mam_M10

Perform the motif adjacency matrix calculations for motif M10

internal

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

mam_M10 function

<dl><dt>adj_mat</dt>
<dd>Adjacency matrix from which to build the motif
adjacency matrix.</dd>
<dt>motif_type</dt>
<dd>Type of motif adjacency matrix to build.</dd>
<dt>mam_weight_type</dt>
<dd>The weighting scheme to use.
One of <code>"unweighted"</code>, <code>"mean"</code> or <code>"product"</code>.</dd>
<dt>mam_method</dt>
<dd>Which formulation to use.
One of <code>"dense"</code> or <code>"sparse"</code>.</dd></dl>

Arguments

Perform the motif adjacency matrix calculations for motif M10 — mam_M10

<dl>

<dt>adj_mat</dt>
<dd>Adjacency matrix from which to build the motif
adjacency matrix.</dd>


<dt>motif_type</dt>
<dd>Type of motif adjacency matrix to build.</dd>


<dt>mam_weight_type</dt>
<dd>The weighting scheme to use.
One of <code>"unweighted"</code>, <code>"mean"</code> or <code>"product"</code>.</dd>


<dt>mam_method</dt>
<dd>Which formulation to use.
One of <code>"dense"</code> or <code>"sparse"</code>.</dd>

</dl>

mam_M10: Perform the motif adjacency matrix calculations for motif M10

Description

Usage

Value

Arguments