2000 character limit reached
Influence maximization by rumor spreading on correlated networks through community identification (1705.00630v5)
Published 1 May 2017 in physics.soc-ph, cond-mat.stat-mech, and cs.SI
Abstract: 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.