  Identifying a set of influential spreaders in complex networks plays a
crucial role in effective information spreading. A simple strategy is to choose
top-$r$ ranked nodes as spreaders according to influence ranking method such as
PageRank, ClusterRank and $k$-shell decomposition. Besides, some heuristic
methods such as hill-climbing, SPIN, degree discount and independent set based
are also proposed. However, these approaches suffer from a possibility that
some spreaders are so close together that they overlap sphere of influence or
time consuming. In this report, we present a simply yet effectively iterative
method named VoteRank to identify a set of decentralized spreaders with the
best spreading ability. In this approach, all nodes vote in a spreader in each
turn, and the voting ability of neighbors of elected spreader will be decreased
in subsequent turn. Experimental results on four real networks show that under
Susceptible-Infected-Recovered (SIR) model, VoteRank outperforms the
traditional benchmark methods on both spreading speed and final affected scale.
What's more, VoteRank is also superior to other group-spreader identifying
methods on computational time.
