  We study online social networks in which relationships can be either positive
(indicating relations such as friendship) or negative (indicating relations
such as 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. We find that the signs of links in the underlying
social networks can be predicted with high accuracy, using models that
generalize across this diverse range of sites. These models provide insight
into some of the fundamental principles that drive the formation of signed
links in networks, shedding light on theories of balance and status from social
psychology; they also suggest social computing applications by which the
attitude of one user toward another can be estimated from evidence provided by
their relationships with other members of the surrounding social network.
