Open AccessA method of evaluating importance of nodes in complex network based on Tsallis entropy
Author(s) -
Song-Qing Yang,
Yuan Jiang,
Tianchi Tong,
Yuwei Yan,
Ge-Sheng Gan
Publication year - 2021
Publication title -
wuli xuebao
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.199
H-Index - 47
ISSN - 1000-3290
DOI - 10.7498/aps.70.20210979
Subject(s) - computer science , complex network , centrality , node (physics) , entropy (arrow of time) , data mining , monotonic function , evaluation methods , theoretical computer science , algorithm , mathematics , statistics , mathematical analysis , physics , structural engineering , quantum mechanics , world wide web , engineering , reliability engineering
Evaluating the importance of nodes in complex networks is an important topic in the research of network characteristics. Its relevant research has a wide range of applications, such as network supervision and rumor control. At present, many methods have been proposed to evaluate the importance of nodes in complex networks, but most of them have the deficiency of one-sided evaluation or too high time complexity. In order to break through the limitations of existing methods, in this paper a novel method of evaluating the importance of complex network nodes is proposed based on Tsallis entropy. This method takes into account both the local and global topological information of the node. It considers the structural hole characteristics and K-shell centrality of the node and fully takes into account the influence of the node itself and its neighboring nodes. To illustrate the effectiveness and applicability of this method, eight real networks are selected from different fields and five existing methods of evaluating node importance are used as comparison methods. On this basis, the monotonicity index, SIR (susceptible-infectious-recovered) model, and Kendall correlation coefficient are used to illustrate the superiority of this method and the relationship among different methods. Experimental results show that this method can effectively and accurately evaluate the importance of nodes in complex networks, distinguish the importance of different nodes significantly, and can show good accuracy of evaluating the node importance under different proportions of nodes. In addition, the time complexity of this method is \begin{document}$ O({n^2}) $\end{document}, which is suitable for large-scale complex networks.