greed (version 0.6.1)

MultSbm: Multinomial Stochastic Block Model Prior class

Description

An S4 class to represent a Multinomial Stochastic Block Model. Such model can be used to cluster multi-layer 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 CombinedModels-class.

Usage

MultSbmPrior(beta = 1, type = "guess")
MultSbm(alpha = 1, beta = 1, type = "guess")

Value

a MultSbmPrior-class object

a MultSbm-class object

Arguments

beta

Dirichlet prior parameter over Multinomial links

type

define the type of networks (either "directed", "undirected" or "guess", default to "guess"), for undirected graphs the adjacency matrix is supposed to be symmetric.

alpha

Dirichlet prior parameter over the cluster proportions (default to 1)

See Also

MultSbmFit-class, MultSbmPath-class

Other DlvmModels: CombinedModels, DcLbm, DcSbm, DiagGmm, DlvmPrior-class, Gmm, Lca, MoM, MoR, Sbm, greed()

Examples

MultSbmPrior()
MultSbmPrior(type = "undirected")
MultSbm()
MultSbm(type = "undirected")