Graph Regularization

Graph regularization enhances machine learning models by incorporating structural information from data represented as graphs, aiming to improve model performance and robustness. Current research focuses on integrating graph regularization into various techniques, including matrix factorization, nonnegative matrix factorization, and graph neural networks, often employing algorithms like conjugate gradient methods and bilevel optimization to address computational challenges and gradient scarcity. This approach finds applications in diverse fields such as image processing, drug interaction prediction, and continual learning, offering improvements in accuracy, efficiency, and stability compared to methods lacking graph-based constraints.