This vignette describes the two implemented methods for blockmodeling in signed networks.

In signed blockmodeling, the goal is to determine `k`

blocks of nodes such that all intra-block edges are positive and inter-block edges are negative. In the example below, we construct a network with a perfect block structure with `sample_islands_signed()`

. The network consists of 10 blocks with 10 vertices each, where each block has a density of 1 (of positive edges). The function `signed_blockmodel()`

is used to construct the blockmodel. The parameter `k`

is the number of desired blocks. `alpha`

is a trade-off parameter. The function minimizes \(P(C)=\alpha N+(1-\alpha)P\), where \(N\) is the total number of negative ties within blocks and \(P\) be the total number of positive ties between blocks.

g <- sample_islands_signed(10,10,1,20) clu <- signed_blockmodel(g,k = 10,alpha = 0.5) table(clu$membership) #> #> 1 2 3 4 5 6 7 8 9 10 #> 10 10 10 10 10 10 10 10 10 10 clu$criterion #> [1] 0

The function returns a list with two entries. The block membership of nodes and the value of \(P(C)\).

The function `ggblock()`

can be used to plot the outcome of the blockmodel (`ggplot2`

is required).

ggblock(g,clu$membership,show_blocks = TRUE)

If the parameter `annealing`

is set to TRUE, simulated annealing is used in the optimization step. This generally leads to better results but longer runtimes.

data("tribes") set.seed(44) #for reproducibility signed_blockmodel(tribes,k = 3,alpha=0.5,annealing = TRUE) #> $membership #> [1] 1 1 3 3 2 3 3 3 2 2 3 3 2 2 1 1 #> #> $criterion #> [1] 2 signed_blockmodel(tribes,k = 3,alpha=0.5,annealing = FALSE) #> $membership #> [1] 1 1 2 3 2 2 2 2 3 3 2 2 3 3 1 1 #> #> $criterion #> [1] 5

The function `signed_blockmodel()`

is only able to provide a blockmodel where the diagonal blocks are positive and off-diagonal blocks are negative. The function `signed_blockmodel_general()`

can be used to specify different block structures. In the below example, we construct a network that contains three blocks. Two have positive and one has negative intra-group ties. The inter-group edges are negative between group one and two, and one and three. Between group two and three, all edges are positive.

g1 <- g2 <- g3 <- graph.full(5) V(g1)$name <- as.character(1:5) V(g2)$name <- as.character(6:10) V(g3)$name <- as.character(11:15) g <- Reduce("%u%",list(g1,g2,g3)) E(g)$sign <- 1 E(g)$sign[1:10] <- -1 g <- add.edges(g,c(rbind(1:5,6:10)),attr = list(sign=-1)) g <- add.edges(g,c(rbind(1:5,11:15)),attr = list(sign=-1)) g <- add.edges(g,c(rbind(11:15,6:10)),attr = list(sign=1))

The parameter `blockmat`

is used to specify the desired block structure.

set.seed(424) #for reproducibility blockmat <- matrix(c(1,-1,-1,-1,1,1,-1,1,-1),3,3,byrow = TRUE) blockmat #> [,1] [,2] [,3] #> [1,] 1 -1 -1 #> [2,] -1 1 1 #> [3,] -1 1 -1 general <- signed_blockmodel_general(g,blockmat,alpha = 0.5) traditional <- signed_blockmodel(g,k = 3,alpha = 0.5,annealing = TRUE) c(general$criterion,traditional$criterion) #> [1] 0 6

Doreian, Patrick, and Andrej Mrvar. 1996. “A Partitioning Approach to Structural Balance.” Social Networks 18 (2): 149–68.

Doreian, Patrick, and Andrej Mrvar. 2009. “Partitioning Signed Social Networks.” Social Networks 31 (1): 1–11.

Doreian, Patrick, and Andrej Mrvar. 2015. “Structural Balance and Signed International Relations.” Journal of Social Structure 16: 1.