R: An R Package for Generalized and Classical Blockmodeling of...

blockmodeling-package {blockmodeling} | R Documentation

## An R Package for Generalized and Classical Blockmodeling of Valued Networks

### Description

This package is primarily meant as an implementation of Generalized blockmodeling. In addition, functions for computation of (dis)similarities in terms of structural and regular equivalence, plotting and other "utility" functions are provided.

### Author(s)

Aleš Žiberna

### References

ŽIBERNA, Aleš (2006): Generalized Blockmodeling of Valued Networks. Social Networks, Jan. 2007, vol. 29, no. 1, 105-126. http://dx.doi.org/10.1016/j.socnet.2006.04.002.

ŽIBERNA, Aleš. Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. J. math. sociol., 2008, vol. 32, no. 1, 57-84. http://www.informaworld.com/smpp/content?content=10.1080/00222500701790207.

DOREIAN, Patrick, BATAGELJ, Vladimir, FERLIGOJ, Anuška (2005): Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press, 2005. XV, 384 p., ISBN 0-521-84085-6.

White, D. R., K. P. Reitz (1983): "Graph and semigroup homomorphisms on networks of relations". Social Networks, 5, p. 193-234.

White, Douglas R.(2005): REGGE (web page). http://eclectic.ss.uci.edu/~drwhite/REGGE/ (12.5.2005).

### See Also

Packages: `sna` `network`

Functions inside this package: `crit.fun`, `opt.par`, `opt.random.par`, `opt.these.par`, `check.these.par`, `REGE`, `plot.mat`

### Examples

    n<-8 #if larger, the number of partitions increases dramaticaly,
    # as does if we increase the number of clusters
    net<-matrix(NA,ncol=n,nrow=n)
    clu<-rep(1:2,times=c(3,5))
    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)

    #we select a random parition and then optimise it

    all.par<-nkpartitions(n=n, k=length(tclu))
    #forming the partitions
    all.par<-lapply(apply(all.par,1,list),function(x)x[[1]])
    # to make a list out of the matrix

    #optimizing one partition
    res<-opt.par(M=net,
       clu=all.par[[sample(1:length(all.par),size=1)]],
       approach="ss", blocks="com")
    plot(res) #Hopefully we get the original partition

    #optimizing 10 random partitions which with opt.these.par
    res<-opt.these.par(M=net,
       partitions=all.par[sample(1:length(all.par),size=10)],
       approach="ss",blocks="com")
    plot(res) #Hopefully we get the original partition

    #optimizing 10 random partitions with opt.random.par
    res<-opt.random.par(M=net,k=2,rep=10,approach="ss",blocks="com")
    plot(res) #Hopefully we get the original partition

    #Checking all possible partitions
    nkpar(n=n, k=length(tclu)) #computing the number of partitions
    all.par<-nkpartitions(n=n, k=length(tclu))
    #forming the partitions
    all.par<-lapply(apply(all.par,1,list),function(x)x[[1]])
    # to make a list out of the matrix
    res<-check.these.par(M=net,partitions=all.par,approach="ss",
       blocks="com")
    plot(res) #we get the original partition

    #using indidect approach - structural equivalence
    D<-sedist(M=net)
    plot.mat(net, clu=cutree(hclust(d=D,method="ward"),k=2))

[Package blockmodeling version 0.1.9 Index]