  Social Network Analysis (SNA) is used to study the exchange of resources
among individuals, groups, or organizations. The role of individuals or
connections in a network is described by a set of centrality metrics which
represent one of the most important results of SNA. Degree, closeness,
betweenness and clustering coefficient are the most used centrality measures.
Their use is, however, severely hampered by their computation cost. This issue
can be overcome by an algorithm called Game of Thieves (GoT). Thanks to this
new algorithm, we can compute the importance of all elements in a network (i.e.
vertices and edges), compared to the total number of vertices. This calculation
is done not in a quadratic time, as when we use the classical methods, but in
polylogarithmic time. Starting from this we present our results on the
correlation existing between GoT and the most widely used centrality measures.
From our experiments emerge that a strong correlation exists, which makes GoT
eligible as a centrality measure for large scale complex networks.
