Link Sign Prediction

Link sign prediction focuses on determining the positive or negative nature of relationships (edges) in signed graphs, which represent both friendly and antagonistic interactions. Recent research emphasizes improving the accuracy and explainability of Signed Graph Neural Networks (SGNNs), exploring architectures like transformers and incorporating techniques such as data augmentation, contrastive learning, and curriculum-based training to address challenges like graph sparsity and adversarial attacks. These advancements are significant because accurate link sign prediction can enhance our understanding of complex social, biological, and technological networks, leading to improved applications in areas like recommendation systems and fraud detection.