Learning the long-tail distribution in latent space for Weighted Link Prediction via conditional Invertible Neural Networks

Link prediction is an important problem in dynamic systems, where the goal is to predict future unweighted or weighted link topologies using historical context. Compared with unweighted links, weighted links can preferably reveal the nature and strength of the interactions among entities. However, weighted links also bring greater challenges because they require subtle structural adjustments and numerical variations to be captured. Existing methods are primarily tailored for unweighted links and most generally suffer from low-quality performance when applied to Weighted Link Prediction (WLP) task. In this study, we propose a novel generative framework that adopts conditional Invertible Neural Networks (INNs) to achieve WLP. The proposed framework leverages the benefits of conditional INNs to exactly optimize the log-likelihood in the latent space conditioned on the historical context, which can be sensitive to minor replacements in real-world systems and derive accurate WLPs. Furthermore, to deal with the long-tail statistical phenomenon of edge weights observed in real life, a tail-adaptive distribution is learned in latent space to capture the tail properties and enhance the model’s ability. To verify the effectiveness of the proposed method, we conduct extensive experiments on four datasets from different systems. The experimental results demonstrate that our model achieves impressive results compared to state-of-the-art competitors.