Link Prediction Based on Node Embedding and Personalized Time Interval in Temporal Multi-relational Network

Abstract

Link prediction on temporal networks has a wide range of applications, such as facilitating individual relationship mining, user recommendation, and user behavior analysis. The traditional link prediction methods on temporal network only considered the structure of single-relational networks, which ignored the diversity of network link types and the influence between different link types. This paper proposes a temporal multi-relational network link prediction method combining personalized time interval and node embedding. Firstly, the node embedding is generated according to the structure of target network and auxiliary network which overcomes the defect of single network information sparseness; then, considering the diversity of link types, we construct the relationship formation sequence based on personalized time interval and the influence between different relationships for each link; next, the relationship formation sequence is modeled based on the Hawkes process, which takes product of the node embedding as the initial value to calculate the possibility of link formation. The method captures the dynamic characteristics and multi-relational property of the network, which is helpful to improve the accuracy of link prediction. Experimental results find that the proposed method has better performance and can be applied to large-scale networks.