Title: Influence maximization on correlated networks through community identification,
Abstract: The identification of the minimal set of nodes that maximizes the propagation
of information is one of the most important 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 on the spreading dynamics. Simulation results show that our
algorithm is statistically similar, in terms of the average size of outbreaks,
to the greedy approach. However, our method is much less time consuming than
the greedy algorithm.