Unlabeled Node

Unlabeled nodes in graph data present a significant challenge in machine learning, particularly for node classification tasks where labeled data is scarce. Current research focuses on leveraging unlabeled nodes through techniques like self-training (assigning pseudo-labels based on model confidence or information gain), label propagation, and meta-learning to improve model generalization and address issues like class imbalance and distribution shifts between labeled and unlabeled data. These advancements are crucial for improving the performance of graph neural networks (GNNs) in various applications, including rumor detection, anomaly detection, and other scenarios where acquiring complete labeled datasets is impractical or expensive.