Predicting Positive and Negative Relationships in Large Social Networks.

ABSTRACT: In a social network, users hold and express positive and negative attitudes (e.g. support/opposition) towards other users. Those attitudes exhibit some kind of binary relationships among the users, which play an important role in social network analysis. However, some of those binary relationships are likely to be latent as the scale of social network increases. The essence of predicting latent binary relationships have recently began to draw researchers' attention. In this paper, we propose a machine learning algorithm for predicting positive and negative relationships in social networks inspired by structural balance theory and social status theory. More specifically, we show that when two users in the network have fewer common neighbors, the prediction accuracy of the relationship betwe

SUBMITTER: Wang GN 

PROVIDER: S-EPMC4468140 | biostudies-literature | 2015

REPOSITORIES: biostudies-literature