DSMN: A New Approach for Link Prediction in Multilplex Networks

In a multiplex network, there exists different types of relationships between the same set of nodes such as people which have different accounts in online social networks. Previous researches have proved that in a multiplex network the structural features of different layers are interrelated. Therefore, effective use of information from other layers can improve link prediction accuracy in a specific layer. In this paper, we propose a new inter-layer similarity metric DSMN, for predicting missing links in multiplex networks. We then combine this metric with a strong intralayer similarity metric to enhance the performance of link prediction. The efficiency of our proposed method has been evaluated on both real-world and synthetic networks and the experimental results indicate the outperformance of the proposed method in terms of prediction accuracy in comparison with similar methods.