Identifying a set of influential spreaders in complex networks plays acrucial role in effective information spreading. A simple strategy is to choosetop-$r$ ranked nodes as spreaders according to influence ranking method such asPageRank, ClusterRank and $k$-shell decomposition. Besides, some heuristicmethods such as hill-climbing, SPIN, degree discount and independent set basedare also proposed. However, these approaches suffer from a possibility thatsome spreaders are so close together that they overlap sphere of influence ortime consuming. In this report, we present a simply yet effectively iterativemethod named VoteRank to identify a set of decentralized spreaders with thebest spreading ability. In this approach, all nodes vote in a spreader in eachturn, and the voting ability of neighbors of elected spreader will be decreasedin subsequent turn. Experimental results on four real networks show that underSusceptible-Infected-Recovered (SIR) model, VoteRank outperforms thetraditional benchmark methods on both spreading speed and final affected scale.What's more, VoteRank is also superior to other group-spreader identifyingmethods on computational time.
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机译：识别复杂网络中的一组有影响力的传播者在有效的信息传播中起着至关重要的作用。一种简单的策略是根据影响力排序方法（例如PageRank，ClusterRank和$ k $ -shell分解）选择排名靠前的$ r $个节点作为扩展器。此外，还提出了一些启发式方法，例如爬山，SPIN，度数折扣和基于独立集的方法。但是，这些方法可能会导致某些吊具之间靠得太近，以致影响范围或耗时重叠。在此报告中，我们提出了一种简单而有效的迭代方法，称为VoteRank，以识别一组具有最佳扩展能力的分散式扩展器。在这种方法中，所有节点在每个回合中都在一个扩展器中投票，并且随后的选举中，扩展器的邻居的投票能力将降低。在四个真实网络上的实验结果表明，在敏感感染恢复（SIR）模型下，VoteRank在传播速度和最终影响范围上均优于传统基准方法，而且在计算时间上，VoteRank还优于其他群体传播者识别方法。
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