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Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix

Author

Listed:
  • Samer El Zant
  • Katia Jaffrès-Runser
  • Dima L Shepelyansky

Abstract

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).

Suggested Citation

  • Samer El Zant & Katia Jaffrès-Runser & Dima L Shepelyansky, 2018. "Capturing the influence of geopolitical ties from Wikipedia with reduced Google matrix," PLOS ONE, Public Library of Science, vol. 13(8), pages 1-31, August.
    • Handle: RePEc:plo:pone00:0201397
    DOI: 10.1371/journal.pone.0201397
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    References listed on IDEAS

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    1. Young-Ho Eom & Dima L Shepelyansky, 2013. "Highlighting Entanglement of Cultures via Ranking of Multilingual Wikipedia Articles," PLOS ONE, Public Library of Science, vol. 8(10), pages 1-10, October.
    2. Jim Giles, 2005. "Internet encyclopaedias go head to head," Nature, Nature, vol. 438(7070), pages 900-901, December.
    3. A. O. Zhirov & O. V. Zhirov & D. L. Shepelyansky, 2010. "Two-dimensional ranking of Wikipedia articles," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 77(4), pages 523-531, October.
    4. Young-Ho Eom & Klaus M. Frahm & András Benczúr & Dima L. Shepelyansky, 2013. "Time evolution of Wikipedia network ranking," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(12), pages 1-9, December.
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    Cited by:

    1. Andrei Zinovyev & Urszula Czerwinska & Laura Cantini & Emmanuel Barillot & Klaus M Frahm & Dima L Shepelyansky, 2020. "Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network," PLOS Computational Biology, Public Library of Science, vol. 16(2), pages 1-19, February.
    2. Guillaume Rollin & José Lages & Dima L Shepelyansky, 2019. "Wikipedia network analysis of cancer interactions and world influence," PLOS ONE, Public Library of Science, vol. 14(9), pages 1-26, September.

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