Farthest-First Traversal for Identifying Multiple Influential Spreaders

We propose a method for identifying multiple influential spreaders in complex networks. This method is based on a farthest-first traversal of the network. The spreaders selected by this method satisfy the two criteria of being dispersed as well as influential in their neighborhood. To examine the influence of the spreaders identified by our method, we perform numerical simulations of SIR-based epidemic spread dynamics. For a range of parameter values, we observe that the epidemic size obtained using the spreaders generated by our method as the initial spreaders is at least as large as the epidemic size obtained using hubs as initial spreaders.