A Feature-Based Approach for the Redefined Link Prediction Problem in Signed Networks

Abstract

Link prediction is an important research issue in social networks, which can be applied in many areas, such as trust-aware business applications and viral marketing campaigns. With the rise of signed networks, the link prediction problem becomes more complex and challenging as it introduces negative relations among users. Instead of predicting future relation for a pair of users, however, the current research focuses on distinguishing whether a certain link is positive or negative, on the premise of the link existence. The situation that two users do not have relation (i.e., no-relation) is also not considered, which actually is the most common case in reality. In this paper, we redefine the link prediction problem in signed social networks by also considering “no-relation” as a future status of a node pair. To understand the underlying mechanism of link formation in signed networks, we propose a feature framework on the basis of a thorough exploration of potential features for the newly identified problem. We find that features derived from social theories can well distinguish these three social statuses. Grounded on the feature framework, we adopt a multiclass classification model to leverage all the features, and experiments show that our method outperforms the state-of-the-art methods.