Blockmodeling for signed networks

Finds blocks of nodes with intra-positive and inter-negative edges

Usage

signed_blockmodel(g, k, alpha = 0.5, annealing = FALSE)

Arguments

g

igraph object with a sign edge attribute.

k

number of blocks

alpha

see details

annealing

logical. if TRUE, use simulated annealing followed by a greedy local search. If FALSE, only use the greedy local search (Default: FALSE)

Value

list with the block assignments (membership) and the associated criterion value (criterion)

Details

The function minimizes P(C)=\(\alpha\)N+(1-\(\alpha\))P, where N is the total number of negative ties within plus-sets and P be the total number of positive ties between plus-sets. This function implements the structural balance model. That is, all diagonal blocks are positive and off-diagonal blocks negative. Ties are counted per entry of the adjacency matrix, so each undirected tie counts twice. Both algorithms start from a random partition, so results can differ between runs. Use set.seed() for reproducible results. For the generalized version see signed_blockmodel_general.

References

Doreian, Patrick and Andrej Mrvar (2009). Partitioning signed social networks. Social Networks 31(1) 1-11

Author

David Schoch

Examples

library(igraph)

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

# Using simulated annealing (less change of getting trapped in local optima)
data("tribes")
clu <- signed_blockmodel(tribes, k = 3, alpha = 0.5, annealing = TRUE)
table(clu$membership)
#> 
#> 1 2 3 
#> 4 5 7 
clu$criterion
#> [1] 2