Link Prediction

Link prediction aims to forecast missing connections in networks by analyzing existing relationships and node attributes. Current research heavily involves graph neural networks (GNNs), but also explores alternative approaches like traditional machine learning models and diffusion probabilistic models, often enhanced with techniques such as contrastive learning and data augmentation to improve accuracy and address issues like heterophily and long-tailed distributions. This field is crucial for advancing knowledge graph completion, recommendation systems, and other applications requiring the inference of relationships between entities, with ongoing efforts focused on improving model interpretability and fairness.