Training Graph

Training graph neural networks (GNNs) efficiently and effectively on large-scale graph data is a central challenge in machine learning, with research focusing on improving scalability, generalization, and fairness. Current efforts explore various techniques, including novel partitioning methods for distributed training, data augmentation strategies to enhance robustness, and the development of more efficient model architectures like Perceiver-based encoders and forward-forward algorithms to bypass backpropagation. These advancements are crucial for enabling GNN applications in diverse fields, such as recommendation systems, biological network analysis, and anomaly detection, where massive datasets and complex relationships are prevalent.