SbmPrior-class

Sbm-class

SbmPrior

An S4 class to represent a Stochastic Block Model.
Such model can be used to cluster graph vertex, and model a square adjacency matrix $X$ with the following generative model :
$$ \pi \sim Dirichlet(\alpha)$$
$$ Z_i \sim \mathcal{M}(1,\pi)$$
$$ \theta_{kl} \sim Beta(a_0,b_0)$$
$$ X_{ij}|Z_{ik}Z_{jl}=1 \sim \mathcal{B}(\theta_{kl})$$
These classes mainly store the prior parameters value $\alpha,a_0,b_0$ of this generative model.
The <code>Sbm-class</code> must be used when fitting a simple Sbm whereas the <code>SbmPrior-class</code> must be used when fitting a <code>CombinedModels-class</code>.

An ensemble of algorithms that enable the clustering of networks and data matrices (such as counts, categorical or continuous) with different type of generative models. Model selection and clustering is performed in combination by optimizing the Integrated Classification Likelihood (which is equivalent to minimizing the description length). Several models are available such as: Stochastic Block Model, degree corrected Stochastic Block Model, Mixtures of Multinomial, Latent Block Model. The optimization is performed thanks to a combination of greedy local search and a genetic algorithm (see <arXiv:2002:11577> for more details).

Etienne C<c3><b4>me

greed

Clustering and Model Selection with the Integrated
Classification Likelihood

Etienne Côme

Nicolas Jouvin

Sbm function

<dl><dt>a0</dt>
<dd>Beta prior parameter over links (default to 1)</dd>
<dt>b0</dt>
<dd>Beta prior parameter over no-links (default to 1)</dd>
<dt>type</dt>
<dd>define the type of networks (either "directed", "undirected" or "guess", default to "guess"), for undirected graphs the adjacency matrix is supposed to be symmetric.</dd>
<dt>alpha</dt>
<dd>Dirichlet prior parameter over the cluster proportions (default to 1)</dd></dl>

Arguments

Stochastic Block Model Prior class — Sbm

<dl>

<dt>a0</dt>
<dd>Beta prior parameter over links (default to 1)</dd>


<dt>b0</dt>
<dd>Beta prior parameter over no-links (default to 1)</dd>


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


<dt>alpha</dt>
<dd>Dirichlet prior parameter over the cluster proportions (default to 1)</dd>

</dl>

Sbm: Stochastic Block Model Prior class

Description

Usage

Value

Arguments

References

See Also

Examples