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使用指数随机图模型分析全球贸易网络。

Analysis of the global trade network using exponential random graph models.

作者信息

Setayesh Amin, Sourati Hassan Zadeh Zhivar, Bahrak Behnam

机构信息

University of Tehran, Tehran, Iran.

出版信息

Appl Netw Sci. 2022;7(1):38. doi: 10.1007/s41109-022-00479-7. Epub 2022 Jun 11.

Abstract

The global trade network has significant importance in analyzing countries' economic exchanges. Therefore, studying the global trade network and the factors influencing its structure is helpful for both economists and political decision makers. Putting these in mind, we try to analyze the global trade network from various viewpoints. We use the backbone filtering methods to construct a network of essential trades between countries. We analyze the structural, economic, geographical, political, and cultural factors and their effect on the global trade network using exponential random graph models. Additionally, we analyze the global trade network evolution using the separable temporal exponential random models. Our results show multiple structural, economic, geographical, and political factors affect the global trade network structure.

摘要

全球贸易网络在分析各国经济交流方面具有重要意义。因此,研究全球贸易网络及其结构的影响因素对经济学家和政治决策者都很有帮助。考虑到这些,我们尝试从不同角度分析全球贸易网络。我们使用骨干过滤方法构建各国之间的关键贸易网络。我们使用指数随机图模型分析结构、经济、地理、政治和文化因素及其对全球贸易网络的影响。此外,我们使用可分离的时间指数随机模型分析全球贸易网络的演变。我们的结果表明,多种结构、经济、地理和政治因素影响全球贸易网络结构。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/e3fe/9187932/c4f7f6c31e3f/41109_2022_479_Fig1_HTML.jpg

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