The influence maximization problem (IMP) in complex networks is to address finding a set of key nodes that play vital roles in the information diffusion process, and when these nodes are employed as ”seed nodes”, the diffusion effect is maximized. First, this paper presents a refined network centrality measure, a refined shell (RS) index for node ranking, and then proposes an algorithm for identifying key node sets, namely the reject neighbors algorithm (RNA), which consists of two main sequential parts, i.e., node ranking and node selection. The RNA refuses to select multiple-order neighbors of the seed nodes, scatters the selected nodes from each other, and results in the maximum influence of the identified node set on the whole network. Experimental results on real-world network datasets show that the key node set identified by the RNA exhibits significant propagation capability.

中文翻译：

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RNA：复杂网络中影响最大化的拒绝邻居算法

复杂网络中的影响最大化问题（IMP）旨在解决在信息传播过程中起关键作用的一组关键节点，当将这些节点用作“种子节点”时，扩散效果将最大化。首先，本文提出了一种改进的网络中心性度量，一种用于节点排名的精细化外壳（RS）索引，然后提出了一种用于识别关键节点集的算法，即拒绝邻居算法（RNA），该算法由两个主要的顺序部分组成，即节点排名和节点选择。RNA拒绝选择种子节点的多阶邻居，将选定的节点彼此分散，从而使所标识的节点集对整个网络产生最大影响。