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.

近年来，已经引入了多路复用网络来描述真正的复杂系统，其中同一组实体进行不同类型的交互。在多路复用网络中，每一层都表示一种不同类型的交互。链路预测是复杂网络分析的研究热点。已经提出了大量的链路预测方法，但是只有少数几种被设计用于多路复用网络。在本文中，我们关注于复用网络中的链路预测问题。我们认为，建议一种通过同时考虑来自所有层的信息来执行链路预测的方法，因为一层中的链路形成会受到其他层中相同节点对的链路的影响。在这项研究中提出了一种监督方法来实现多路复用网络中的链路预测，将链接预测视为二进制分类问题。在提出的方法中，分类模型由从所有层提取的一组节点对的精细结构特征提供。在六个网络上进行了广泛的实验，以分析该方法的有效性。结果表明，所提出的方法明显优于所比较的方法。