Link Prediction based on Deep Latent Feature Model by Fusion of Network Hierarchy Information

Abstract

Link prediction aims at predicting latent edges according to the existing network structure information and it has become one of the hot topics in complex networks. Latent feature model that has been used in link prediction directly projects the original network into the latent space. However, traditional latent feature model cannot fully characterize the deep structure information of complex networks. As a result, the prediction ability of the traditional method in sparse networks is limited. Aiming at the above problems, we propose a novel link prediction model based on deep latent feature model by Deep Non-negative Matrix Factorization (DNMF). DNMF method can obtain more comprehensive network structure information through multi-layer factorization. Experiments on ten typical real networks show that the proposed method has performances superior to the state-of-the-art link prediction methods.

Keywords

• complex
• deep non-negative matrix factorization
• latent feature model
• link prediction