Imbalanced Node Classification

Imbalanced node classification addresses the challenge of classifying nodes in graphs where classes have highly uneven distributions, leading to biased model performance favoring majority classes. Current research focuses on developing techniques like data augmentation (e.g., generating synthetic minority class nodes or leveraging unlabeled nodes), modifying loss functions to address class imbalance, and employing graph neural networks (GNNs) with specialized architectures or training strategies (e.g., curriculum learning, contrastive learning). These advancements aim to improve the accuracy and fairness of node classification in real-world applications, such as fraud detection, recommendation systems, and social network analysis, where imbalanced data is common.