Motifs-based Link Prediction for Multiplexed Networks.

Link prediction on multiplexed networks has wide applications in various fields such as social networks and recommendation systems. Some algorithms have been proposed to solve link prediction on multiplexed networks, which often rely on single-layer node characteristics, inter-layer similarity, and layer fusion. In this paper, we attempt to address this issue from the perspective of the local structure that can be constructed by two nodes. We propose a motifs-based naïve Bayes model for link prediction in multiplex networks, which considers the number of motif predictors and the role of nodes in the motif composition of different network layers. We apply this method to multiple empirical networks, and the results show that our method not only outperforms other existing algorithms in link prediction but also has high prediction accuracy. In addition, the role functions of the nodes included in the model can, indeed, help improve the performance of link prediction methods based solely on motifs. The motifs-based link prediction method effectively leverages the heterogeneity of edges and provides ideas for a more in-depth study of multiplexed networks.