Network Classification

Network classification focuses on grouping networks based on their structural properties, aiming to understand and predict their behavior. Current research explores diverse approaches, including convolutional neural networks (CNNs) for image-based network representations, topological data analysis coupled with deep learning for fMRI data, and methods analyzing network dynamics using cellular automata to extract descriptive features. These advancements improve accuracy in classifying various network types, from biological and social networks to those representing image data, with implications for diverse fields like precision agriculture, medical diagnosis, and network security.