Extracting Hidden Information from Knowledge Networks

Details Extracting Hidden Information from Knowledge Networks Extracting Hidden Information from Knowledge Networks

2001-04-06

arxiv.org/abs/cond-mat/0104121v2

Physical Review Letters, 87, 248701 (2001)

Physics

Condensed Matter

Statistical Mechanics

published version, 5 pages, 1 figure

Scientific paper

10.1103/PhysRevLett.87.248701

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.

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