Research on the OBOR trade network based on the maximal weighted spanning tree and centrality analysis strategy

被引:0
作者
Zhou, Hui [1 ,2 ]
Xiao, Fuxian [2 ]
Liu, Hongling [2 ,3 ]
Liu, Jie [2 ,3 ,4 ]
机构
[1] Wuhan Textile Univ, Sch Media, Wuhan 430073, Peoples R China
[2] Wuhan Textile Univ, Res Ctr Nonlinear Sci, Wuhan 430073, Peoples R China
[3] Wuhan Textile Univ, Dept Appl Math, Wuhan 430073, Peoples R China
[4] Wuhan Textile Univ, Sch Econ, Wuhan 430073, Peoples R China
来源
2022 34TH CHINESE CONTROL AND DECISION CONFERENCE, CCDC | 2022年
关键词
OBOR trade network; complex network; community analysis; centrality analysis; MST;
D O I
10.1109/CCDC55256.2022.10033662
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
In this brief paper, the textile materials, clothing, and shoes trade network along one belt one road countries was constructed and analyzed (mainly based on STIC 83 and STIC 84 hybrid trade data in 2004-2019). Based on basic characters calculation, trade member community detective, membership importance ranking by page rank value, bridging centrality calculation, and network visualization analysis, the topological structure and evolution trend of the One Belt One Road (OBOR) trade network was analyzed on details. The network diameter and average length of the path between countries is relatively small with the development of world globalization process. The analysis of node PR value's distribution analysis showed that, Czechia, India, Singapore, United Arab Emirates, Viet Nam, Myanmar, Pakistan, Poland, Sri Lanka, Turkey, Ukraine, China, Indonesia, Malaysia, Romania, Russian Federation, and Thailand behaved important function for trade communications. The analysis of node betweenness centrality value's distribution analysis showed that, Czechia, India, Singapore, United Arab Emirates, Viet Nam, Myanmar, Pakistan, Poland, Sri Lanka, Turkey, Ukraine, China, Indonesia, Malaysia, Romania, Russian Federation, Thailand behaved most important media Junction. We also found that, the 65 One Belt One Road countries in the trade network behaves like a three layers small-world network, and it can be divided into 3 main community according to geography address on the earth along the OBOR line according to community analysis, in which China occupies a very important position in the whole trade network. We also tried to check whether the maximal weighted spanning tree will occupy most of such a trade network or not. The analysis of maximal weighted spanning tree of the trade network further revealed that, the betweenness centrality and bridging centrality distributions of constructed MST is different from each other. The betweenness centrality distribution of weighted MST is something like power-law. The bridging centrality distribution of the weighted MST is homogenous. China, United Arab Emirates, Iran, Russian Federation, Thailand, Viet Nam, Singapore, India, Turkey, Indonesia, Saudi Arabia play an important role in such an import trade network of countries along the OBOR line.
引用
收藏
页码:5853 / 5859
页数:7
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