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

Influence maximization by rumor spreading on correlated networks through community identification