mimiSBM: Mixture of Multilayer Integrator SBM (mimiSBM)

In mimiSBM: Mixture of Multilayer Integrator Stochastic Block Models

Mixture of Multilayer Integrator SBM (mimiSBM)

Description

Model that allows both clustering of individuals and grouping of views by component. This bayesian model estimates the probability of individuals belonging to each cluster (cluster crossing all views) and the membership component for all views. In addition, the connectivity tensor between classes, conditional on the components, is also estimated.

Usage

mimiSBM(
  A,
  Kset,
  Qset,
  beta_0 = 1/2,
  theta_0 = 1/2,
  eta_0 = 1/2,
  xi_0 = 1/2,
  criterion = "ILVB",
  tol = 0.001,
  iter_max = 10,
  n_init = 1,
  alternate = FALSE,
  Verbose = FALSE,
  eps_conv = 1e-04,
  type_init = "SBM"
)

Arguments

A	an array of dim=c(N,N,V)
Kset	Set of number of clusters
Qset	Set of number of components
beta_0	hyperparameters for beta
theta_0	hyperparameters for theta
eta_0	hyperparameters for eta
xi_0	hyperparameters for xi
criterion	model selection criterion, criterion=c("ILVB","ICL_approx","ICL_variationnel","ICL_exact")
tol	convergence parameter on ELBO
iter_max	maximal number of iteration of mimiSBM
n_init	number of initialization of the mimi algorithm.
alternate	boolean indicated if we put an M-step after each part of the E-step, after u optimization and after tau optimization. If not, we optimize u and tau and after the M-step is made.
Verbose	boolean for information on model fitting
eps_conv	parameter of convergence for tau.
type_init	select the type of initialization type_init=c("SBM","Kmeans","random")

Value

The best model, conditionnally to the criterion, and its parameters.

Examples

set.seed(42)
K = c(2,3); pi_k = rep(1/4,4) ; rho = rep(1/2,2)
res <- rSMB_partition(N = 50,V = 5,K = K ,pi_k = pi_k ,rho = rho,p_switch = 0.1)
A = res$simulation$A ; Kset = 4 ; Qset = 2
model <- mimiSBM(A,Kset,Qset,n_init = 1, Verbose=FALSE)

mimiSBM documentation built on May 29, 2024, 2:48 a.m.