Institut de Recherche en Informatique de Toulouse / Toulouse INP, Université de Toulouse, Toulouse, France.
Laboratoire de Physique Théorique du CNRS/IRSAMC, Université de Toulouse, Toulouse, France.
PLoS One. 2018 Aug 24;13(8):e0201397. doi: 10.1371/journal.pone.0201397. eCollection 2018.
Interactions between countries originate from diverse aspects such as geographic proximity, trade, socio-cultural habits, language, religions, etc. Geopolitics studies the influence of a country's geographic space on its political power and its relationships with other countries. This work reveals the potential of Wikipedia mining for geopolitical study. Actually, Wikipedia offers solid knowledge and strong correlations among countries by linking web pages together for different types of information (e.g. economical, historical, political, and many others). The major finding of this paper is to show that meaningful results on the influence of country ties can be extracted from the hyperlinked structure of Wikipedia. We leverage a novel stochastic matrix representation of Markov chains of complex directed networks called the reduced Google matrix theory. For a selected small size set of nodes, the reduced Google matrix concentrates direct and indirect links of the million-node sized Wikipedia network into a small Perron-Frobenius matrix keeping the PageRank probabilities of the global Wikipedia network. We perform a novel sensitivity analysis that leverages this reduced Google matrix to characterize the influence of relationships between countries from the global network. We apply this analysis to two chosen sets of countries (i.e. the set of 27 European Union countries and a set of 40 top worldwide countries). We show that with our sensitivity analysis we can exhibit easily very meaningful information on geopolitics from five different Wikipedia editions (English, Arabic, Russian, French and German).
国家之间的相互作用源于地理邻近、贸易、社会文化习惯、语言、宗教等多个方面。地缘政治学研究一个国家的地理空间对其政治权力及其与其他国家关系的影响。这项工作揭示了从维基百科挖掘进行地缘政治研究的潜力。实际上，维基百科通过将网页链接在一起提供不同类型的信息（例如经济、历史、政治等），为国家之间的联系提供了坚实的知识和强大的关联。本文的主要发现是表明，可以从维基百科的超链接结构中提取关于国家联系影响的有意义结果。我们利用一种新的随机矩阵表示法，即复杂有向网络的马尔可夫链的约化 Google 矩阵理论。对于选择的一小部分节点，约化 Google 矩阵将百万节点大小的维基百科网络的直接和间接链接集中到一个小的佩尔隆-弗罗贝尼乌斯矩阵中，保持全球维基百科网络的 PageRank 概率。我们进行了一项新颖的敏感性分析，利用这个约化 Google 矩阵从全球网络中描述国家关系的影响。我们将此分析应用于两组选定的国家（即 27 个欧盟国家和 40 个全球顶级国家）。我们表明，通过我们的敏感性分析，我们可以从五个不同的维基百科版本（英语、阿拉伯语、俄语、法语和德语）轻松展示关于地缘政治的非常有意义的信息。
