title: Extracting hidden information from knowledge networks.
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.
authors.affiliation: Department of Physics, Brookhaven National Laboratory, Upton, New York 11973, USA.
authors.forename: S
authors.initials: S
authors.lastname: Maslov
authors.forename: Y C
authors.initials: YC
authors.lastname: Zhang
mesh_terms: D003258:Consumer Behavior; D006801:Humans; D018803:Models, Economic