Partition of a collection of unipartite networks based on their common mesoscale structures

Partition of a collection of unipartite networks based on their common mesoscale structures

Usage

clusterize_unipartite_networks(
  netlist,
  colsbm_model,
  directed = FALSE,
  net_id = NULL,
  distribution = "bernoulli",
  nb_run = 3L,
  global_opts = list(),
  fit_opts = list(),
  fit_init = NULL,
  full_inference = FALSE,
  verbose = TRUE,
  temp_save_path = tempfile(fileext = ".Rds")
)

Arguments

netlist

A list of matrices.

colsbm_model

Which colSBM to use, one of "iid", "pi", "delta", "deltapi",

directed

A boolean, should the networks be considered as directed or not.

net_id

A vector of string, the name of the networks.

distribution

A string, the emission distribution, either "bernoulli" (the default) or "poisson"

nb_run

An integer, the number of run the algorithm do.

global_opts

Global options for the outer algorithm and the output

fit_opts

Fit options for the VEM algorithm

fit_init

Do not use! Optional fit init from where initializing the algorithm.

full_inference

The default "FALSE", the algorithm stop once splitting groups of networks does not improve the BICL criterion. If "TRUE", then continue to split groups until a trivial classification of one network per group.

verbose

A boolean, should the function be verbose or not. Default to TRUE.

temp_save_path

A string, the path where to save the temporary results. Defaults to a temporary file.

Value

A list with two elements:

partition

A list of models giving the best partition.

cluster

A vector of integers giving the cluster of each network.

Details

This functions make call to estimate_colSBM.

See also

estimate_colSBM(), fitSimpleSBMPop, browseVignettes("colSBM")

Examples

alpha1 <- matrix(c(0.8, 0.1, 0.2, 0.7), byrow = TRUE, nrow = 2)
alpha2 <- matrix(c(0.8, 0.5, 0.5, 0.2), byrow = TRUE, nrow = 2)
first_collection <- generate_unipartite_collection(
  n = 50,
  pi = c(0.5, 0.5),
  alpha = alpha1, M = 2
)
second_collection <- generate_unipartite_collection(
  n = 50,
  pi = c(0.5, 0.5),
  alpha = alpha2, M = 2
)

netlist <- append(first_collection, second_collection)

if (FALSE) { # \dontrun{
cl_separated <- clusterize_networks(
  netlist = netlist,
  colsbm_model = "iid"
)
} # }