Global Graph

Global graph learning focuses on collaboratively training graph neural networks (GNNs) across distributed datasets, addressing challenges like non-independent and identically distributed data and privacy concerns. Current research emphasizes techniques to effectively aggregate information from local subgraphs to build a comprehensive global graph representation, often employing methods like federated learning and novel GNN architectures designed to mitigate over-smoothing and capture both local and global graph structures. This field is significant for enabling large-scale graph analysis while preserving data privacy and improving the efficiency and accuracy of GNNs across diverse applications, including recommendation systems and robotics.