  Social networks constitute a new platform for information propagation, but
its success is crucially dependent on the choice of spreaders who initiate the
spreading of information. In this paper, we remove edges in a network at random
and the network segments into isolated clusters. The most important nodes in
each cluster then form a group of influential spreaders, such that news
propagating from them would lead to an extensive coverage and minimal
redundancy. The method well utilizes the similarities between the
pre-percolated state and the coverage of information propagation in each social
cluster to obtain a set of distributed and coordinated spreaders. Our tests on
the Facebook networks show that this method outperforms conventional methods
based on centrality. The suggested way of identifying influential spreaders
thus sheds light on a new paradigm of information propagation on social
networks.
