Inter-Layer Similarity-Based Graph Neural Network for Link Prediction in Social Multiplex Networks

Graph neural networks have gained a great popularity in the past few years because they have proven to be useful in many tasks in complex networks, including link prediction. The complex and multi-layered structure of multiplex networks poses challenges to traditional link prediction methods. In this study, we propose a new approach based on Graph Neural Networks (GNN) for link prediction in multiplex networks. In the suggested approach, several adjacency matrices have been aggregated based on measuring the inter-layer similarities and employed in a GNN. The experimental results on benchmark real-world networks show the effectiveness and validity of the method.