core_periphery: Discrete core-periphery model

In schochastics/netUtils: A Collection of Tools for Network Analysis

Discrete core-periphery model

Description

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

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.

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

Author(s)

David Schoch

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)

schochastics/netUtils documentation built on Oct. 17, 2024, 10:45 a.m.