Xu Xiang, Zhu Cheng, Wang Qingyong, Zhu Xianqiang, Zhou Yun
Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha, 410072, China.
Sci Rep. 2020 Feb 14;10(1):2691. doi: 10.1038/s41598-020-59616-w.
Identifying the vital nodes in networks is of great significance for understanding the function of nodes and the nature of networks. Many centrality indices, such as betweenness centrality (BC), eccentricity centrality (EC), closeness centricity (CC), structural holes (SH), degree centrality (DC), PageRank (PR) and eigenvector centrality (VC), have been proposed to identify the influential nodes of networks. However, some of these indices have limited application scopes. EC and CC are generally only applicable to undirected networks, while PR and VC are generally used for directed networks. To design a more applicable centrality measure, two vital node identification algorithms based on node adjacency information entropy are proposed in this paper. To validate the effectiveness and applicability of the proposed algorithms, contrast experiments are conducted with the BC, EC, CC, SH, DC, PR and VC indices in different kinds of networks. The results show that the index in this paper has a high correlation with the local metric DC, and it also has a certain correlation with the PR and VC indices for directed networks. In addition, the experimental results indicate that our algorithms can effectively identify the vital nodes in different networks.
识别网络中的关键节点对于理解节点功能和网络性质具有重要意义。人们已经提出了许多中心性指标，如介数中心性（BC）、离心率中心性（EC）、接近中心性（CC）、结构洞（SH）、度中心性（DC）、PageRank（PR）和特征向量中心性（VC），以识别网络中有影响力的节点。然而，其中一些指标的应用范围有限。EC和CC通常仅适用于无向网络，而PR和VC通常用于有向网络。为了设计一种更适用的中心性度量方法，本文提出了两种基于节点邻接信息熵的关键节点识别算法。为了验证所提算法的有效性和适用性，在不同类型的网络中与BC、EC、CC、SH、DC、PR和VC指标进行了对比实验。结果表明，本文提出的指标与局部度量DC具有高度相关性，并且对于有向网络，它与PR和VC指标也具有一定的相关性。此外，实验结果表明我们的算法能够有效地识别不同网络中的关键节点。
