Extracting Hidden Information from Knowledge Networks

Sergei Maslov and Yi-Cheng Zhang
Phys. Rev. Lett. 87, 248701 – Published 27 November 2001
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Abstract

We develop a method allowing us to reconstruct individual tastes of customers from a sparsely connected network of their opinions on products, services, or each other. Two distinct phase transitions occur as the density of edges in this network is increased: Above the first, macroscopic prediction of tastes becomes possible; while above the second, all unknown opinions can be uniquely reconstructed. We illustrate our ideas using a simple Gaussian model, which we study using both field-theoretical methods and numerical simulations. We point out a potential relevance of our approach to the field of bioinformatics.

  • Received 6 April 2001

DOI:https://doi.org/10.1103/PhysRevLett.87.248701

©2001 American Physical Society

Authors & Affiliations

Sergei Maslov1,2 and Yi-Cheng Zhang2

  • 1Department of Physics, Brookhaven National Laboratory, Upton, New York 11973
  • 2Institut de Physique Théorique, Université de Fribourg, CH-1700, Fribourg, Switzerland

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Vol. 87, Iss. 24 — 10 December 2001

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