# sampleMultipartiteSBM: Sampling of Multipartite SBMs In GrossSBM/sbm: Stochastic Blockmodels

## Description

This function samples a Multipartite Stochastic Block Models, with various model for the distribution of the edges: Bernoulli, Poisson, or Gaussian models

## Usage

 ``` 1 2 3 4 5 6 7 8 9 10``` ```sampleMultipartiteSBM( nbNodes, blockProp, archiMultipartite, connectParam, model, directed, dimLabels = NULL, seed = NULL ) ```

## Arguments

 `nbNodes` number of nodes in each functional group involved in the multipartite network `blockProp` a list of parameters for block proportions in each functional group `archiMultipartite` a matrix with two columns and nbNetworks lines, each line specifying the index of the functional groups in interaction. `connectParam` list of parameters for connectivity (of length nbNetworks). Each element is a list of one or two elements: a matrix of means 'mean' and an optional matrix of variances 'var', the sizes of which must match `blockProp` length `model` a vector of characters describing the model for each network of the Multipartite relation between nodes (`'bernoulli'`, `'poisson'`, `'gaussian'`, ...). Default is `'bernoulli'`. `directed` a vector of logical, directed network or not for each network. Default is `FALSE`. `dimLabels` an optional list of labels for functional group involved in the network `seed` numeric to set the seed.

## Value

a list of two elements : `simulatedMemberships` are the clustering of each node in each Functional Group, `multipartiteNetwork` is the list of the simulated networks (each one being a simple or bipartite network)

## 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 25 26 27 28 29 30 31``` ```### ======================================= ### MULTIPARTITE SBM : 4 networks between 3 Functional Groups ## Graph parameters # About the Functional Groups (FG) nbNodes <- c(100,50,40) blockProp <- vector("list", 3) # parameters of clustering in each functional group blockProp[] <- c(0.4,0.3,0.3) # in Functional Group 1 blockProp[] <- c(0.6,0.4) # in Functional Group 2 blockProp[] <- c(0.6,0.4) # in Functional Group 3 # About the interactions between the FG archiMultipartite <- rbind(c(1,2),c(2,3),c(2,2),c(1,3)) # model <- c('bernoulli','poisson','gaussian','gaussian') # type of distribution in each network # for each network : directed or not (not required for an interaction between two different FG) directed <- c( NA, NA , FALSE , NA) connectParam <- list() connectParam[] <- list(mean = matrix(c(0.3, 0.3, 0.5, 0.2, 0.6, 0.6),3,2)) connectParam[] <- list(mean = matrix(c(1000 , 500, 400 , 950),2,2)) connectParam[] <- list(mean = matrix(c(10, 0, -10, 20), 2,2), var = matrix(1,2,2)) connectParam[] <- list(mean = matrix(c(3, 23 ,11 ,16 , 2 ,25), 3,2)) connectParam[]\$var <- matrix(c(10,20,1,5,0.1,10), 3,2) dimLabels <- c('A','B','C') ## Graph Sampling mySampleMBM <- sampleMultipartiteSBM(nbNodes, blockProp, archiMultipartite, connectParam, model, directed, dimLabels,seed = 3) listSBM <- mySampleMBM\$listSBM memberships <- mySampleMBM\$memberships plotMyMultipartiteMatrix(listSBM) plotMyMultipartiteMatrix(listSBM,plotOptions = list(normalized = TRUE)) plotMyMultipartiteMatrix(listSBM,memberships = memberships,plotOptions = list(normalized = TRUE)) ```

GrossSBM/sbm documentation built on Oct. 8, 2021, 6:23 p.m.