Institut UTINAM, CNRS, UMR 6213, OSU THETA, Université de Bourgogne Franche-Comté, Besançon, France.
Laboratory for Translational Research and Personalized Medicine, Moscow Institute of Physics and Technology, Moscow, Russia.
PLoS One. 2019 Dec 4;14(12):e0225500. doi: 10.1371/journal.pone.0225500. eCollection 2019.
Using the English Wikipedia network of more than 5 million articles we analyze interactions and interlinks between the 34 largest pharmaceutical companies, 195 world countries, 47 rare renal diseases and 37 types of cancer. The recently developed algorithm using a reduced Google matrix (REGOMAX) allows us to take account both of direct Markov transitions between these articles and also of indirect transitions generated by the pathways between them via the global Wikipedia network. This approach therefore provides a compact description of interactions between these articles that allows us to determine the friendship networks between them, as well as the PageRank sensitivity of countries to pharmaceutical companies and rare renal diseases. We also show that the top pharmaceutical companies in terms of their Wikipedia PageRank are not those with the highest market capitalization.
利用拥有超过 500 万篇文章的英文维基百科网络,我们分析了 34 家最大的制药公司、195 个世界国家、47 种罕见肾脏疾病和 37 种癌症之间的相互作用和相互联系。最近开发的使用简化 Google 矩阵(REGOMAX)的算法使我们能够同时考虑这些文章之间的直接马尔可夫转移以及通过全球维基百科网络在它们之间的路径产生的间接转移。这种方法为这些文章之间的相互作用提供了一个紧凑的描述,使我们能够确定它们之间的友谊网络,以及国家对制药公司和罕见肾脏疾病的 PageRank 敏感性。我们还表明,就维基百科 PageRank 而言,顶级制药公司并非市值最高的公司。