GNNs - AI
AI A Graph Neural Network (GNN) is a specialized class of deep learning models designed to process data naturally structured as a graph , meaning data composed of nodes (entities) and edges (relationships) . While traditional deep learning models like Convolutional Neural Networks (CNNs) are optimized for grid-like data (images) and Recurrent Neural Networks (RNNs) excel at sequential data (text), GNNs are purpose-built to capture complex, non-Euclidean relationships where connections between data points matter as much as the data points themselves. [ 1 , 2 , 3 ] The Core Anatomy of a Graph To understand GNNs, it helps to break down a graph data structure into its foundational mathematical components (G = (V, E)): [ 1 ] Nodes (V) : The individual entities within a network (e.g., users in a social network, atoms in a molecule, or web pages). Each node possesses a feature vector representing its metadata. [ 1 , 2 , 3 , 4 , 5 ] Edges (E) : The links or connections indicating a relation...