The functions computed the distances in terms of Structural equivalence (Lorrain and White, 1971) between the units of a onemode network. Several options for treating the diagonal values are supported.
1 2 3 
M 
A matrix representing the (usually valued) network. For now, only onerelational networks are supported. The network must be onemode. 
method 
The method used to compute distances  any of the methods allowed by functions dist, cor or cov all package::stats or just "cor" or "cov" (given as character). 
fun 
Which function should be used to compute distances (given as character), . 
fun.on.rows 
For nonstandard function  does the function compute measure on rows (such as 
handle.interaction 
How should the interaction between the vertices analysed be handled: 
use 
For use with methods "cor" and "cov", for other methods (the default option should be used if handle.interaction=="ignore"), "pairwise.complete.obs" are always used, if stats.dist.cor.cov=TRUE 
... 
Additional arguments to 
If both method
and fun
are "default", the euclidean distances are computed. the "default" method for fun="dist"
is "euclidean" and for fun="cor"
"pearson".
A matrix (usually of class dist) is returned.
Aleš Žiberna
Batagelj, V., Ferligoj, A., Doreian, P. (1992): Direct and indirect methods for structural equivalence. Social Networks 14, 6390.
Lorrain, F., White, H.C., 1971. Structural equivalence of individuals in social networks. Journal of Mathematical Sociology 1, 4980.
dist
, hclust
, REGE
, crit.fun
, opt.par
, opt.random.par
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15  #generating a simple network corresponding to the simple Sum of squares
#structural equivalence with blockmodel:
# null com
# null null
n<20
net<matrix(NA,ncol=n,nrow=n)
clu<rep(1:2,times=c(5,15))
tclu<table(clu)
net[clu==1,clu==1]<rnorm(n=tclu[1]*tclu[1],mean=0,sd=1)
net[clu==1,clu==2]<rnorm(n=tclu[1]*tclu[2],mean=4,sd=1)
net[clu==2,clu==1]<rnorm(n=tclu[2]*tclu[1],mean=0,sd=1)
net[clu==2,clu==2]<rnorm(n=tclu[2]*tclu[2],mean=0,sd=1)
D<sedist(M=net)
plot.mat(net, clu=cutree(hclust(d=D,method="ward"),k=2))

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