StochBlock-package: StochBlock: Stochastic Blockmodeling of One-Mode and Linked...

In StochBlock: Stochastic Blockmodeling of One-Mode and Linked Networks

StochBlock: Stochastic Blockmodeling of One-Mode and Linked Networks

Description

Stochastic blockmodeling of one-mode and linked networks as presented in Škulj and Žiberna (2022) \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1016/j.socnet.2022.02.001")}. The optimization is done via CEM (Classification Expectation Maximization) algorithm that can be initialized by random partitions or the results of k-means algorithm. The development of this package is financially supported by the Slovenian Research Agency (https://www.arrs.si/) within the research programs P5-0168 and the research projects J7-8279 (Blockmodeling multilevel and temporal networks) and J5-2557 (Comparison and evaluation of different approaches to blockmodeling dynamic networks by simulations with application to Slovenian co-authorship networks).

Author(s)

Maintainer: Aleš Žiberna [e-mail] (ORCID)

Other contributors:

• Fabio Ashtar Telarico [e-mail] (ORCID) [contributor]

References

Škulj, D., & Žiberna, A. (2022). Stochastic blockmodeling of linked networks. Social Networks, 70, 240-252, \Sexpr[results=rd]{tools:::Rd_expr_doi("10.1371/journal.pcbi.1005697")}.

See Also

stochBlockORP

stochBlock

Examples

# Simple one-mode network
library(blockmodeling)
k<-2
blockSizes<-rep(20,k)
IM<-matrix(c(0.8,.4,0.2,0.8), nrow=2)
if(any(dim(IM)!=c(k,k))) stop("invalid dimensions")

set.seed(2021)
clu<-rep(1:k, times=blockSizes)
n<-length(clu)
M<-matrix(rbinom(n*n,1,IM[clu,clu]),ncol=n, nrow=n)
diag(M)<-0
plotMat(M)

resORP<-stochBlockORP(M,k=2, rep=10, return.all = TRUE)
resORP$ICL
plot(resORP)
clu(resORP)


# Linked network
library(blockmodeling)
set.seed(2021)
IM<-matrix(c(0.8,.4,0.2,0.8), nrow=2)
clu<-rep(1:2, each=20)
n<-length(clu)
nClu<-length(unique(clu))
M1<-matrix(rbinom(n^2,1,IM[clu,clu]),ncol=n, nrow=n)
M2<-matrix(rbinom(n^2,1,IM[clu,clu]),ncol=n, nrow=n)
M12<-diag(n)
nn<-c(n,n)
k<-c(2,2)
Ml<-matrix(0, nrow=sum(nn),ncol=sum(nn))
Ml[1:n,1:n]<-M1
Ml[n+1:n,n+1:n]<-M2
Ml[n+1:n, 1:n]<-M12
plotMat(Ml)

resMl<-stochBlockORP(M=Ml, k=k, n=nn, rep=10)
resMl$ICL
plot(resMl)
clu(resMl)

StochBlock documentation built on July 25, 2025, 3 a.m.