degree_c

Compute the degree 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

degree_c function

<dl><dt>adj</dt>
<dd>is an adjacency matrix of a weighted and directed network</dd>
<dt>alpha</dt>
<dd>is a tuning parameter. The value of alpha must be nonnegative.
By convention, alpha takes a value from 0 to 1 (default).</dd>
<dt>mode</dt>
<dd>which mode to compute: "out" (default) or "in"? For undirected
networks, this setting is irrelevant.</dd></dl>

Arguments

Degree-based centrality — degree_c

<dl>

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


<dt>alpha</dt>
<dd>is a tuning parameter. The value of alpha must be nonnegative.
By convention, alpha takes a value from 0 to 1 (default).</dd>


<dt>mode</dt>
<dd>which mode to compute: "out" (default) or "in"? For undirected
networks, this setting is irrelevant.</dd>

</dl>

degree_c: Degree-based centrality

Description

Usage

Value

Arguments

References