基于K-shell位置和两阶邻居的复杂网络节点重要性评估方法

K-shell分解法能快速识别复杂网络中的关键节点，但是无法辨别同壳层内节点重要性的差异，并且低估了处于网络边缘位置的高度值节点的重要性。针对这两个问题，提出一种基于K-shell位置和两阶邻居的节点重要性评估方法。该方法根据K-shell分解过程中节点移除的顺序细化节点的全局位置信息，然后综合考虑节点的局部拓扑结构信息和全局位置信息，利用两步长内邻居节点的K-shell位置信息度量节点的重要性。在八个真实网络上用传染病模型进行仿真实验，结果表明，所提方法与其他五种相关方法相比能更准确有效地评估并区分节点的重要性。

Evaluation method of node importance in complex networks based on K-shell position and neighborhood within two steps

K-shell method can quickly identify influential nodes in complex networks. However, it is hard to distinguish the differences in the importance of these nodes. In addition, it underestimates the importance of high degree nodes at the edge of the network. Aiming at these two shortcomings of the K-shell method, this paper proposed an approach based on the K-shell position and neighborhood within two steps to identify influential nodes in complex networks. It refined the global position of the node according to the removal order during K-shell decomposition. Considering the local topology and global position of nodes, the proposed method identified the influential nodes based on the K-shell position of the neighborhood within two steps. To simulate the propagation process with epidemic model on eight real networks, the experiment results show that the proposed method can identify and rank the influential nodes compared with other five relative methods with better accuracy and efficiency.