Google Matrix of the World Trade Network

Ermann, Leonardo; Shepelyansky, Dima L.

doi:10.12693/aphyspola.120.a-158

Cited by 41 publications

(89 citation statements)

References 27 publications

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“…in [14,18,21]), for universities we have significantly stronger concentration around diagonal (K U = K * U ). It looks like that ingoing and outgoing information for articles of universities is approximately conserved like it is approximately the case of commercial flows on the world trade network where the countries try to keep their economic balance [30]. Thus we can say that universities of Oxford, Yale, Uppsala are more communicative (located below diagonal) while those of Cambridge, Princeton, Chicago are much less communicative (located above diagonal).…”

Section: Comparison Of Wrwu and Arwumentioning

confidence: 99%

Wikipedia ranking of world universities

2016

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Abstract. We use the directed networks between articles of 24 Wikipedia language editions for producing the Wikipedia Ranking of World Universities (WRWU) using PageRank, 2DRank and CheiRank algorithms. This approach allows to incorporate various cultural views on world universities using the mathematical statistical analysis independent of cultural preferences. The Wikipedia ranking of top 100 universities provides about 60 percent overlap with the Shanghai university ranking demonstrating the reliable features of this approach. At the same time WRWU incorporates all knowledge accumulated at 24 Wikipedia editions giving stronger highlights for historically important universities leading to a different estimation of efficiency of world countries in university education. The historical development of university ranking is analyzed during ten centuries of their history.

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Section: Comparison Of Wrwu and Arwumentioning

confidence: 99%

Wikipedia ranking of world universities

2016

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“…Therefore, to determine the centrality of node i it exploits not only the amount of its incoming links (as approximated for instance by the strength of node i ), but it also considers how its neighbourhood is connected to i. This feature makes the PageRank an appealing indicator and motivates the exploitation of its variants even in several economic and social fields, such as: financial networks and the assessment of systemic risk (Battiston et al, 2012;Hautsch et al, 2014), social networks (Kwak et al, 2010), multiplex networks (Halu et al, 2013), trade networks (Ermann and Shepelyansky, 2011), urban transportation networks (Agryzkov et al, 2012), the ECommerce (Oestreicher-Singer and Sundararajan, 2012), among others.…”

Section: Network Measuresmentioning

confidence: 99%

EU Ets Facets in the Net: How Account Types Influence the Structure of the System

Borghesi

Flori

2016

SSRN Journal

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In this work, we investigate which countries have been more central during Phases I and II of the European Emission Trading Scheme (EU ETS) with respect to the different types of accounts operating in the system. We borrow a set of centrality measures from Network Theory's tools to describe how the structure of the system has evolved over time and to identify which countries have been in the core or in the periphery of the network. In doing this, we investigate by means of extensive partitions on the different types of accounts and transactions characterizing the EU ETS whether the role of intermediaries (approximated by Person Holding Accounts -PHAs) has affected the overall structure of the system. Preliminary findings over the period [2005][2006][2007][2008][2009][2010][2011][2012] suggest that PHAs have played a prominent role in the transaction of permits, heavily influencing the configuration of the system. This motivates further research on the impact of non-regulated entities in the EU ETS design.

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“…We note that there has been a number of other investigations of the WTN reported in [18,19,20,21,22,23,24]. However, in this work we have the new important elements, developed in [12,13,15]: the analysis of PageRank and CheiRank probabilities corresponding to direct and inverted network flows and related to Import and Export; democratic treatment of countries combined with the contributions of sectors (or products) being proportional to their commercial exchange fractions. We point out that the OECD-WTO TiVA database of economic activities between world countries and activity sectors has been created very recently (2013) and thus this work represents new studies of the WNEA data evolving in time, extending the results reported recently in [15].…”

Section: Introductionmentioning

confidence: 96%

Google matrix of the world network of economic activities

2015

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Using the new data from the OECD-WTO world network of economic activities we construct the Google matrix G of this directed network and perform its detailed analysis. The network contains 58 countries and 37 activity sectors for years 1995, 2000, 2005, 2008, 2009. The construction of G, based on Markov chain transitions, treats all countries on equal democratic grounds while the contribution of activity sectors is proportional to their exchange monetary volume. The Google matrix analysis allows to obtain reliable ranking of countries and activity sectors and to determine the sensitivity of CheiRank-PageRank commercial balance of countries in respect to price variations and labor cost in various countries. We demonstrate that the developed approach takes into account multiplicity of network links with economy interactions between countries and activity sectors thus being more efficient compared to the usual exportimport analysis. Our results highlight the striking increase of the influence of German economic activity on other countries during the period 1995 to 2009 while the influence of Eurozone decreases during the same period. We compare our results with the similar analysis of the world trade network from the UN COMTRADE database. We argue that the knowledge of network structure allows to analyze the effects of economic influence and contagion propagation over the world economy. PACS. 89.75.Fb Structures and organization in complex systems -89.65.Gh Econophysics -89.75.Hc Networks and genealogical trees -89.20.Hh World Wide Web, Internet arXiv:1507.03278v1 [q-fin.ST] 12 Jul 2015

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Google Matrix of the World Trade Network

Cited by 41 publications

References 27 publications

Wikipedia ranking of world universities

Wikipedia ranking of world universities

EU Ets Facets in the Net: How Account Types Influence the Structure of the System

Google matrix of the world network of economic activities

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