sampleBipartiteSBM function

Sampling of Bipartite SBMs

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

sampleBipartiteSBM(
  nbNodes,
  blockProp,
  connectParam,
  model = "bernoulli",
  dimLabels = c(row = "row", col = "col"),
  covariates = list(),
  covariatesParam = numeric(0)
)

Arguments

• nbNodes: number of nodes in the network
• blockProp: parameters for block proportions: list of size two with row and column block proportions
• connectParam: list of parameters for connectivity with a matrix of means 'mean' and an optional matrix of variances 'var', the sizes of which must match blockProp length (in row, respectively in column)
• model: character describing the model for the relation between nodes ('bernoulli', 'poisson', 'gaussian', 'ZIgaussian'). Default is 'bernoulli'.
• dimLabels: an optional list of labels for each dimension (in row, in column)
• covariates: a list of matrices with same dimension as mat describing covariates at the edge level. No covariate per Default.
• covariatesParam: optional vector of covariates effect. A zero length numeric vector by default.

Returns

an object with class BipartiteSBM

Examples

### =======================================
### BIPARTITE BERNOULLI SBM
## Graph parameters
nbNodes <- c(100, 120)
blockProp <- list(c(.5, .5), c(1/3, 1/3, 1/3)) # group proportions
means <- matrix(runif(6), 2, 3)  # connectivity matrix
# In Bernoulli SBM, parameters is a list with
# a matrix of means 'mean' which are probabilities of connection
connectParam <- list(mean = means)

## Graph Sampling
dimLabels = c(row='Reader',col='Book')
mySampler <- sampleBipartiteSBM(nbNodes, blockProp, connectParam, model = 'bernoulli',dimLabels)
plot(mySampler)
plot(mySampler,type='meso',plotOptions = list(vertex.label.name=list(row='Reader',col='Book')))
plot(mySampler,type='meso',plotOptions = list(vertex.label.name=c('A','B'),vertex.size = 1.4))
mySampler$rMemberships() # sample new memberships
mySampler$rEdges()   # sample new edges
mySampler$rNetwork()   # sample a new networrk (blocks and edges)
### =======================================
### BIPARTITE POISSON SBM
## Graph parameters
nbNodes <- c(100, 120)
blockProp <- list(c(.5, .5), c(1/3, 1/3, 1/3)) # group proportions
means <- matrix(rbinom(6, 30, 0.25), 2, 3)  # connectivity matrix
# In Poisson SBM, parameters is a list with a matrix of
# means 'mean' which are a mean integer value taken by edges
connectParam <- list(mean = means)

## Graph Sampling
dimLabels = c(row = 'Ind', col = 'Service')
mySampler <- sampleBipartiteSBM(nbNodes, blockProp, connectParam, model = 'poisson', dimLabels)
plot(mySampler,type='expected')
plotOptions = list(vertex.label.name=c('U','V'),vertex.size = c(1.4,1.3))
plot(mySampler, type='meso', plotOptions = plotOptions)
hist(mySampler$networkData)

### =======================================
### BIPARTITE GAUSSIAN SBM
## Graph parameters
nbNodes <- c(100, 120)
blockProp <- list(c(.5, .5), c(1/3, 1/3, 1/3)) # group proportions
means <- 20 * matrix(runif(6), 2, 3)  # connectivity matrix
# In Gaussian SBM, parameters is a list with a matrix
# of means 'mean' and a matrix of variances 'var'
connectParam <- list(mean = means, var = 1)

## Graph Sampling
mySampler <- sampleBipartiteSBM(nbNodes, blockProp, connectParam, model = 'gaussian')
plot(mySampler)
hist(mySampler$networkData)