Before revision:   As a result  , training classifiers with seven classical centralities leads to a nearly maximum average precision function (0.995)  .
After revision:  This is, intuitively, because a local centrality may rank highly some nodes which are located in dense, but peripheral regions of the network---a situation in which an additional global centrality indicator can help by prioritising nodes located more centrally. The nodes selected as superspreaders will usually jointly maximise the values of both centralities. As a result , training classifiers with seven classical centralities leads to a nearly maximum average precision function (0.995) .