从国家学科轮廓推断出的全球双边地缘政治互动网络
Worldwide bilateral geopolitical interactions network inferred from national disciplinary profiles
AI总结：
基于国家学科轮廓推断全球双边地缘政治互动网络，利用海森堡模型和聚类方法分析互动关系。
摘要：
国家的学科轮廓被定义为一个向量，其分量是某一学科发表的文章数除以该国总发表量。从基本科学指标（ESI）的学科分类模式出发，我们获得了全球网络的年度学科轮廓，其中每个节点代表一个国家，在两个时间区间[1980-1988]和[1992-2017]内，柏林墙的倒塌成为分水岭。我们分析了学科轮廓时间序列的实证成对交叉相关矩阵。与随机矩阵理论的对比证明，在测量噪声之外，实证交叉相关矩阵带来了真实的信息。根据香农定理作为与测量成对相关性一致的最不结构化模型，学科轮廓的稳态概率分布可以描述为与广义$ n_d $维海森堡模型相关的玻尔兹曼分布。海森堡模型的网络互动集已被推断，并应用了两种聚类方法，即层次聚类和主成分分析。在地缘政治层面，这使我们能够基于物理建模获得全球双边互动的特征。简单的地缘政治分析揭示了所获得结果的一致性，并为更深入的历史分析提供了推动力。为了获得最优的成对互动集，我们使用了伪似然方法。我们分析性地计算了伪似然及其梯度。这些分析计算在任何涉及广义$ n_d $维海森堡模型的贝叶斯推断问题中都值得关注。
英文摘要：
A disciplinary profile of a country is defined as the versor whose components are the numbers of articles produced in a given discipline divided the overall production of the country. Starting from the Essential Science Indicators (ESI) schema of classification of subject area, we obtained the yearly disciplinary profiles of a worldwide graph, where on each node sits a country, in the two time intervals [1980-1988] and [1992-2017], the fall of the Berlin Wall being the watershed. We analyse the empirical pairwise cross-correlation matrices of the time series of disciplinary profiles. The contrast with random matrix theory proves that, beyond measurement noise, the empirical cross-correlation matrices bring genuine information. Arising from the Shannon theorem as the least-structured model consistent with the measured pairwise correlations, the stationary probability distribution of disciplinary profiles can be described by a Boltzmann distribution related to a generalized $n_d$-dimensional Heisenberg model. The set of network interactions of the Heisenberg model have been inferred and to it they have been applied two clusterization methods, hierarchical clustering and principal component analysis. On a geopolitical plane this allow to obtain a characterization of the worldwide bilateral interactions based on physical modeling. A simple geopolitical analysis reveals the consistency of the results obtained and gives a boost to deeper historical analysis. In order to obtain the optimal set of pairwise interactions we used a Pseudo-Likelihood approach. We analytically computed the Pseudo-Likelihood and its gradient. The analytical computations deserve interest in whatever inference Bayesian problem involving a $n_d$-dimensional Heisenberg model.
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