  Measures of complex network analysis, such as vertex centrality, have the
potential to unveil existing network patterns and behaviors. They contribute to
the understanding of networks and their components by analyzing their
structural properties, which makes them useful in several computer science
domains and applications. Unfortunately, there is a large number of distinct
centrality measures and little is known about their common characteristics in
practice. By means of an empirical analysis, we aim at a clear understanding of
the main centrality measures available, unveiling their similarities and
differences in a large number of distinct social networks. Our experiments show
that the vertex centrality measures known as information, eigenvector,
subgraph, walk betweenness and betweenness can distinguish vertices in all
kinds of networks with a granularity performance at 95%, while other metrics
achieved a considerably lower result. In addition, we demonstrate that several
pairs of metrics evaluate the vertices in a very similar way, i.e. their
correlation coefficient values are above 0.7. This was unexpected, considering
that each metric presents a quite distinct theoretical and algorithmic
foundation. Our work thus contributes towards the development of a methodology
for principled network analysis and evaluation.
