Multi-cultural Wikipedia mining of geopolitics interactions leveraging reduced Google matrix analysis

Abstract

Geopolitics focuses on political power in relation to geographic space. Interactions among world countries have been widely studied at various scales, observing economic exchanges, world history or international politics among others. This work exhibits the potential of Wikipedia mining for such studies. Indeed, Wikipedia stores valuable fine-grained dependencies among countries by linking webpages together for diverse types of interactions (not only related to economical, political or historical facts). We mine herein the Wikipedia networks of several language editions using the recently proposed method of reduced Google matrix analysis. This approach allows to establish direct and hidden links between a subset of nodes that belong to a much larger directed network. Our study concentrates on 40 major countries chosen worldwide. Our aim is to offer a multicultural perspective on their interactions by comparing networks extracted from five different Wikipedia language editions, emphasizing English, Russian and Arabic ones. We demonstrate that this approach allows to recover meaningful direct and hidden links among the 40 countries of interest.

Analysis of world terror networks from the reduced Google matrix of Wikipedia

“…The weights of these three G R components are W rr =0.0644, W pr =0.8769 and W qr =0.0587 (the weight is given by the sum of all matrix elements divided by N r , thus W rr + W qr + W qr = 1). The dominant component is G pr but as stated above it is approximately given by columns of the PageRank vector so that the most interesting information is provided by G rr and especially the component G qr given by indirect links [8,9].…”

What is the central bank of Wikipedia?

“…The indirect links take into account all possible pathways from one node to another one via the global network of 5 millions of Wikipedia articles. The efficiency of the REGOMAX method has been demonstrated for various examples such as interactions between politicians [20], countries [21], world universities [22] and cancer networks [23]. The results of our analysis show that the central bank of Wikipedia is not at all ICB China with the largest asset K a = 1 but Goldman Sachs which has the asset rank K a = 35.…”

“…We determine the PageRank index of countries and banks for N r = 255. As discussed in [11,21] the top PageRank positions of the global matrix G are taking by countries. The PageRank local index K b marking only banks is given in Table 2.…”

“…The weights of the components G pr , G rr , G qr and G qrnd are respectively W pr = 0.8912, W rr = 0.0402, W qr = 0.06859 and W qrnd = 0.04417. The weights of components G rr , G qr are relatively small but as it was discussed above and in [20][21][22] they provide the most interesting information on interactions of nodes. Table 2.…”

“…for interactions between politicians [20], countries [21] and interactions between infectious diseases and countries [32].…”