sampleSimpleSBM function

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

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

Arguments

• nbNodes: number of nodes in the network
• blockProp: parameters for 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
• 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.

Returns

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)