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On the mathematical background of Google PageRank algorithm

Author

Listed:
  • Alberto Peretti

    (Department of Economics (University of Verona))

  • Alberto Roveda

    (Department of Economics (University of Verona))

Abstract

The PageRank algorithm, the kernel of the method used by Google Search to give us the answer of a search we are asking in the web, contains a lot of mathematics. Maybe one could say that graphs and Markov chains theories are in the background, while the crucial steps are in a linear algebra context, as the eigenvalues of a matrix are involved. In this working paper we deal with all the mathematics we need to explain how the PageRank method works.

Suggested Citation

  • Alberto Peretti & Alberto Roveda, 2014. "On the mathematical background of Google PageRank algorithm," Working Papers 25/2014, University of Verona, Department of Economics.
    • Handle: RePEc:ver:wpaper:25/2014
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    File URL: http://dse.univr.it/home/workingpapers/wp2014n25.pdf
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    Citations

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    Cited by:

    1. Alberto Peretti, 2017. "A linear model for a ranking problem," Working Papers 20/2017, University of Verona, Department of Economics.
    2. Alberto Peretti, 2014. "The importance of Perron-Frobenius Theorem in ranking problems," Working Papers 26/2014, University of Verona, Department of Economics.
    3. Alberto Peretti, 2016. "The algebraic approach to some ranking problems," Working Papers 22/2016, University of Verona, Department of Economics.

    More about this item

    Keywords

    Graph theory; Linear mappings; Eigenvalues; Markov chains.;
    All these keywords.

    JEL classification:

    • C65 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Miscellaneous Mathematical Tools

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