Features and Evolution of Global Energy Trade Patterns from the Perspective of Complex Networks

Abstract

As an integral part of economic trade, energy trade is crucial to international dynamics and national interests. In this study, an international energy trade network is constructed by abstracting countries as nodes and representing energy trade relations as edges. A variety of indicators are designed in terms of networks, nodes, bilaterals, and communities to analyze the temporal and spatial evolution of the global energy trade network from 2001 to 2020. The results indicate that network density and strength have been steadily increasing since the beginning of the 21st century. It is observed that the position of the United States as the core of the international energy market is being impacted by emerging developing countries, thus affecting the existing trade balance based on topological analysis. The weighted analysis of bilateral relations demonstrates that emerging countries such as China, Brazil, and Saudi Arabia are pursuing closer cooperation. The community analysis reveals that an increasing number of countries possess strong energy trade capabilities, resulting in a corresponding increase in energy trade volumes.

Feature selection and interpretability analysis of compound faults in rolling bearings based on the causal feature weighted network

“…Although there is limited literature utilizing weighted networks to study the impact of heterogeneity in causal relationships between compound fault features on feature selection, existing research on the structural characteristics of weighted networks is of referential significance to this paper. Weighted networks can depict the diversity between different links and represent the interaction strength between nodes in the form of weights, such as the traffic in trade networks [31], the bandwidth of internet fibers [32], and the passenger flow in aviation networks [33]. Compared to unweighted networks, weighted networks offer a richer expression in aspects like node centrality measurement and dynamic characteristics.…”

Modular Structures of Trade Flow Networks in International Commodities

“…Clement et al employed a module detection approach along with a supervised learning in the agricultural-food trade network [23]. Cong et al analyzed the international energy trade network and detected modules evolving over time [24]. Wang et al examined the evolution pattern of African countries oil trade under the changing in the global oil market [25].…”