Graph Contrastive Learning

Instead of relying on large amounts of labeled data, modern graph learning increasingly depends on self-supervised strategies that allow models to learn directly from graph structure. This chapter introduces Graph Contrastive Learning (GCL) as a powerful self-supervised framework for extracting meaningful representations from unlabeled graph data. It begins with the fundamentals of self-supervised learning, highlighting its characteristics, task types, and relevance to graph domains. The chapter contrasts GCL with traditional supervised graph learning to emphasize its advantages in data-scarce and evolving environments. Core principles of graph contrastive learning, its motivation, key components, and learning mechanism are also explained. Various GCL frameworks are outlined to show how positive and negative graph views are constructed and compared. The chapter concludes by demonstrating how GCL improves robustness, generalization, and representation quality in graph-based machine learning systems.