Link Predictor

Link prediction aims to forecast missing or future connections within networks, a crucial task with applications across diverse fields. Current research emphasizes improving the accuracy and fairness of predictions, often employing graph neural networks (GNNs) and transformer-based architectures, while also addressing challenges like scalability, generalization to unseen data, and the effective integration of textual information. Significant efforts focus on mitigating biases, enhancing model explainability, and developing methods robust to distribution shifts in real-world data, ultimately leading to more reliable and trustworthy network analysis.