Negative sign prediction for signed social networks

Sign prediction reveals the underlying relationships between users of signed social networks. Though negative signs usually dominate the final user decision in most real applications, since negative signs cannot be directly propagated between users like positive signs, the research of negative sign prediction is still at its beginning stage. Existing works predict negative signs by analyzing features according to Structural Balance Theory, Social Status Theory, or both. However, these works only involve partial information related to negative signs, which leads to the limited negative sign prediction performances. We therefore propose a novel negative sign prediction method involving negative sign related features comprehensively from different categories. The proposed method contributes to generalize three main categories of negative sign related features: nodes features, triad features and user similarity features. By merging features belonging to these categories via Logistic Regression Model, the proposed method shows superior performances to existing works: the negative sign prediction accuracy can be improved around 5% and F1 score can be improved up to 32.69%. The generalization performance and embeddedness can also be significantly improved by using the proposed method. (C) 2017 Elsevier B.V. All rights reserved.