Node importance recognition algorithm based on adjacency information entropy in networks

被引：0
|
作者：
Hu G. [1 ,3 ]
Xu X. [2 ]
Gao H. [1 ]
Guo X. [3 ]
机构：
[1] School of Management Science and Engineering, Anhui University of Technology, Ma'anshan
[2] Science and Technology on Information Systems Engineering Laboratory, National University of Defense Technology, Changsha
[3] School of Transportation, Southeast University, Nanjing
来源：
Xitong Gongcheng Lilum yu Shijian | / 3卷 / 714-725期
基金：
中国国家自然科学基金;
关键词：
Adjacency degree; Complex network; Information entropy; Node importance;
D O I：
10.12011/1000-6788-2018-1805-12
中图分类号：
学科分类号：
摘要：
By studying the relationship between nodes and their direct and indirect adjacent nodes, an algorithm for identifying the importance of network nodes based on adjacent information entropy is explored. The algorithm only needs to extract the relationships between all nodes and their direct and indirect neighbors, and it can calculates the information-entropy of each node by the degree of adjacency of each node in the network, the numerical size of information-entropy shows the importance of nodes in the network. The applicability of the algorithm is expanded to simulate a basic network, an undirected weightless ARPA network and a weighted directed ARPA network. In addition, we prove the accuracy of the algorithm based on the number and scale of sub-networks formed, which can reflects the nature of the importance of deleted-node in the network. © 2020, Editorial Board of Journal of Systems Engineering Society of China. All right reserved.
引用
收藏
页码：714 / 725
页数：11
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