Weak link prediction based on hyper latent distance in complex network

Weak links play a crucial role in the functionality and dynamics of networks. Nevertheless, the ability to forecast weak links accurately remains elusive. This article introduces a neural network framework that solely utilizes the network topology to predict weak links. Firstly, we embed the network into an embedding space and observe that weak links generally possess longer distances compared to strong links. Subsequently, we propose the concept of 'hyper latent distance' as a means to characterize the pairwise node strength in the embedding space and incorporate it into our neural network model. Empirical investigations conducted on real networks demonstrate that our approach enhances the prediction accuracy for both weak and strong links concurrently. The potential of our method extends to the enhancement of recommender systems.