  Identifying important nodes in complex networks is essential in theoretical
and applied fields. A small number of such nodes have deterministic power to
decide information spreading, so it is of importance to find a set of nodes
that maximize the propagation in networks. Based on baseline ranking methods,
various improved methods were proposed, but there does not exist one enhanced
method that covers all the base methods. In this paper, we propose a penalized
method called RCD-Map, which is short for resampling community detection to
maximize 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 to
decrease the biases and randomness brought by community detection methods-both
overlapping and non-overlapping methods. To assess the performance of our
identifying method, SIR(susceptible-infected-recovered) model is applied to
simulate the information propagation process. The result shows that methods
with penalties perform better with a vaster propagation range in general.
