Department of Control and Systems Engineering, Nanjing University, Nanjing, 210093, China.
College of Biological Sciences and Technology, Beijing Forestry University, Beijing, 100083, China.
Sci Rep. 2021 Nov 24;11(1):22829. doi: 10.1038/s41598-021-02306-y.
It is still a hot research topic to identify node importance in complex networks. Recently many methods have been proposed to deal with this problem. However, most of the methods only focus on local or path information, they do not combine local and global information well. In this paper, a new model to identify node importance based on Decision-making Trial and Evaluation Laboratory (DEMATEL) is presented. DEMATEL method is based on graph theory which takes the global information into full consideration so that it can effectively identify the importance of one element in the whole complex system. Some experiments based on susceptible-infected (SI) model are used to compare the new model with other methods. The applications in three different networks illustrate the effectiveness of the new model.
在复杂网络中识别节点重要性仍然是一个热门的研究课题。最近已经提出了许多方法来解决这个问题。然而,大多数方法仅关注局部或路径信息,它们没有很好地结合局部和全局信息。在本文中,提出了一种基于决策试验和评价实验室(DEMATEL)的新模型来识别节点重要性。DEMATEL 方法基于图论,充分考虑了全局信息,因此可以有效地识别整个复杂系统中一个元素的重要性。基于易感染-感染(SI)模型的一些实验用于将新模型与其他方法进行比较。在三个不同网络中的应用说明了新模型的有效性。