sample_dsbm

Sample the (weighted) adjacency matrix of a (weighted) directed stochastic
block model (DSBM) with specified parameters.

Tools for spectral clustering of weighted directed networks using motif
adjacency matrices. Methods perform well on large and sparse networks, and
random sampling methods for generating weighted directed networks are also
provided. Based on methodology detailed in Underwood, Elliott and Cucuringu
(2020) <arXiv:2004.01293>.

William George Underwood

motifcluster

Motif-Based Spectral Clustering of Weighted Directed Networks

sample_dsbm function

<dl><dt>block_sizes</dt>
<dd>A vector containing the size of each block of vertices.</dd>
<dt>connection_matrix</dt>
<dd>A matrix containing the block-to-block connection
probabilities.</dd>
<dt>weight_matrix</dt>
<dd>A matrix containing the block-to-block weight
parameters.
Unused for <code>sample_weight_type = "constant"</code>.
Defaults to <code>NULL</code>.</dd>
<dt>sample_weight_type</dt>
<dd>The type of weighting scheme.
One of <code>"unweighted"</code>, <code>"constant"</code> or <code>"poisson"</code>.</dd></dl>

Arguments

Sample a directed stochastic block model (DSBM) — sample_dsbm

<dl>

<dt>block_sizes</dt>
<dd>A vector containing the size of each block of vertices.</dd>


<dt>connection_matrix</dt>
<dd>A matrix containing the block-to-block connection
probabilities.</dd>


<dt>weight_matrix</dt>
<dd>A matrix containing the block-to-block weight
parameters.
Unused for <code>sample_weight_type = "constant"</code>.
Defaults to <code>NULL</code>.</dd>


<dt>sample_weight_type</dt>
<dd>The type of weighting scheme.
One of <code>"unweighted"</code>, <code>"constant"</code> or <code>"poisson"</code>.</dd>

</dl>

sample_dsbm: Sample a directed stochastic block model (DSBM)

Description

Usage

Value

Arguments

Examples