An Algorithm of Dynamic Heterogeneous Networks Link Prediction

Abstract

By considering the evolution of networks over time and the rich semantic and structural characteristics of heterogeneous networks, DyMDGCN model is proposed in this paper. The model uses multi-channel partition to deal with heterogeneous networks, and then combines RNN and time attention capture evolution mode to obtain the final node embedding vector from and apply it to link prediction. In order to verify the effectiveness of this method, this paper selects two real dynamic heterogeneous network data sets of Twitter and EComm for experiments, and compares the AUC index with the traditional algorithm. The results show that this method can deal with heterogeneous network information and capture the dynamic evolution information of the network, and has a certain improvement in accuracy compared with the traditional algorithm.