论文标题
根据混合度中心和局部结构对节点在复杂网络中的扩散影响进行排名
Ranking the spreading influence of nodes in complex networks based on mixing degree centrality and local structure
论文作者
论文摘要
网络的安全性和鲁棒性吸引了各行各业的人们的注意，几个关键节点的损害将导致极严重的后果。在本文中，我们提出了基于节点本身的H索引及其邻居的相对距离的聚类H索引混合（CHM）中心性。从节点本身开始，并与节点周围的拓扑结合起来，确定了节点的重要性及其传播能力。为了评估所提出的方法的性能，我们使用易感感染的（SIR）模型，单调性和分辨率作为实验的评估标准。人工网络和现实世界网络中的实验结果表明，CHM中心性在识别节点重要性及其传播能力方面具有出色的性能。
The safety and robustness of the network have attracted the attention of people from all walks of life, and the damage of several key nodes will lead to extremely serious consequences. In this paper, we proposed the clustering H-index mixing (CHM) centrality based on the H- index of the node itself and the relative distance of its neighbors. Starting from the node itself and combining with the topology around the node, the importance of the node and its spreading capability were determined. In order to evaluate the performance of the proposed method, we use Susceptible-Infected-Recovered (SIR) model, monotonicity and resolution as the evaluation standard of experiment. Experimental results in artificial networks and real-world networks show that CHM centrality has excellent performance in identifying node importance and its spreading capability.
