Closeness centrality in a weighted network
This function calculates closeness scores for nodes in a weighted network based on the distance_w-function.
closeness_w(net, directed=NULL, gconly=TRUE, precomp.dist=NULL, alpha=1)
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. |
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).
version 1.0.0, taken, with permission, from package tnet
Tore Opsahl; https://toreopsahl.com/
## 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)
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