Augmented Graph

Augmented graphs are modified versions of original graphs, created to improve the performance of graph-based machine learning models. Current research focuses on developing sophisticated augmentation techniques that preserve crucial graph properties while introducing variability, often employing contrastive learning, Gaussian processes, or Metropolis-Hastings algorithms to generate these augmented graphs. These methods aim to address challenges like imbalanced data, limited labeled data, and the need for robust models capable of handling large-scale graphs. The resulting improvements in model accuracy and generalization have significant implications for various applications, including financial forecasting, social network analysis, and biomedical research.