MultSbm function

Multinomial Stochastic Block Model Prior class

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 : [REMOVE_ME]$\pi \sim Dirichlet(\alpha) [REMOVE_ME_2]$

[REMOVE_ME]$Z_i \sim \mathcal{M}(1,\pi) [REMOVE_ME_2]$

[REMOVE_ME]$\theta_{kl} \sim Dirichlet(\beta) [REMOVE_ME_2]$

[REMOVE_ME]$X_{ij.}|Z_{ik}Z_{jl}=1 \sim \mathcal{M}(L_{ij},\theta_{kl}) [REMOVE_ME_2]$

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. class

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

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

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)

Returns

a MultSbmPrior-class object

a MultSbm-class object

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 :

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.

Examples

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

See Also

MultSbmFit-class, MultSbmPath-class

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