rCollectSBM

Simulate a sample of networks of a stochastic block model

Graph clustering using an agglomerative algorithm to maximize the
integrated classification likelihood criterion and a mixture of stochastic
block models. The method is described in the article "Model-based clustering
of multiple networks with a hierarchical algorithm" by
T. Rebafka (2022) <arXiv:2211.02314>.

Tabea Rebafka

graphclust

Hierarchical Graph Clustering for a Collection of Networks

rCollectSBM function

<dl><dt>vec_n</dt>
<dd>vector with number of vertices</dd>
<dt>theta</dt>
<dd>stochastic block model parameter with latent group probabilities $pi and
connectivy parameters $gamma</dd>
<dt>directed</dt>
<dd>directed networks (TRUE by default) or undirected (FALSE)</dd></dl>

Arguments

Simulate a sample of networks of a stochastic block model — rCollectSBM

<dl>

<dt>vec_n</dt>
<dd>vector with number of vertices</dd>


<dt>theta</dt>
<dd>stochastic block model parameter with latent group probabilities $pi and
connectivy parameters $gamma</dd>


<dt>directed</dt>
<dd>directed networks (TRUE by default) or undirected (FALSE)</dd>

</dl>

rCollectSBM: Simulate a sample of networks of a stochastic block model

Description

Usage

Value

Arguments

Examples