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 predictive poweracross a set of 60 finite, diverse, static real-world topologies from six categories of social networks . 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.  As a result  , training classifiers with seven classical centralities leads to a nearly maximum average precision function (0.995)  .
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 .
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predictive poweracross a set of 60 finite, diverse, static real-world topologies from six categories of social networks
ranking power
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multi-centrality statistical classifiers , trained on a sample of the nodes from each network,
statistical classifiers using two or more centralities as input
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across diversenetwork cases
over many diverse, static real-world topologies
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class
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combine well with global centralities
benefit from the addition of a global centrality
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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.
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of the interplay between centrality indicators
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centralities
indicators
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across the networks in this study