Accurate prediction of influential spreaders in complex networks via message-passing approach

Identifying the most influential spreaders is one of outstanding problems in physics of complex systems. So far, many approaches have attempted to rank the influence of nodes but there is still the lack of accuracy to single out influential spreaders. Here, we directly tackle the problem of finding important spreaders by solving analytically the expected size of epidemic outbreaks when spreading originates from a single seed. We derive and validate a theory for calculating the size of epidemic outbreaks with a single seed using message-passing approach. In addition, we find that the probability to occur epidemic outbreak is highly dependent on the location of the seed but the size of epidemic outbreak once it occurs is insensitive to the seed. We also show that our approach can be successfully adapted into weighted networks.