  The identification of the minimal set of nodes that maximizes the propagation
of information is one of the most relevant problems in network science. In this
paper, we introduce a new method to find the set of initial spreaders to
maximize the information propagation in complex networks. We evaluate this
method in assortative networks and verify that degree-degree correlation plays
a fundamental role in the spreading dynamics. Simulation results show that our
algorithm is statistically similar, regarding the average size of outbreaks, to
the greedy approach in real-world networks. However, our method is much less
time consuming than the greedy algorithm.
