Link Prediction with Multiple Structural Attentions in Multiplex Networks

Many real networks can be viewed as multiplex networks with more than one layers. As different layers are usually not independent from each other, they can provide complementary information in the task of link prediction. In this paper, with the help of attention mechanism, we dig the structural correlations among different layers of the multiplex network as well as the network structural information of the target layer to make more precise link predictions. Specifically, we introduce three different attentions, namely the intra-layer distance/degree attention, the intra-layer neighbourhood attention, and the interlayer structural attention, to calculate both the influence among nodes in the same layer and the link correlations in different layers. Compared with other state-of-the art methods which usually require the information of node attributes or edge types, we only utilize the topological information of the network and thus provide a more general link prediction solution for multiplex network. We conduct comprehensive experiments on several real-world datatsets of different scales. By comparing with the state-of-the-art link prediction algorithms, we show the advantages of our algorithm, and the effectiveness of different attentions. Also, through visual case studies we uncover some intuitions about the relationship between the graph structure and the existence of a link. We make our source code anonymously available at: (will be released after review)