How Google’s PageRank predicts Nobel Prize winners

Google, Inc.

Sergej Maslow and Sidney Redner, both researchers have found out that Google‘s Page Rank algorithm could be used to predict future nobel price winners based on interlinked scientific papers. Scientific papers are interlinked by references, just like websites are which means they can be ranked by googles page rank algorithm. Taking into account how important a paper is, it is possible to generate a list of wighted ranked scientific papers.

The researchers have applied the algorithm to 353.268 articles published by the American Physical Society since 1893 and generated a ranked list. (Paper: “Promise and Pitfalls of Extending Google’s PageRank Algorithm to Citation Networks”).  Interesting is that nearly all of the authors are nobel price winners.

KenntuckyFC writes:

The top 10 papers by Google Pageranking are:

  1. Unitary Symmetry & Leptonic Decays by Cabibbo
  2. Theory of Superconductivity by Bardeen, Cooper & Schrieffer
  3. Self-Consistent Equations . . . by Kohn & Sham
  4. Inhomogeneous Electron Gas by Hohenberg & Kohn
  5. A Model of Leptons by Weinberg
  6. Crystal Statistics . . . by Onsager
  7. Theory of the Fermi Interaction by Feynman & Gell-Mann
  8. Absence of Diffusion in . . . by Anderson
  9. The Theory of Complex Spectra by Slater
  10. Scaling Theory of Localization by Abrahams, Anderson, et al.

That’s an impressive list, not least because most of these authors are Nobel Prize winners. (Curiously the author of the top paper, Nicola Cabibbo, is not. That ought to be of interest to the Nobel committee who awarded Makoto Kobayashi and Toshihide Maskawa the 2008 Nobel Prize for physics for work that was heavily based on Cabibbo’s ideas.)

All of which suggests an idea. Mining the later entries in this list might be an good way of predicting future prize winners. So get your bets in before the bookies get wind of it.

Redner and Maslov conclude: “Google’s PageRank algorithm and its modifications hold great promise for quantifying the impact of scientific publications.”

Well that’s an interesting example on how interlinked data can be processed in order to predict meaningful information. In terms of Semantic Web Linking of Open Data should be provided by every new application on internet. This example illustrates how interlinking of library data increases the possibility to generate new information.

(via slashdot)

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