bipartite (version 2.19)

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-function.

Usage

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

Value

Returns a data.frame with three columns: the first column contains the nodes' ids, the second column contains the closeness scores, and the third column contains the normalised closeness scores (i.e., divided by N-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. If this parameter is set to FALSE, a closeness measure for all nodes is computed. For details, see https://toreopsahl.com/2010/03/20/closeness-centrality-in-networks-with-disconnected-components/

precomp.dist

If you have already computed the distance matrix using distance_w-function, 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). If this parameter is set to 1 (default), the Dijkstra shortest paths are used. The identification procedure of these paths rely simply on the tie weights and disregards the number of nodes on the paths.

Author

Tore Opsahl; https://toreopsahl.com/

References

https://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)