Scalable GNN

Scalable Graph Neural Networks (GNNs) aim to overcome the limitations of traditional GNNs, which struggle with the massive datasets and complex structures found in real-world applications. Current research focuses on developing efficient algorithms and architectures, such as those employing graph partitioning, sampling techniques, and pre-computation strategies, to accelerate both training and inference. These advancements are crucial for deploying GNNs in large-scale domains like recommendation systems and scientific computing, enabling the analysis of previously intractable datasets and leading to improved model performance and efficiency.