[1]YAN Jiameng,XU Libo,LI Xingsen,et al.Dynamic analysis method of importance of science and education interpersonal network nodes based on extension clustering[J].CAAI Transactions on Intelligent Systems,2019,14(5):915-921.[doi:10.11992/tis.201811012]
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Dynamic analysis method of importance of science and education interpersonal network nodes based on extension clustering

CAAI Transactions on Intelligent Systems[ISSN 1673-4785/CN 23-1538/TP] Volume: 14 Number of periods: 2019 5 Page number: 915-921 Column: 学术论文—智能系统 Public date: 2019-09-05
Title:
Dynamic analysis method of importance of science and education interpersonal network nodes based on extension clustering
Author(s):
YAN Jiameng1; 2;  XU Libo2;  LI Xingsen3;  PANG Chaoyi2;  DONG Ruichen4
1. Polytechnic Institute, Zhejiang University, Hangzhou 310015, China;
2. School of Computer and Data Engineering, Ningbo Institute of Technology, Zhejiang University, Ningbo 315100, China;
3. Research Institute of Extenics and Innovation Methods, Guangdong University of Technology, Guangzhou 510006, China;
4. Department of International School, Beijing University of Posts and Telecommunications, Beijing 100876, China
Keywords:
complex network; node importance; multi-attribute; extenics; extension clustering; extension theory; matter element; correlation function
CLC:
TP301.6
DOI:
10.11992/tis.201811012
Abstract:
At present, the identification of the influence of key nodes in complex social network is usually static and does not involve the analysis of dynamic changes. The extension clustering method is used to quantitatively analyze the interpersonal network of science and education under dynamic changes. First, the importance of each node is calculated by multi-attribute decision-making method. Then the comprehensive importance of the node is calculated by the coefficient of variation weight method. It is then classified, and the standard positive domain and the positive domain are acquired. The extension correlation function is used to calculate the degree of association between each node and each level. The level with the highest correlation value is the corresponding level of the node. Finally, the importance level of the same social network node at different time points is analyzed. The extension clustering method aims to dynamically determine the importance of network nodes. Finally, the effectiveness of the method is verified using an example.
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