tnet (version 3.0.1)

closeness_w: Closeness centrality in a weighted network

Description

This function calculates closeness scores for nodes in a weighted network based on the distance_w.

Usage

closeness_w(net, directed=NULL, gconly=TRUE, precomp.dist=NULL, alpha=1)

Arguments

net

A weighted edgelist

directed

Logical: whether the edgelist is directed or undirected. Default is NULL, then the function detects this parameter.

gconly

Logical: whether to calculate closeness only on the main component (traditional closeness). Default is TRUE.

precomp.dist

If you have already computed the distance matrix using distance_w, you can enter the name of the matrix object here.

alpha

sets the alpha parameter in the generalised measures from Opsahl, T., Agneessens, F., Skvoretz, J., 2010. Node Centrality in Weighted Networks: Generalizing Degree and Shortest Paths. Social Networks.

Value

• Returns a data.frame with three columns: the first containts the node ids of all the nodes in the edgelist, the second containts the corresponding closeness scores, and the third contains the normalised closeness scores (divided by N-1).

References

http://toreopsahl.com/2009/01/09/average-shortest-distance-in-weighted-networks/

Examples

## Load sample data
sampledata <- rbind(
c(1,2,4),
c(1,3,2),
c(2,1,4),
c(2,3,4),
c(2,4,1),
c(2,5,2),
c(3,1,2),
c(3,2,4),
c(4,2,1),
c(5,2,2),
c(5,6,1),
c(6,5,1))

## Run the programme
closeness_w(sampledata)