Neighbor Sampling

Neighbor sampling techniques optimize the selection of relevant data points within a larger dataset for improved efficiency and accuracy in various machine learning tasks. Current research focuses on developing adaptive sampling strategies that consider factors like temporal dynamics, feature similarity, and the contribution of individual neighbors, leading to algorithms like RepeatMixer and AFIND+. These advancements are crucial for addressing challenges in large-scale graph processing, federated learning, and self-supervised learning, ultimately improving model performance and scalability in diverse applications. The impact extends to areas such as recommendation systems, traffic prediction, and graph neural networks.