Du Ruijin, Dong Gaogao, Tian Lixin, Wang Minggang, Fang Guochang, Shao Shuai
Nonlinear Science Research Center, Jiangsu University, Zhenjiang, Jiangsu, China.
School of Mathematics Sciences, Nanjing Normal University, Nanjing, Jiangsu, China.
PLoS One. 2016 Oct 5;11(10):e0162362. doi: 10.1371/journal.pone.0162362. eCollection 2016.
We study the overall topological structure properties of global oil trade network, such as degree, strength, cumulative distribution, information entropy and weight clustering. The structural evolution of the network is investigated as well. We find the global oil import and export networks do not show typical scale-free distribution, but display disassortative property. Furthermore, based on the monthly data of oil import values during 2005.01-2014.12, by applying random matrix theory, we investigate the complex spatiotemporal dynamic from the country level and fitness evolution of the global oil market from a demand-side analysis. Abundant information about global oil market can be obtained from deviating eigenvalues. The result shows that the oil market has experienced five different periods, which is consistent with the evolution of country clusters. Moreover, we find the changing trend of fitness function agrees with that of gross domestic product (GDP), and suggest that the fitness evolution of oil market can be predicted by forecasting GDP values. To conclude, some suggestions are provided according to the results.
我们研究全球石油贸易网络的整体拓扑结构特性，如度、强度、累积分布、信息熵和权重聚类。同时也研究了网络的结构演变。我们发现全球石油进出口网络并未呈现典型的无标度分布，而是具有异配性。此外，基于2005.01 - 2014.12期间石油进口值的月度数据，通过应用随机矩阵理论，我们从国家层面研究了复杂的时空动态，并从需求侧分析了全球石油市场的适应性演变。从偏离特征值中可以获得有关全球石油市场的丰富信息。结果表明，石油市场经历了五个不同时期，这与国家集群的演变一致。此外，我们发现适应度函数的变化趋势与国内生产总值（GDP）的变化趋势一致，并表明通过预测GDP值可以预测石油市场的适应性演变。最后，根据研究结果提出了一些建议。
