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<h2>DESCRIPTION</h2>
<em>v.net.centrality</em> computes degree, closeness, betweenness
and eigenvector centrality measures.
<h2>NOTES</h2>
The module computes various centrality measures for each node and
stores them in the given columns of an attribute table, which is
created and linked to the output map. For the description of these,
please check the following
<a href="https://en.wikipedia.org/wiki/Centrality">wikipedia article</a>.
If the column name is not given for a measure then that measure is not
computed. If <b>-a</b> flag is set then points are added on nodes
without points. Also, the points for which the output is computed
can be specified by <b>cats</b>, <b>layer</b> and <b>where</b>
parameters. However, if any of these parameters is present then
<b>-a</b> flag is ignored and no new points are added.
<br>
Betweenness measure is not normalised. In order to get the normalised
values (between 0 and 1), each number needs to be divided by <em>N
choose 2=N*(N-1)/2</em> where N is the number of nodes in the
connected component. Computation of eigenvector measure terminates
if the given number of iterations is reached or the cumulative <em>
squared</em> error between the successive iterations is less than <b>
error</b>.
<h2>EXAMPLES</h2>
Compute closeness and betweenness centrality measures for each node
and produce a map containing not only points already present in the
input map but a map with point on every node.
<div class="code"><pre>
v.net.centrality input=roads output=roads_cent closeness=closeness \
betweenness=betweenness -a
</pre></div>
<h2>SEE ALSO</h2>
<em>
<a href="v.net.html">v.net</a>,
<a href="v.generalize.html">v.generalize</a>
</em>
<h2>AUTHORS</h2>
Daniel Bundala, Google Summer of Code 2009, Student<br>
Wolf Bergenheim, Mentor
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