Blockmodeling

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

Traditional Blockmodeling

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 2 2 3 2 2 2 3 3 2 2 3 3 1 1
#> 
#> $criterion
#> [1] 2
signed_blockmodel(tribes,k = 3,alpha=0.5,annealing = FALSE)
#> $membership
#>  [1] 1 1 2 2 3 2 2 2 3 3 2 2 3 3 1 1
#> 
#> $criterion
#> [1] 2

Generalized Blockmodeling

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

Blockmodeling

Traditional Blockmodeling

Generalized Blockmodeling

References