Towards two-dimensional search engines
arXiv:1106.6215 · doi:10.1088/1751-8113/45/27/275101
Abstract
We study the statistical properties of various directed networks using ranking of their nodes based on the dominant vectors of the Google matrix known as PageRank and CheiRank. On average PageRank orders nodes proportionally to a number of ingoing links, while CheiRank orders nodes proportionally to a number of outgoing links. In this way the ranking of nodes becomes two-dimensional that paves the way for development of two-dimensional search engines of new type. Statistical properties of information flow on PageRank-CheiRank plane are analyzed for networks of British, French and Italian Universities, Wikipedia, Linux Kernel, gene regulation and other networks. A special emphasis is done for British Universities networks using the large database publicly available at UK. Methods of spam links control are also analyzed.
22 pages, 16 figures. Additional data available at http://www.quantware.ups-tlse.fr/QWLIB/dvvadi/
References in corpus (7)
- Two-dimensional ranking of Wikipedia articles
- Spectral properties of the Google matrix of the World Wide Web and other directed networks
- Google matrix, dynamical attractors and Ulam networks
- Google matrix and Ulam networks of intermittency maps
- Towards Google matrix of brain
- Fractal Weyl law for Linux Kernel Architecture
- Google matrix of business process management
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