  A pivotal idea in network science, marketing research and innovation
diffusion theories is that a small group of nodes -- called influencers -- have
the largest impact on social contagion and epidemic processes in networks.
Despite the long-standing interest in the influencers identification problem in
socio-economic and biological networks, there is not yet agreement on which is
the best identification strategy. State-of-the-art strategies are typically
based either on heuristic centrality metrics or on analytic arguments that only
hold for specific network topologies or peculiar dynamical regimes. Here, we
leverage the recently introduced random-walk effective distance -- a
topological metric that estimates almost perfectly the arrival time of
diffusive spreading processes on networks -- to introduce a new centrality
metric which quantifies how close a node is to the other nodes. We show that
the new centrality metric significantly outperforms state-of-the-art metrics in
detecting the influencers for global contagion processes. Our findings reveal
the essential role of the network effective distance for the influencers
identification and lead us closer to the optimal solution of the problem.
