Abstract: The research on traditional heterogeneous network link prediction has path-predicted algorithm and MPBP(meta-path feature-based backpPropagation neural network model) algorithm based on the metapath supervised learning.However,they can't make full use of the rich information provided by heterogeneous network to make link prediction.Based on the traditional supervised learning algorithm,this paper first designs the HLE-T(heterogeneous link entropy with time) algorithm in order to increase the link entropy and time dynamic information.Moreover,it constructs the MSLP(modified supervised link prediction)model of the Supervised learning algorithm with the multi-classification problem by the numerical segment of the link strength and weak relationship,and finally completes the experimental test under four data sets with different density.The experimental results show that the MSLP model improves the link prediction performance in heterogeneous network to some extent,and has some reference significance for the future link prediction research.

Key words: Heterogeneous information, Link prediction, Predictive model, Supervised learning