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NAME   -  Computes  degree,  centrality, betweeness, closeness and eigenvector
       centrality measures in the network.


       vector, network, centrality measures

SYNOPSIS help   [-ga]   input=name     [layer=integer]     output=name     [cats=range]
       [where=sql_query]    [afcolumn=name]    [abcolumn=name]   [degree=name]   [closeness=name]
       [betweenness=name]       [eigenvector=name]       [iterations=integer]       [error=float]
       [--overwrite]  [--verbose]  [--quiet]

           Use geodesic calculation for longitude-latitude locations

           Add points on nodes

           Allow output files to overwrite existing files

           Verbose module output

           Quiet module output

           Name of input vector map

           Layer number
           A  single  vector  map  can  be  connected  to  multiple  database tables. This number
           determines which table to use.
           Default: 1

           Name for output vector map

           Category values
           Example: 1,3,7-9,13

           WHERE conditions of SQL statement without 'where' keyword
           Example: income = 10000

           Name of arc forward/both direction(s) cost column

           Name of arc backward direction cost column

           Name of degree centrality column

           Name of closeness centrality column

           Name of betweenness centrality column

           Name of eigenvector centrality column

           Maximum number of iterations to compute eigenvector centrality
           Default: 1000

           Cummulative error tolerance for eigenvector centrality
           Default: 0.1

DESCRIPTION  computes  degree,  closeness,  betweenness  and  eigenvector  centrality


       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 wikipedia article.  If the column name is
       not given for a measure then that measure is not computed. If -a flag is set  then  points
       are  added  on nodes without points. Also, the points for which the output is computed can
       be specified by cats, layer and where parameters. However, if any of these  parameters  is
       present then -a flag is ignored and no new points are added.
       Betweenness  measure  is  not normalised. In order to get the normalised values (between 0
       and 1), each number needs to be divided by N choose 2=N*(N-1)/2 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  cummulative  squared  error  between  the
       successive iterations is less than error.


       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. input=roads output=roads_cent closeness=closeness \
             betweenness=betweenness -a

SEE ALSO, v.generalize


       Daniel Bundala, Google Summer of Code 2009, Student
       Wolf Bergenheim, Mentor

       Last changed: $Date: 2013-05-23 13:01:55 -0700 (Thu, 23 May 2013) $

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