Graph Regularized Neural Network

Graph-regularized neural networks (GRNNs) aim to leverage graph-structured data to improve the performance and efficiency of neural networks, particularly in tasks like node classification and semi-supervised learning. Current research focuses on addressing limitations such as over-smoothing in graph convolutional networks (GCNs) and exploring alternative architectures, including graph-regularized multi-layer perceptrons (MLPs) and bilevel optimization frameworks, to achieve better performance and scalability. These advancements are significant because they enable more accurate and efficient analysis of complex relational data across diverse applications, from hyperspectral image classification to extreme classification problems with millions of labels.