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

NAME KEYWORDS SYNOPSIS Flags: Parameters: DESCRIPTION NOTES EXAMPLES SEE ALSO AUTHORS SOURCE CODE

NAME

v.net.centrality − Computes degree, centrality, betweeness, closeness and eigenvector centrality measures in the network.

v.net.centrality
v.net.centrality −−help
v.net.centrality [−ga] input=name [arc_layer=string] [node_layer=string] output=name [cats=range] [where=sql_query] [arc_column=name] [arc_backward_column=name] [node_column=string] [degree=name] [closeness=name] [betweenness=name] [eigenvector=name] [iterations=integer] [error=float] [−−overwrite] [−−help] [−−verbose] [−−quiet] [−−ui]

Flags:

−g

Use geodesic calculation for longitude−latitude projects

−a

Add points on nodes

−−overwrite

Allow output files to overwrite existing files

−−help

Print usage summary

−−verbose

Verbose module output

−−quiet

Quiet module output

−−ui

Force launching GUI dialog

Parameters:

input=nameÂ [required]

Name of input vector map
Or data source for direct OGR access

arc_layer=string

Arc layer
Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.
Default: 1

node_layer=string

Node layer
Vector features can have category values in different layers. This number determines which layer to use. When used with direct OGR access this is the layer name.
Default: 2

output=nameÂ [required]

Name for output vector map

cats=range

Category values
Example: 1,3,7−9,13

where=sql_query

WHERE conditions of SQL statement without ’where’ keyword
Example: income < 1000 and population >= 10000

arc_column=name

Arc forward/both direction(s) cost column (number)

arc_backward_column=name

Arc backward direction cost column (number)

node_column=string

Node cost column (number)

degree=name

Name of degree centrality column

closeness=name

Name of closeness centrality column

betweenness=name

Name of betweenness centrality column

eigenvector=name

Name of eigenvector centrality column

iterations=integer

Maximum number of iterations to compute eigenvector centrality
Default: 1000

error=float

Cumulative error tolerance for eigenvector centrality
Default: 0.1

DESCRIPTION

v.net.centrality computes degree, closeness, betweenness and eigenvector centrality measures.

NOTES

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 cumulative squared error between the successive iterations is less than error.

EXAMPLES

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.

v.net.centrality input=roads output=roads_cent closeness=closeness \
      betweenness=betweenness −a

AUTHORS

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

SOURCE CODE

Available at: v.net.centrality source code (history)

Latest change: Friday Oct 25 16:16:09 2024 in commit: 4ea983fff87951516b6e4578a0527e3b10f04643

Updated 2026-06-01 - jenkler.se | uex.se