Label Propagation

Label propagation is a semi-supervised machine learning technique that leverages the relationships between labeled and unlabeled data points to infer labels for the unlabeled data. Current research focuses on improving label propagation's robustness to noisy labels, heterophilic graph structures, and sparse data, often integrating it with graph neural networks (GNNs) or other advanced architectures like transformers. These advancements are significantly impacting various fields, including computer vision (object segmentation, image classification), natural language processing (fake news detection, text classification), and other domains where labeled data is scarce or expensive to obtain, leading to more efficient and accurate machine learning models.