What is self-supervised pretraining for GNN?

Self-supervised pretraining for graph neural networks (GNN) is a training method where the model learns to extract useful representations from graph data without using labeled tags. Instead, tasks based on the structure of the graph itself or properties of nodes/edges are used, such as predicting hidden parts of the graph, contrastive learning, or information recovery.

This approach allows GNNs to understand the structure and relationships within the graph, which improves subsequent supervised tasks (e.g., node or graph classification), especially when labeled data is scarce.