A Novel Link Prediction Method for Social Multiplex Networks Based on Deep Learning

Due to the great advances in information technology, an increasing number of social platforms have appeared. Friend recommendation is an important task in social media, but newly built social platforms have insufficient information to predict entity relationships. In this case, platforms with sufficient information can help newly built platforms. To address this challenge, a model of link prediction in social multiplex networks (LPSMN) is proposed in this work. Specifically, we first extract graph structure features, latent features and explicit features and then concatenate these features as link representations. Then, with the assistance of external information from a mature platform, an attention mechanism is employed to construct a multiplex and enhanced forecasting model. Additionally, we consider the problem of link prediction to be a binary classification problem. This method utilises three different kinds of features to improve link prediction performance. Finally, we use five synthetic networks with various degree distributions and two real-world social multiplex networks (Weibo–Douban and Facebook–Twitter) to build an experimental scenario for further assessment. The numerical results indicate that the proposed LPSMN model improves the prediction accuracy compared with several baseline methods. We also find that with the decline in network heterogeneity, the performance of LPSMN increases.