Subgraph Wise Sampling Method

Subgraph-wise sampling methods are efficient mini-batch training techniques designed to overcome computational challenges in processing large graphs, particularly within graph neural networks (GNNs) and related models. Current research focuses on improving the accuracy of gradient estimation during training, often through novel algorithms like message compensation techniques, and developing provably convergent methods to ensure reliable performance. These advancements are crucial for scaling GNNs to real-world applications, impacting fields like recommender systems and knowledge graph analysis by enabling faster and more accurate model training on massive datasets.