expectation

expectation.undirected_factor_model

expectation.directed_factor_model

Calculate the expected adjacency matrix

Samples generalized random product graphs, a generalization of
a broad class of network models. Given matrices X, S, and Y with with
non-negative entries, samples a matrix with expectation X S Y^T and
independent Poisson or Bernoulli entries using the fastRG algorithm of
Rohe et al. (2017) <https://www.jmlr.org/papers/v19/17-128.html>. The
algorithm first samples the number of edges and then puts them down
one-by-one. As a result it is O(m) where m is the number of edges, a
dramatic improvement over element-wise algorithms that which require
O(n^2) operations to sample a random graph, where n is the number of
nodes.

Alex Hayes

fastRG

Sample Generalized Random Dot Product Graphs in Linear Time

Karl Rohe

Jun Tao

Xintian Han

Norbert Binkiewicz

expectation function

<dl><dt>model</dt>
<dd>A <code>directed_factor_model()</code> or an <code>undirected_factor_model()</code>
object.</dd>
<dt>...</dt>
<dd>Unused.</dd></dl>

Arguments

Calculate the expected adjacency matrix — expectation

<dl>

<dt>model</dt>
<dd>A <code>directed_factor_model()</code> or an <code>undirected_factor_model()</code>
object.</dd>


<dt>...</dt>
<dd>Unused.</dd>

</dl>

expectation: Calculate the expected adjacency matrix

Description

Usage

Value

Arguments