migraph (version 0.8.13)

blockmodel: Blockmodelling

Description

Blockmodelling

Usage

blockmodel(object, clusters)
blockmodel_concor(
  object,
  p = 1,
  cutoff = 0.999,
  max.iter = 25,
  block.content = "density"
)
# S3 method for blockmodel
print(x, ...)
reduce_graph(blockmodel, block_labels = NULL)
summarise_statistics(node_measure, clusters = NULL, sumFUN = mean)

Arguments

object

A migraph-consistent object (matrix, igraph, tidygraph).

clusters

the vector of cluster membership for the blockmodel

p

An integer representing the desired number of partitions.

cutoff

A value between 0 and 1 used to determine convergence.

max.iter

An integer representing the maximum number of iterations.

block.content

A string indicating which method to use for calculating block content. Options are: "density", "sum", "meanrowsum", "meancolsum", "median", "min", "max".

x

An object of class "blockmodel"

...

Additional arguments passed to generic print method

blockmodel

a blockmodel object

block_labels

A character vector manually providing labels for the blocks in the blockmodel

node_measure

A vector or matrix of node-level statistics, such as centrality measures or a census.

sumFUN

A function by which the values should be aggregated or summarised. By default mean.

References

Breiger, R.L., Boorman, S.A., and Arabie, P. 1975. An Algorithm for Clustering Relational Data with Applications to Social Network Analysis and Comparison with Multidimensional Scaling. Journal of Mathematical Psychology, 12: 328--383.

Examples

# NOT RUN {
mex_concor <- blockmodel_concor(mpn_elite_mex)
mex_concor
plot(mex_concor)
usa_concor <- blockmodel_concor(mpn_elite_usa_advice)
usa_concor
plot(usa_concor)
summarise_statistics(node_degree(mpn_elite_mex), 
          cutree(cluster_structural_equivalence(mpn_elite_mex), 3))
summarise_statistics(node_triad_census(mpn_elite_mex), 
          cutree(cluster_structural_equivalence(mpn_elite_mex), 3))
# }