Supervised link prediction in multiplex networks

In recent years, multiplex networks have been introduced to describe real complex systems, where the same group of entities make different types of interaction. In a multiplex network, each layer expresses one distinct type of interaction. Link prediction is a research hotspot in complex network analysis. A large number of link prediction methods have been proposed, but only a few were designed for multiplex networks. In this paper, we focus on the link prediction problem in multiplex networks. In our opinion, an approach in which link prediction is performed by simultaneously considering the information from all layers is advisable, because the formation of links in one layer can be affected by links of the same node pairs in other layers. A supervised method is proposed in this study to implement link prediction in multiplex networks, which regards link prediction as a binary classification problem. In the proposed method, a classification model is fed by a set of elaborate structural features of node pairs that are extracted from all layers. Extensive experiments are conducted on six networks to analyze the effectiveness of the proposed method. The results demonstrate that the proposed method outperforms the compared methods significantly.