Structural Change and Random Graph Models in Global Oil Trade Networks

2026-08-28

AI Summary

研究使用网络方法和UN Comtrade数据，通过PageRank、Louvain算法等手段分析了全球石油贸易网络的结构变化，并与随机图模型进行比较。

Abstract

We studied structural change in global oil trade using a network approach. Using UN Comtrade data, we examined the temporal evolution of international trade networks, with an emphasis on crude oil. Weighted in-degree identified major changes in country rankings in 1991, 2011, 2017, and 2021, while PageRank detected pronounced changes around 1991 and 2024. The Louvain algorithm identified clear geographic communities within the overall trade network. In the oil trade network, modularity declined from the 1990s to the 2010s, with node2vec embeddings showing weaker clustering in 2011 than in 1991. We also compared the oil trade network with several random graph models using 3- and 4-node subgraph profiles and machine learning classification. The oil trade network was consistently classified as a Chung-Lu graph, while the Geometric model was not favored, suggesting that a model based on the degree distribution better matched its subgraph profiles than the other models considered.

Problem

• structural change
• oil trade networks
• network approach
• community structure
• random graph models

Innovation

• network approach
• PageRank
• Louvain algorithm
• Chung-Lu model
• subgraph profiles