Network Regression

Network regression focuses on modeling how network structures change in response to external factors, aiming to predict network topology based on associated covariates. Recent research emphasizes developing robust regression methods that account for the unique characteristics of network data, such as size, topology, and sparsity, often employing techniques like optimal transport and Wasserstein metrics or adapting ensemble methods from social network theory. These advancements improve prediction accuracy and efficiency, particularly in handling dynamic networks and concept drift, with applications ranging from social network analysis to biological network modeling.