The performance of cooperation strategies for enhancing the efficiency of international oil trade networks

IF 2.2 4区数学 Q2 MATHEMATICS, INTERDISCIPLINARY APPLICATIONS

Journal of complex networks Pub Date : 2022-01-01 DOI:10.1093/comnet/cnab053

Na Wei;Wen-Jie Xie;Wei-Xing Zhou;Naoki Masuda

引用次数: 4

Abstract

The efficiency of the international oil trade networks (iOTNs) is an important measure of the efficient redistribution of oil resources among various economies. Adopting cooperation strategies between economies can enhance the efficiency of the iOTNs. We design a series of trade cooperation strategies based on trade volumes, geographic locations and local similarities of economies, and quantitatively analyse the impact of new trade relations on the efficiency of the iOTNs under different trade cooperation strategies. We find that the oil trade system rapidly developed into a more efficient system for the flows of resources and market information. When the proportion of newly added trade relationships is fairly large, the win–win strategy can improve the network efficiency the most; otherwise, the common neighbour strategy performs the best.

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提高国际石油贸易网络效率的合作战略执行情况

国际石油贸易网络的效率是衡量各经济体之间石油资源再分配效率的重要指标。采用经济体间的合作策略可以提高物联网的效率。我们基于经济体的贸易量、地理位置和地方相似性设计了一系列贸易合作策略，并定量分析了不同贸易合作策略下新型贸易关系对物联网效率的影响。我们发现，石油贸易系统迅速发展成为一个更有效的资源和市场信息流动系统。当新增贸易关系比例较大时，双赢策略最能提高网络效率;否则，共同邻居策略表现最好。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Journal of complex networks MATHEMATICS, INTERDISCIPLINARY APPLICATIONS-

CiteScore

4.20

自引率

9.50%

发文量

期刊介绍： Journal of Complex Networks publishes original articles and reviews with a significant contribution to the analysis and understanding of complex networks and its applications in diverse fields. Complex networks are loosely defined as networks with nontrivial topology and dynamics, which appear as the skeletons of complex systems in the real-world. The journal covers everything from the basic mathematical, physical and computational principles needed for studying complex networks to their applications leading to predictive models in molecular, biological, ecological, informational, engineering, social, technological and other systems. It includes, but is not limited to, the following topics: - Mathematical and numerical analysis of networks - Network theory and computer sciences - Structural analysis of networks - Dynamics on networks - Physical models on networks - Networks and epidemiology - Social, socio-economic and political networks - Ecological networks - Technological and infrastructural networks - Brain and tissue networks - Biological and molecular networks - Spatial networks - Techno-social networks i.e. online social networks, social networking sites, social media - Other applications of networks - Evolving networks - Multilayer networks - Game theory on networks - Biomedicine related networks - Animal social networks - Climate networks - Cognitive, language and informational network