Control-based Graph Embeddings with Data Augmentation for Contrastive Learning
The author introduces Control-based Graph Contrastive Learning (CGCL) as a novel framework for unsupervised graph representation learning, leveraging graph controllability properties and advanced edge augmentation methods to create augmented data for contrastive learning while preserving the controllability rank of graphs.