REGE: A Method for Incorporating Uncertainty in Graph Embeddings
REGE measures uncertainty in graph data and model outputs to create more robust graph embeddings. It defines data uncertainty using low-rank reconstructions and edge consensus, and model uncertainty via a student-teacher model refined by conformal learning. These uncertainties are injected as noise during training, improving embedding accuracy against adversarial attacks.