# estimateMultipartiteSBM: Estimation for multipartite SBM In GrossSBM/sbm: Stochastic Blockmodels

## Description

Estimation for multipartite SBM

## Usage

 `1` ```estimateMultipartiteSBM(listSBM, estimOptions = list()) ```

## Arguments

 `listSBM` list of networks that were defined by the `defineSBM` function `estimOptions` options for the inference procedure

## 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

• "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

## Value

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

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24``` ```## 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") ```

GrossSBM/sbm documentation built on April 8, 2021, 5:53 a.m.