Clusterize bipartite networks in a bottom up fashion with graphon distance

Usage

clusterize_bipartite_networks_graphon(
  netlist,
  colsbm_model,
  net_id = NULL,
  distribution = "bernoulli",
  nb_run = 3L,
  global_opts = list(),
  fit_opts = list(),
  fit_init = NULL,
  full_inference = FALSE,
  keep_history = 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", "rho", "pirho", "delta", "deltapi".
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. Defaults to 3.
global_opts: Global options for the outer algorithm and the output
fit_opts: Fit options for the VEM algorithm
fit_init: WIP A list of fitted collections from which to start the fusions 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.
keep_history: A boolean, should the function keep the history of the fusions or not. Default to FALSE to reduce file size. Note that the fusion history is saved in the temporary file.
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 of models for the recursive partition of the collection of networks.