An S4 class to represent a Multinomial Stochastic Block Model. Such model can be used to cluster multilayer-graph vertex, and model a square adjacency cube \(X\) of size NxNxM with the following generative model :
$$ \pi \sim Dirichlet(\alpha)$$
$$ Z_i \sim \mathcal{M}(1,\pi)$$
$$ \theta_{kl} \sim Dirichlet(\beta)$$
$$ X_{ij.}|Z_{ik}Z_{jl}=1 \sim \mathcal{M}(L_{ij},\theta_{kl})$$
With \(L_{ij}=\sum_{m=1}^MX_{ijm}\). These classes mainly store the prior parameters value \(\alpha,\beta\) of this generative model.
The MultSbm-class
must be used when fitting a simple MultSbm whereas the MultSbmPrior-class
must be sued when fitting a MixedModels-class
.
MultSbmPrior(beta = 1, type = "guess")
MultSbm(alpha = 1, beta = 1, type = "guess")
Dirichlet prior parameter over Multinomial links
define the type of networks (either "directed", "undirected" or "guess", default to "guess"), for undirected graphs the adjacency matrix is supposed to be symmetric.
Dirichlet prior parameter over the cluster proportions (default to 1)
a MultSbmPrior-class
object
a MultSbm-class
object
MultSbm-class
: MultSbm class constructor
MultSbmPrior
: MultSbmPrior class constructor
MultSbm
: MultSbm class constructor
beta
Dirichlet prior parameter over Multinomial links
type
define the type of networks (either "directed", "undirected" or "guess", default to "guess")
alpha
Dirichlet prior parameter over the cluster proportions (default to 1)
MultSbmPrior()
#> An object of class "MultSbmPrior"
#> Slot "beta":
#> [1] 1
#>
#> Slot "type":
#> [1] "guess"
#>
MultSbmPrior(type = "undirected")
#> An object of class "MultSbmPrior"
#> Slot "beta":
#> [1] 1
#>
#> Slot "type":
#> [1] "undirected"
#>
MultSbm()
#> An object of class "MultSbm"
#> Slot "alpha":
#> [1] 1
#>
#> Slot "beta":
#> [1] 1
#>
#> Slot "type":
#> [1] "guess"
#>
MultSbm(type = "undirected")
#> An object of class "MultSbm"
#> Slot "alpha":
#> [1] 1
#>
#> Slot "beta":
#> [1] 1
#>
#> Slot "type":
#> [1] "undirected"
#>