spectral

performs regularized spectral clustering of a sparse adjacency matrix

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

spectral function

<dl><dt>X</dt>
<dd>An adjacency matrix in sparse format (see the <code>Matrix</code> package)</dd>
<dt>K</dt>
<dd>Desired number of cluster</dd></dl>

Arguments

Regularized spectral clustering — spectral

<dl>

<dt>X</dt>
<dd>An adjacency matrix in sparse format (see the <code>Matrix</code> package)</dd>


<dt>K</dt>
<dd>Desired number of cluster</dd>

</dl>

spectral: Regularized spectral clustering

Description

Usage

Value

Arguments

References