Neighborhood Propagation

Neighborhood propagation methods enhance data representation by leveraging relationships between data points, primarily within graph structures or implicitly defined neighborhoods. Current research focuses on improving efficiency and scalability, particularly for large graphs, through techniques like lazy propagation and optimized algorithms for handling high-order relationships and mitigating issues like over-smoothing. These advancements are impacting diverse fields, improving performance in tasks such as graph neural network training, semi-supervised node classification, and even tabular data prediction by enriching feature representations through the propagation of information across related data instances.