Compute the weighted PageRank centrality measures of the vertices in a
weighted and directed network represented through its adjacency matrix.

internal

Assortativity coefficients, centrality measures,
and clustering coefficients for weighted and directed networks.
Rewiring unweighted networks with given assortativity coefficients.
Generating general preferential attachment networks.

Yelie Yuan

wdnet

Weighted and Directed Networks

Tiandong Wang

Jun Yan

Panpan Zhang

wpr function

<dl><dt>adj</dt>
<dd>is an adjacency matrix of a weighted and directed network</dd>
<dt>gamma</dt>
<dd>is the damping factor; it takes 0.85 (default) if not given.</dd>
<dt>theta</dt>
<dd>is a tuning parameter leveraging node degree and strength; theta
= 0 does not consider edge weight; theta = 1 (default) fully considers edge
weight.</dd>
<dt>prior.info</dt>
<dd>vertex-specific prior information for restarting when
arriving at a sink. When it is not given (<code>NULL</code>), a random restart is
implemented.</dd></dl>

Arguments

Weighted PageRank centrality — wpr

<dl>

<dt>adj</dt>
<dd>is an adjacency matrix of a weighted and directed network</dd>


<dt>gamma</dt>
<dd>is the damping factor; it takes 0.85 (default) if not given.</dd>


<dt>theta</dt>
<dd>is a tuning parameter leveraging node degree and strength; theta
= 0 does not consider edge weight; theta = 1 (default) fully considers edge
weight.</dd>


<dt>prior.info</dt>
<dd>vertex-specific prior information for restarting when
arriving at a sink. When it is not given (<code>NULL</code>), a random restart is
implemented.</dd>

</dl>

wpr: Weighted PageRank centrality

Description

Usage

Value

Arguments

References