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Identifying important nodes in complex networks is essential in theoreticaland applied fields. A small number of such nodes have deterministic power todecide information spreading, so it is of importance to find a set of nodesthat maximize the propagation in networks. Based on baseline ranking methods,various improved methods were proposed, but there does not exist one enhancedmethod that covers all the base methods. In this paper, we propose a penalizedmethod called RCD-Map, which is short for resampling community detection tomaximize propagation, on five baseline ranking methods(Degree centrality,Closeness centrality, Betweennees centrality, K-shell and PageRank) with nodes'local community information. We perturbed the original graph by resampling todecrease the biases and randomness brought by community detection methods-bothoverlapping and non-overlapping methods. To assess the performance of ouridentifying method, SIR(susceptible-infected-recovered) model is applied tosimulate the information propagation process. The result shows that methodswith penalties perform better with a vaster propagation range in general.

Resampling community detection to maximize propagation in complex network