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We study online social networks in which relationships can be either positive(indicating relations such as friendship) or negative (indicating relationssuch as opposition or antagonism). Such a mix of positive and negative linksarise in a variety of online settings; we study datasets from Epinions,Slashdot and Wikipedia. We find that the signs of links in the underlyingsocial networks can be predicted with high accuracy, using models thatgeneralize across this diverse range of sites. These models provide insightinto some of the fundamental principles that drive the formation of signedlinks in networks, shedding light on theories of balance and status from socialpsychology; they also suggest social computing applications by which theattitude of one user toward another can be estimated from evidence provided bytheir relationships with other members of the surrounding social network.

Predicting Positive and Negative Links in Online Social Networks