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