  How to identify influential nodes in social networks is of theoretical
significance, which relates to how to prevent epidemic spreading or cascading
failure, how to accelerate information diffusion, and so on. In this Letter, we
make an attempt to find \emph{effective multiple spreaders} in complex networks
by generalizing the idea of the coloring problem in graph theory to complex
networks. In our method, each node in a network is colored by one kind of color
and nodes with the same color are sorted into an independent set. Then, for a
given centrality index, the nodes with the highest centrality in an independent
set are chosen as multiple spreaders. Comparing this approach with the
traditional method, in which nodes with the highest centrality from the
\emph{entire} network perspective are chosen, we find that our method is more
effective in accelerating the spreading process and maximizing the spreading
coverage than the traditional method, no matter in network models or in real
social networks. Meanwhile, the low computational complexity of the coloring
algorithm guarantees the potential applications of our method.
