Sampling of Simple 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

Usage

sampleSimpleSBM(
  nbNodes,
  blockProp = numeric(0),
  connectParam,
  model = "bernoulli",
  directed = FALSE,
  dimLabels = c("node"),
  covariates = list(),
  covariatesParam = numeric(0),
  nodesCovariates = matrix(0, 0, 0),
  nodesCovariatesParam = matrix(0, 0, 0)
)

Arguments

nbNodes

number of nodes in the network

blockProp

parameters for block proportions. Possibly null if the nodes affectation is driven by nodesCovariates.

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

model

character describing the model for the relation between nodes ('bernoulli', 'poisson', 'gaussian', ...). Default is 'bernoulli'.

directed

logical, directed network or not. Default is FALSE.

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.

nodesCovariates

an optional matrix with nbNodes rows and nbNodesCovariates columns corresponding to covariates describing the nodes. No nodes covariates by default.

nodesCovariatesParam

an optional matrix of dimension nbNodesCovariates x nbBlocks corresponding to effect of the nodesCovariates on the clustering.

Value

an object with class SimpleSBM

Examples

### =======================================
### SIMPLE BINARY SBM (Bernoulli model)
## Graph parameters
nbNodes  <- 90
blockProp <- c(.5, .25, .25) # group proportions
means <- diag(.4, 3) + 0.05  # connectivity matrix: affiliation network
# In Bernoulli SBM, parameters is a list with a
# matrix of means 'mean' which are probabilities of connection
connectParam <- list(mean = means)

## Graph Sampling
mySampler <- sampleSimpleSBM(nbNodes, blockProp, connectParam, model = 'bernoulli')
plot(mySampler)

plot(mySampler)

plot(mySampler,type='meso')

hist(mySampler$networkData)


### =======================================
### SIMPLE POISSON SBM
## Graph parameters
nbNodes  <- 90
blockProp <- c(.5, .25, .25) # group proportions
means <- diag(15., 3) + 5    # connectivity matrix: affiliation network
# 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
mySampler <- sampleSimpleSBM(nbNodes, blockProp, list(mean = means), model = "poisson")
plot(mySampler)

plot(mySampler,type='meso')

hist(mySampler$networkData)


### =======================================
### SIMPLE GAUSSIAN SBM
## Graph parameters
nbNodes  <- 90
blockProp <- c(.5, .25, .25)      # group proportions
means <- diag(15., 3) + 5 # connectivity matrix: affiliation network
# In Gaussian SBM, parameters is a list with
# a matrix of means 'mean' and a matrix of variances 'var'
connectParam <- list(mean = means, var = 2)

## Graph Sampling
mySampler <- sampleSimpleSBM(nbNodes, blockProp, connectParam, model = "gaussian",dimLabels='Tree')
plot(mySampler)

plot(mySampler,type='meso')

hist(mySampler$networkData)