World influence and interactions of universities from Wikipedia networks

Abstract

We present Wikipedia Ranking of World Universities (WRWU) based on analysis of networks of 24 Wikipedia editions collected in May 2017. With PageRank and CheiRank algorithms we determine ranking of universities averaged over cultural views of these editions. The comparison with the Shanghai ranking gives overlap of 60% for top 100 universities showing that WRWU gives more significance to their historical development. We show that the new reduced Google matrix algorithm allows to determine interactions between leading universities on a scale of ten centuries. This approach also determines the influence of specific universities on world countries. We also compare different cultural views of Wikipedia editions on significance and influence of universities. PACS. 89.75.Fb Structures and organization in complex systems -89.75.Hc Networks and genealogical trees -89.20.Hh World Wide Web, Internet Paris-Sorbonne Pantheon-Sorbonne EPHE ENS Polytechnique USC UW-Madison Florida UT Austin NYU UPenn UCLA Cornell Michigan Chicago Princeton Berkeley MIT Stanford Yale Columbia HarvardHarvard Columbia Yale Stanford MIT Berkeley Princeton Chicago Michigan Cornell UCLA UPenn NYU UT Austin Florida UW-Madison USC Polytechnique ENS EPHE Paris-Sorbonne Pantheon-Sorbonne EPHE ENS Polytechnique USC UW-Madison Florida UT Austin NYU UPenn UCLA Cornell Paris-Sorbonne Pantheon-Sorbonne EPHE ENS Polytechnique USC UW-Madison Florida UT Austin NYU UPenn UCLA Cornell Harvard Columbia Yale Stanford MIT Berkeley Princeton Chicago Michigan Cornell UCLA UPenn NYU UT Austin Florida UW-Madison USC Polytechnique ENS EPHE

Influence of petroleum and gas trade on EU economies from the reduced Google matrix analysis of UN COMTRADE data

“…It is usual for Wikipedia networks that the weight W pr ≈ 0.95 (see e.g. [13,14]) is rather close to unity since G pr is approximately composed from identical columns of PageRank vector, while the remaining weight of about 0.05 is approximately equally distributed between W rr and W qr . We find that for the WTN the situation is different.…”

What is the central bank of Wikipedia?

“…To measure the sensitivity of a country c to a bank b we change the matrix element G R (b → c) by a factor (1 + δ) with δ 1 and renormalize to unity the sum of the column elements associated with . This approach already demonstrated its efficiency as reported in [22,32]. 4…”

“…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.…”

Collective intelligence defines biological functions in Wikipedia as communities in the hidden protein connection network

“…18 Studying the large graph of Wikipedia hyperlinks with a focus on a particular subset of pages can provide 19 interesting insights about certain topics. Thus, for example, Wikipedia networks were explored to establish the 20 top historical figures of human history over 15 centuries [11], the geopolitical relations between countries [9], 21 the leading world universities [7], world influence of infectious and cancer diseases [27,28]. Hierarchical 22 structure of Wikipedia was revealed through application of network community detection algorithms [20].…”

“…The efficiency of the REGOMAX approach has been demonstrated for various Wikipedia networks 300 [7,9,13,27,28], protein networks from SIGNOR database [19], and the multiproduct world trade network 301 from UN COMTRADE database [7]. For the networks of hidden protein connections we applied Markov Clustering Algorithm (MCL) implemented 312 in ClusterMaker plugin for Cytoscape [23] with default parameters (granularity=2.0, edge weight cutoff=1.0, 313 number of iterations=16, maximum residual value=0.001).…”