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

2017 ◽  
Vol 381 (33) ◽  
pp. 2677-2685 ◽  
Author(s):  
Klaus M. Frahm ◽  
Samer El Zant ◽  
Katia Jaffrès-Runser ◽  
Dima L. Shepelyansky
Keyword(s):  
Matrix Analysis ◽  
Google Matrix

scholarly journals Google matrix analysis of C.elegans neural network

2014 ◽  
Vol 378 (28-29) ◽  
pp. 1932-1936 ◽  
Author(s):  
V. Kandiah ◽  
D.L. Shepelyansky
Keyword(s):  
Neural Network ◽  
Matrix Analysis ◽  
Google Matrix

scholarly journals Google matrix analysis of worldwide football mercato

2018 ◽  
Author(s):  
José Lages ◽  
Justin Loye ◽  
Célestin Coquidé ◽  
Guillaume Rollin
Keyword(s):  
Large Scale ◽  
World Wide ◽  
Matrix Analysis ◽  
Two Dimensional ◽  
Network Nodes ◽  
Pagerank Algorithm ◽  
Google Matrix ◽  
Football Clubs ◽  
The World ◽  
Different Types

The worldwide football transfer market is analyzed as a directed complex network: the football clubs are the network nodes and the directed edges are weighted by the total amount of money transferred from a club to another. The Google matrix description allows to treat every club independently of their richness and allows to measure for a given club the efficiency of player sales and player acquisitions. The PageRank algorithm, developed initially for the World Wide Web, naturally characterizes the ability of a club to import players. The CheiRank algorithm, also developed to analyze large scale directed complex networks, characterizes the ability of a club to export players. The analysis in the two-dimensional PageRank-CheiRank plan permits to determine the transfer balance of the clubs in a more subtle manner than the traditional import-export scheme. We investigate the 2017-2018 mercato concerning 2296 clubs, 6698 player transfers, and 147 player nationalities. The transfer balance is determined globally for different types of player trades (defender, midfielder, forward, …) and for different national football leagues. Although, on average, the network transfer flows from and to clubs are balanced, the discrimination by player type draws a specific portrait of each football club.

scholarly journals Post-Brexit Power of European Union From the World Trade Network Analysis

2021 ◽  
pp. 39-47
Author(s):  
Justin Loye ◽  
Katia Jaffrès-Runser ◽  
Dima L. Shepelyansky
Keyword(s):  
European Union ◽  
World Trade ◽  
Dominant Role ◽  
Matrix Analysis ◽  
The European Union ◽  
Trade Network ◽  
Google Matrix ◽  
The World ◽  
Leading Position

We develop the Google matrix analysis of the multiproduct world trade network obtained from the UN COMTRADE database in recent years. The comparison is done between this new approach and the usual Import-Export description of this world trade network. The Google matrix analysis takes into account the multiplicity of trade transactions thus highlighting in a better way the world influence of specific countries and products. It shows that after Brexit, the European Union of 27 countries has the leading position in the world trade network ranking, being ahead of USA and China. Our approach determines also a sensitivity of trade country balance to specific products showing the dominant role of machinery and mineral fuels in multiproduct exchanges. It also underlines the growing influence of Asian countries.

scholarly journals Google matrix analysis of directed networks

2015 ◽  
Vol 87 (4) ◽  
pp. 1261-1310 ◽  
Author(s):  
Leonardo Ermann ◽  
Klaus M. Frahm ◽  
Dima L. Shepelyansky
Keyword(s):  
Matrix Analysis ◽  
Directed Networks ◽  
Google Matrix

scholarly journals Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network

2021 ◽  
Vol 23 (1) ◽  
pp. 67
Author(s):  
Ekaterina Kotelnikova ◽  
Klaus M. Frahm ◽  
Dima L. Shepelyansky ◽  
Oksana Kunduzova
Keyword(s):  
Heart Failure ◽  
Protein Interactions ◽  
Cardiac Fibrosis ◽  
Matrix Analysis ◽  
Great Promise ◽  
Protein Protein Interactions ◽  
Matrix Algorithms ◽  
Google Matrix ◽  
End Stage Heart Failure ◽  
End Stage

Protein–protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein–protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

scholarly journals Interdependence of Sectors of Economic Activities for World Countries from the Reduced Google Matrix Analysis of WTO Data

Entropy ◽  
2020 ◽  
Vol 22 (12) ◽  
pp. 1407
Author(s):  
Célestin Coquidé ◽  
José Lages ◽  
Dima L. Shepelyansky
Keyword(s):  
The Other ◽  
Matrix Analysis ◽  
Economic Activities ◽  
Eu Countries ◽  
Matrix Algorithm ◽  
Google Matrix ◽  
The Usa ◽  
Other World

We apply the recently developed reduced Google matrix algorithm for the analysis of the OECD-WTO World Network of Economic Activities. This approach allows to determine interdependencies and interactions of economy sectors of several countries, including China, Russia and the USA, properly taking into account the influence of all the other world countries and their economic activities. Within this analysis, we also obtain the sensitivity of EU countries’ economies to the petroleum activity sector. We show that this approach takes into account the multiplicity of economical interactions between countries and activity sectors, thus providing a richer analysis compared to the usual export-import analysis.

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