A novel link prediction method for supervising transitivity process

摘要

Link prediction has become an important area in network analysis in recent years due to its theoretical and practical significance. In this paper, we present a similarity-based prediction method under simultaneous consideration of multiple information sources and the corresponding discrimination ability. We first propose a novel supervised transitivity similarity index (STSI), in which the likelihood ratio in the Bayesian theory is employed to supervise the transitivity process. Then, based on the proposed STSI, we design a supervised transitivity similarity algorithm (STSA) for predicting missing links. Finally, empirical experiments are conducted to demonstrate the effectiveness of the proposed method. The experimental results show that our method can achieve a good performance, compared with other mainstream baselines.