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The structure of an online social network in most cases cannot be describedjust by links between its members. We study online social networks, in whichmembers may have certain attitude, positive or negative toward each other, andso the network consists of a mixture of both positive and negativerelationships. Our goal is to predict the sign of a given relationship based onthe evidences provided in the current snapshot of the network. More precisely,using machine learning techniques we develop a model that after being trainedon a particular network predicts the sign of an unknown or hidden link. Themodel uses relationships and influences from peers as evidences for the guess,however, the set of peers used is not predefined but rather learned during thetraining process. We use quadratic correlation between peer members to trainthe predictor. The model is tested on popular online datasets such as Epinions,Slashdot, and Wikipedia. In many cases it shows almost perfect predictionaccuracy. Moreover, our model can also be efficiently updated as theunderlaying social network evolves.

Inferring Attitude in Online Social Networks Based On Quadratic Correlation