Predicting Positive and Negative Links in Online Social Networks

We study online social networks in which relationships can be both positive (indicating friendship) and negative (indicating opposition or antagonism). Such a mix of positive and negative links arise in a variety of online settings; we study datasets from Epinions, Slashdot and Wikipedia. Despite the diversity of settings considered, we find that the signs of links in the underlying social networks can be predicted with high accuracy using models that generalize across the different domains. These models provide insight into some of the fundamental principles that drive the formation of signed links in networks, and also shed light on theories of balance and status from social psychology.