run_laplace_embedding

Run Laplace embedding on a symmetric (weighted) adjacency matrix
with a specified number of eigenvalues and eigenvectors.

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

run_laplace_embedding function

<dl><dt>adj_mat</dt>
<dd>Symmetric adjacency matrix to be embedded.</dd>
<dt>num_eigs</dt>
<dd>Number of eigenvalues and eigenvectors for the embedding.</dd>
<dt>type_lap</dt>
<dd>Type of Laplacian for the embedding.
One of <code>"comb"</code> (combinatorial) or <code>"rw"</code> (random-walk).</dd></dl>

Arguments

Run Laplace embedding — run_laplace_embedding

<dl>

<dt>adj_mat</dt>
<dd>Symmetric adjacency matrix to be embedded.</dd>


<dt>num_eigs</dt>
<dd>Number of eigenvalues and eigenvectors for the embedding.</dd>


<dt>type_lap</dt>
<dd>Type of Laplacian for the embedding.
One of <code>"comb"</code> (combinatorial) or <code>"rw"</code> (random-walk).</dd>

</dl>

run_laplace_embedding: Run Laplace embedding

Description

Usage

Value

Arguments

Examples