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Information flow, opinion, and epidemics spread over structured networks.When using individual node centrality indicators to predict which nodes will beamong the top influencers or spreaders in a large network, no single centralityhas consistently good ranking power. We show that statistical classifiers usingtwo or more centralities as input are instead consistently predictive over manydiverse, static real-world topologies. Certain pairs of centralities cooperateparticularly well in statistically drawing the boundary between the topspreaders and the rest: local centralities measuring the size of a node'sneighbourhood benefit from the addition of a global centrality such as theeigenvector centrality, closeness, or the core number. This is, intuitively,because a local centrality may rank highly some nodes which are located indense, but peripheral regions of the network---a situation in which anadditional global centrality indicator can help by prioritising nodes locatedmore centrally. The nodes selected as superspreaders will usually jointlymaximise the values of both centralities. As a result of the interplay betweencentrality indicators, training classifiers with seven classical indicatorsleads to a nearly maximum average precision function (0.995) across thenetworks in this study.

Top influencers can be identified universally by combining classical centralities