FIELD: information technology.
SUBSTANCE: method comprises steps of entering all possible pairs of profiles; constructing a Conditional Random Field model for all profiles and connections between them; for each pair of profiles, calculating the similarity value of their attributes using a string or graph similarity metric; constructing a feature vector from the obtained similarity metric values, which is sent to a machine learning algorithm which calculates unary energy or binary energy for each pair of profiles, wherein the profiles belong to different social graphs; calculating profile similarity; checking whether the obtained profile similarity value exceeds a given threshold value; if so, the pair of profiles is entered into a list of candidates; a priori true projections are selected from the obtained list of candidates; the Conditional Random Field model is broken into independent components; for each component of the model, the optimum configuration of projections is sought; the lists of the found projections are merged for all components of the model.
EFFECT: high efficiency of integrating user profiles of online social networks.
6 dwg
