Yu Yong, Zhou Biao, Chen Linjie, Gao Tao, Liu Jinzhuo
School of Software, Yunnan University, Kunming 650091, China.
Key Laboratory in Software Engineering of Yunnan Province, Yunnan University, Kunming 650091, China.
Entropy (Basel). 2022 Feb 14;24(2):275. doi: 10.3390/e24020275.
In recent years, the identification of the essential nodes in complex networks has attracted significant attention because of their theoretical and practical significance in many applications, such as preventing and controlling epidemic diseases and discovering essential proteins. Several importance measures have been proposed from diverse perspectives to identify crucial nodes more accurately. In this paper, we propose a novel importance metric called node propagation entropy, which uses a combination of the clustering coefficients of nodes and the influence of the first- and second-order neighbor numbers on node importance to identify essential nodes from an entropy perspective while considering the local and global information of the network. Furthermore, the susceptible-infected-removed and susceptible-infected-removed-susceptible epidemic models along with the Kendall coefficient are used to reveal the relevant correlations among the various importance measures. The results of experiments conducted on several real networks from different domains show that the proposed metric is more accurate and stable in identifying significant nodes than many existing techniques, including degree centrality, betweenness centrality, closeness centrality, eigenvector centrality, and H-index.
近年来，复杂网络中关键节点的识别因其在许多应用中的理论和实际意义而备受关注，如预防和控制流行病以及发现关键蛋白质。已经从不同角度提出了几种重要性度量方法，以更准确地识别关键节点。在本文中，我们提出了一种名为节点传播熵的新型重要性度量方法，该方法结合了节点的聚类系数以及一阶和二阶邻居数量对节点重要性的影响，从熵的角度识别关键节点，同时考虑网络的局部和全局信息。此外，易感-感染-移除和易感-感染-移除-易感流行病模型以及肯德尔系数被用于揭示各种重要性度量之间的相关关系。在来自不同领域的几个真实网络上进行的实验结果表明，与许多现有技术（包括度中心性、介数中心性、接近中心性、特征向量中心性和H指数）相比，所提出的度量方法在识别重要节点方面更准确、更稳定。
