mlvsbm_estimate_network {MLVSBM} | R Documentation |
Infer a multilevel network (MLVSBM object), the original object is modified
Description
The inference use a greedy algorithm to navigate between model size. For a given model size, the inference is done via a variational EM algorithm. The returned model is the one with the highest ICL criterion among all visited models.
By default the algorithm fits a single level SBM for each level, before
inferring the multilevel network. This step can be skipped by specifying an
initial clustering with the init_clustering
. Also, a given model size
can be force by setting the parameters nb_clusters
to a given value.
Usage
mlvsbm_estimate_network(
mlv,
nb_clusters = NULL,
init_clustering = NULL,
nb_cores = NULL,
init_method = "hierarchical"
)
Arguments
mlv |
A MLVSBM object, the network to be inferred. |
nb_clusters |
A list of 2 integers, the model size.
If left to |
init_clustering |
A list of 2 vectors of integers of the same length as the number of node of each level. If specified, the algorithm will start from this clustering, then navigate freely. |
nb_cores |
An integer, the number of cores to use. Default to |
init_method |
One of "hierarchical" (the default) or "spectral", "spectral" might be more efficient but can lead to some numeric errors. Not used when int_clustering is given. |
Value
A FitMLVSBM object, the best inference of the network
Examples
my_mlvsbm <- MLVSBM::mlvsbm_simulate_network(
n = list(I = 10, O = 20), # Number of nodes for the lower level and the upper level
Q = list(I = 2, O = 2), # Number of blocks for the lower level and the upper level
pi = c(.3, .7), # Block proportion for the upper level, must sum to one
gamma = matrix(c(.9, .2, # Block proportion for the lower level,
.1, .8), # each column must sum to one
nrow = 2, ncol = 2, byrow = TRUE),
alpha = list(I = matrix(c(.8, .2,
.2, .1),
nrow = 2, ncol = 2, byrow = TRUE), # Connection matrix
O = matrix(c(.99, .3,
.3, .1),
nrow = 2, ncol = 2, byrow = TRUE)),# between blocks
directed = list(I = FALSE, O = FALSE), # Are the upper and lower level directed or not ?
affiliation = "preferential") # How the affiliation matrix is generated
fit <- MLVSBM::mlvsbm_estimate_network(mlv = my_mlvsbm, nb_cores = 1)