blockmodeling {blockmodeling}

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

Doreian, P., Batagelj, V., & Ferligoj, A. (2005). Generalized blockmodeling, (Structural analysis in the social sciences, 25). Cambridge [etc.]: Cambridge University Press.

Žiberna, A. (2007). Generalized Blockmodeling of Valued Networks. Social Networks, 29(1), 105-126. doi: 10.1016/j.socnet.2006.04.002

Žiberna, A. (2008). Direct and indirect approaches to blockmodeling of valued networks in terms of regular equivalence. Journal of Mathematical Sociology, 32(1), 57-84. doi: 10.1080/00222500701790207

Žiberna, A. (2014). Blockmodeling of multilevel networks. Social Networks, 39(1), 46-61. doi: 10.1016/j.socnet.2014.04.002

See Also

optRandomParC, critFunC, optParC, IM, clu, err, plotMat

Examples

#Generating a simple network corresponding to the simple Sum of squares
# Structural equivalence with blockmodel:
# nul com
# nul nul
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)

# Computation of criterion function with the correct partition
res <- critFunC(M = net, clu = clu, approaches = "hom", homFun = "ss", blocks = "com")
res$err # The error is relatively small
plot(res)

# Computation of criterion function with the correct partition and correct pre-specified blockmodel
# Prespecified blockmodel used
# nul com
# nul nul
B <- array(NA, dim = c(1, 1, 2, 2))
B[1, 1, , ] <- "nul"
B[1, 1, 1, 2] <- "com"
B[1, 1, , ]
res <- critFunC(M = net, clu = clu, approaches = "hom", homFun = "ss", blocks = B)
err(res) # The error is relatively small
IM(res)
plot(res)

# Computation of criterion function with the correct partition
# and pre-specified blockmodel with some alternatives
# Prespecified blockmodel used
# nul nul|com
# nul nul
B <- array(NA, dim = c(2, 2, 2))
B[1, , ] <- "nul"
B[2, 1, 2] <- "com"
res <- critFunC(M = net, clu = clu, approaches = "hom", homFun = "ss", blocks = B)
err(res) # The error is relatively small
IM(res)
plot(res)


# Optimizing a very bad partition
cluStart <- rep(1:2, times = 10)
res <- optParC(M = net,
               clu = cluStart,
               approaches = "hom", homFun = "ss", blocks = "com")
clu(res) # Hopefully we get the original partition)
err(res) 
plot(res) 

# Optimizing 10 random chosen partitions with optRandomParC
res <- optRandomParC(M = net, k = 2, rep = 10,
approaches = "hom", homFun = "ss", blocks = "com")
clu(res) # Hopefully we get the original partition)
err(res) 
plot(res)

# Adapt network for Valued blockmodeling with the same model
net[net > 4] <- 4
net[net < 0] <- 0

# Computation of criterion function with the correct partition
res <- critFunC(M = net, clu = clu, approaches = "val",
                blocks = c("nul", "com"), preSpecM = 4)
err(res) # The error is relatively small
IM(res)
# The image corresponds to the one used for generation of
# The network
plot(res)