  An efficient strategy for the identification of influential spreaders that
could be used to control epidemics within populations would be of considerable
importance. Generally, populations are characterized by its community
structures and by the heterogeneous distributions of weak ties among nodes
bridging over communities. A strategy for community networks capable of
identifying influential spreaders that accelerate the spread of disease is here
proposed. In this strategy, influential spreaders serve as target nodes. This
is based on the idea that, in k-shell decomposition, weak ties and strong ties
are processed separately. The strategy was used on empirical networks
constructed from online social networks, and results indicated that this
strategy is more accurate than other strategies. Its effectiveness stems from
the patterns of connectivity among neighbors, and it successfully identified
the important nodes. In addition, the performance of the strategy remained
robust even when there were errors in the structure of the network.
