blockmodeling-package {blockmodeling} | R Documentation |
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.
Aleš Žiberna
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
White, D. R., & Reitz, K. P. (1983). Graph and semigroup homomorphisms on networks of relations. Social Networks, 5(2), 193-234.
White, D. R. (2005). REGGE. Retrieved from http://eclectic.ss.uci.edu/~drwhite/REGGE/
Functions inside this package:
critFunC
, optParC
, optRandomParC
, REGE
, plot.mat
n <- 8 # If larger, the number of partitions increases dramatically, # 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 partition and then optimize it all.par <- nkpartitions(n = n, k = length(tclu)) # Forming the partitions all.par <- lapply(apply(all.par, 1, list), function(x) x[[1]]) # Optimizing one partition res <- optParC(M = net, clu = all.par[[sample(1:length(all.par), size = 1)]], approaches = "hom", homFun = "ss" , blocks = "com") plot(res) # Hopefully we get the original partition # Optimizing 10 random partitions with optRandomParC res <- optRandomParC(M = net, k = 2, rep = 10, approaches = "hom", homFun = "ss", blocks = "com") plot(res) # Hopefully we get the original partition # Using indirect approach - structural equivalence D <- sedist(M = net) plot.mat(net, clu = cutree(hclust(d = D, method = "ward"), k = 2))