Learning to Predict Links by Integrating Structure and Interaction Information in Microblogs

Link prediction in Microblogs by using unsupervised methods has been studied extensively in recent years, which aims to find an appropriate similarity measure between users in the network. However, the measures used by existing work lack a simple way to incorporate the structure of the network and the interactions between users. This leads to the gap between the predictive result and the ground truth value. For example, the F1-measure created by the best method is around 0.2. In this work, we firstly discover the gap and prove its existence. To narrow this gap, we define the retweet similarity to measure the interactions between users in Twitter, and propose a structural-interaction based matrix factorization model for following-link prediction. Experiments based on the real world Twitter data show that our model outperforms state-of-the-art methods.