Discrete core-periphery model — core

Fits a discrete core-periphery model to a given network

Usage

core_periphery(graph, method = "rk1_dc", iter = 500, ...)

Arguments

graph: igraph object
method: algorithm to use (see details)
iter: number of iterations if method=GA
...: other parameters for GA

Value

list with numeric vector with entries (k1,k2,...ki...) where ki assigns vertex i to either the core (ki=1) or periphery (ki=0), and the maximal correlation with an optimal pattern matrix

Details

The function fits the data to an optimal pattern matrix with a genetic algorithm (method="GA") or a rank 1 approximation, either with degree centrality (method="rk1_dc") or eigenvector centrality (method="rk1_ec") . The rank 1 approximation is computationally far cheaper but also more experimental. Best is to compare the results from both models.

References

Borgatti, Stephen P., and Martin G. Everett. "Models of core/periphery structures." Social networks 21.4 (2000): 375-395.

Author

David Schoch

Examples

set.seed(121)
# split graphs have a perfect core-periphery structure
sg <- split_graph(n = 20, p = 0.3, core = 0.5)
core_periphery(sg)
#> $vec
#>  [1] 1 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0
#> 
#> $corr
#> [1] 1
#>