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Phys Rev Lett. 2001 Dec 10;87(24):248701. Epub 2001 Nov 27.

Extracting hidden information from knowledge networks.

Author information

  • 1Department of Physics, Brookhaven National Laboratory, Upton, New York 11973, USA.

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.

PMID:
11736544
[PubMed - indexed for MEDLINE]
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