fit_mlsbm: R/Rcpp function for fitting multilevel stochastic block model

In carter-allen/mlsbm: Efficient Estimation of Bayesian SBMs & MLSBMs

R/Rcpp function for fitting multilevel stochastic block model

Description

This function allows you to fit multilevel stochastic block models.

Usage

fit_mlsbm(
  A,
  K,
  z_init = NULL,
  a0 = 2,
  b10 = 1,
  b20 = 1,
  n_iter = 1000,
  burn = 100,
  verbose = FALSE,
  r = 1.2
)

Arguments

A	An adjacency list of length L, the number of levels. Each level contains an n x n symmetric adjacency matrix.
K	The number of clusters specified a priori.
z_init	Initialized cluster indicators. If NULL, will initialize automatically with Louvain algorithm.
a0	Dirichlet prior parameter for cluster sizes for clusters 1,...,K.
b10	Beta distribution prior paramter for community connectivity.
b20	Beta distribution prior parameter for community connectivity.
n_iter	The number of total MCMC iterations to run.
burn	The number of burn-in MCMC iterations to discard. The number of saved iterations will be n_iter - burn.
verbose	Whether to print a progress bar to track MCMC progress. Defaults to true.
r	Resolution parameter for Louvain initialization. Sould be >= 0 and higher values give a larger number of smaller clusters.

Value

A list of MCMC samples, including the MAP estimate of cluster indicators (z)

Examples

data(AL)
# increase n_iter in practice
fit <- fit_mlsbm(AL,3,n_iter = 100)

carter-allen/mlsbm documentation built on March 19, 2022, 8:26 a.m.