlabels=NULL, mode="digraph", diag=FALSE)
dat |
one or more input graphs. |
ec |
equivalence classes, in the form of an object of class |
k |
the number of classes to form (using |
h |
the height at which to split classes (using |
block.content |
string indicating block content type (see below). |
plabels |
a vector of labels to be applied to the individual nodes. |
glabels |
a vector of labels to be applied to the graphs being modeled. |
rlabels |
a vector of labels to be applied to the (reduced) roles. |
mode |
a string indicating whether we are dealing with graphs or digraphs. |
diag |
a boolean indicating whether loops are permitted. |
Unless a vector of classes is specified, blockmodel
forms its eponymous models by using cutree
to cut an equivalence clustering in the fashion specified by k
and h
. After forming clusters (roles), the input graphs are reordered and blockmodel reduction is applied. Currently supported reductions are:
density
: block density, computed as the mean value of the block
meanrowsum
: mean row sums for the block
meancolsum
: mean column sums for the block
sum
: total block sum
median
: median block value
min
: minimum block value
max
: maximum block value
types
: semi-intelligent coding of blocks by “type.” Currently recognized types are (in order of precedence) “NA
” (i.e., blocks with no valid data), “null” (i.e., all values equal to zero), “complete” (i.e., all values equal to 1), “1 covered” (i.e., all rows/cols contain a 1), “1 row-covered” (i.e., all rows contain a 1), “1 col-covered” (i.e., all cols contain a 1), and “other” (i.e., none of the above).
Density or median-based reductions are probably the most interpretable for most conventional analyses, though type-based reduction can be useful in examining certain equivalence class hypotheses (e.g., 1 covered and null blocks can be used to infer regular equivalence classes). Once a given reduction is performed, the model can be analyzed and/or expansion can be used to generate new graphs based on the inferred role structure.
An object of class blockmodel
.
Carter T. Butts buttsc@uci.edu
Doreian, P.; Batagelj, V.; and Ferligoj, A. (2005). Generalized Blockmodeling. Cambridge: Cambridge University Press.
White, H.C.; Boorman, S.A.; and Breiger, R.L. (1976). “Social Structure from Multiple Networks I: Blockmodels of Roles and Positions.” American Journal of Sociology, 81, 730-779.
equiv.clust
, blockmodel.expand
#Create a random graph with _some_ edge structure g.p<-sapply(runif(20,0,1),rep,20) #Create a matrix of edge #probabilities g<-rgraph(20,tprob=g.p) #Draw from a Bernoulli graph #distribution #Cluster based on structural equivalence eq<-equiv.clust(g) #Form a blockmodel with distance relaxation of 10 b<-blockmodel(g,eq,h=10) plot(b) #Plot it
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