Before revision:  We show that multi-centrality statistical classifiers , trained on a sample of the nodes from each network, are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.
After revision:  We show that statistical classifiers using two or more centralities as input are instead consistently predictive across diversenetwork cases . Certain pairs of centralities cooperate particularly well in statistically drawing the class boundary between the top spreaders and the rest: local centralities measuring the size of a node's neighbourhood combine well with global centralities such as the eigenvector centrality, closeness, or the core number.