  In this work, we are interested in the applications of big data in the
telecommunication domain, analysing two weeks of datasets provided by Telecom
Italia for Milan and Trento. Our objective is to identify hotspots which are
places with very high communication traffic relative to others and measure the
interaction between them. We model the hotspots as nodes in a graph and then
apply node centrality metrics that quantify the importance of each node. We
review five node centrality metrics and show that they can be divided into two
families: the first family is composed of closeness and betweenness centrality
whereas the second family consists of degree, PageRank and eigenvector
centrality. We then proceed with a statistical analysis in order to evaluate
the consistency of the results over the two weeks. We find out that the ranking
of the hotspots under the various centrality metrics remains practically the
same with the time for both Milan and Trento. We further identify that the
relative difference of the values of the metrics is smaller for PageRank
centrality than for closeness centrality and this holds for both Milan and
Trento. Finally, our analysis reveals that the variance of the results is
significantly smaller for Trento than for Milan.
