Graph Based Feature Normalization

Graph-based feature normalization aims to improve the performance of machine learning models, particularly graph neural networks (GNNs), by adapting feature scaling to the unique structure and characteristics of graph data. Current research focuses on developing novel normalization layers that go beyond standard methods like batch normalization, often incorporating graph topology and neighborhood information into the normalization process, as seen in adaptive graph normalization techniques. These advancements are crucial for enhancing the accuracy and robustness of GNNs across various applications, including facial expression recognition, natural language processing (especially for low-resource languages using complex scripts like Perso-Arabic), and signal processing under noisy conditions. Improved normalization methods lead to more efficient and effective model training, ultimately improving the performance of these models on diverse tasks.