Temporal Link Prediction Based on Dynamic Heterogeneous Information Network

Temporal link prediction on dynamic heterogeneous information networks, aiming to predict both the building times of links and their types, has been widely studied in recent years. The dynamic heterogeneous information network is a network that has different types of vertices and time-labeled edges, and in this paper we study the temporal link prediction problem in the dynamic heterogeneous information network. Most existing studies employs the structure-based predictive methods, where the structures fails to embed the time information. Therefore, they cannot characterize the correlation between structures and time during the prediction. In this work, we firstly construct the structure called the time-difference-labeled path(TDLP) to combine the time information and structural features into a unified setting and propose TDLP, a time-difference-labeled path based temporal link prediction method, which combines the time information with the structural path features. Experiments on a real data set of a scholar bibliographic website demonstrate that the proposed TDLP method performs better than the state-of-the-art methods on predicting both whether and when a link will be built.