Fits a discrete core-periphery model to a given network
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.
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
#>