Weighted Measures of Centrality
Another common measure of important vertices is centrality. There are a number of ways that we can measure centrality, but for this lesson, we'll consider two metrics: eigen centrality and closeness. While eigen centrality has already been covered, closeness is another way of assessing centrality. It considers how close any vertex is to all the other vertices. In earlier lessons, we haven't explicitly considered weighted versus unweighted versions of centrality. In this lesson, we'll calculate both weighted and unweighted versions and see if the change is returned.
In the below example, do you expect to see the same vertex each time? What do you think will be the biggest difference between metrics or between weighted and unweighted versions?
Este exercício faz parte do curso
Case Studies: Network Analysis in R
Exercício interativo prático
Experimente este exercício completando este código de exemplo.
# This calculates weighted eigen-centrality
ec_weight <- eigen_centrality(trip_g_simp, directed = TRUE)$vector
# Calculate unweighted eigen-centrality
ec_unweight <- eigen_centrality(___, directed = ___, weights = ___)$vector