  Information flow, opinion, and epidemics spread over structured networks.
When using individual node centrality indicators to predict which nodes will be
among the top influencers or spreaders in a large network, no single centrality
has consistently good ranking power. We show that statistical classifiers using
two or more centralities as input are instead consistently predictive over many
diverse, static real-world topologies. Certain pairs of centralities cooperate
particularly well in statistically drawing the boundary between the top
spreaders and the rest: local centralities measuring the size of a node's
neighbourhood benefit from the addition of a global centrality such as the
eigenvector centrality, closeness, or the core number. 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 of the interplay between
centrality indicators, training classifiers with seven classical indicators
leads to a nearly maximum average precision function (0.995) across the
networks in this study.
