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Estimation for multipartite SBM

Source: R/estimate.R
estimateMultipartiteSBM.Rd

Estimation for multipartite SBM

Usage

estimateMultipartiteSBM(listSBM, estimOptions = list())

Arguments

listSBM

list of networks that were defined by the defineSBM function

estimOptions

options for the inference procedure

Value

a MultipartiteSBM_fit object with the estimated parameters and the blocks in each Functional Group

Details

The list of parameters estimOptions essentially tunes the optimization process and the variational EM algorithm, with the following parameters

  • "nbCores" integer for number of cores used. Default is 2 (1 under Positron, which does not allow forking)

  • "verbosity" integer for verbosity (0, 1). Default is 1

  • "nbBlocksRange" List of length the number of functional groups, each element supplying the minimal and maximal number of blocks to be explored. The names of the list must be the names of the functional groups. Default value is from 1 to 10)

  • "initBM" Boolean. True if using simple and bipartite SBM as initialisations. Default value = TRUE

  • "maxiterVEM" Number of max. number of iterations in the VEM. Default value = 100

  • "maxiterVE" Number of max. number of iterations in the VE. Default value = 100

Missing values

The networks may contain missing values (NA): the corresponding dyads are considered as not observed and are ignored in the inference (performed by the package GREMLINS). This assumes that the dyads are missing at random.

Examples

if (FALSE) { # \dontrun{
## About the Parts/Functional Groups (FG)
blockProp <- list(c(0.16 ,0.40 ,0.44),c(0.3,0.7)) # prop of blocks in each FG
archiMultipartite <-  rbind(c(1,2),c(2,2),c(1,1)) # architecture of the multipartite net.
nbNodes <- c(60,50)
## About the connection matrices
directed <- c(NA, TRUE, FALSE) # type of each network
model <- c('gaussian','bernoulli','poisson')
C1 <-
 list(mean = matrix(c(6.1, 8.9, 6.6, 9.8, 2.6, 1.0), 3, 2),
      var  = matrix(c(1.6, 1.6, 1.8, 1.7 ,2.3, 1.5),3, 2))
C2 <- list(mean = matrix(c(0.7,1.0, 0.4, 0.6),2, 2))
m3 <- matrix(c(2.5, 2.6 ,2.2 ,2.2, 2.7 ,3.0 ,3.6, 3.5, 3.3),3,3 )
C3 <- list(mean = .5 * (m3 + t(m3)))
connectParam <- list(C1, C2, C3)
## Graph Sampling
mySampleMSBM <- sampleMultipartiteSBM(nbNodes, blockProp,
                                      archiMultipartite, connectParam, model,
                                      directed, dimLabels = c('A','B'), seed = 2)
listSBM <- mySampleMSBM$listSBM
estimOptions <- list(initBM = FALSE, nbCores  = 2)
myMSBM <- estimateMultipartiteSBM(listSBM, estimOptions)
plot(myMSBM, type = "data")
plot(myMSBM, type = "expected")
plot(myMSBM, type = "meso")
} # }