A variation on the previous experiment. This time, eigenvector centrality is used to control the distance of the nodes from the origin (the closer to the origin, the more central the node). Please note that, even if depicted, the weight of links is not considered when computing a node’s centality.
The result seems not so informative, but it should be tested with a greater number of nodes.
The following R script, adapted from this post, was used to compute the centrality measure:
library(network)
src <- c("A", "A", "A", "A", "A", "B", "B", "B", "B", "C", "C", "D", "D", "E")
dst <- c("B", "C", "D", "F", "G", "D", "E", "F", "G", "D", "E", "E", "F", "F")
edges <- cbind(src, dst)
Net <- as.network(edges, matrix.type = "edgelist", directed=FALSE)
EV <- eigen(as.matrix(Net))
centrality <- data.frame(EV$vectors[,1])
centrality = abs(centrality)
names(centrality) <- "Centrality"
print(centrality)