Chapter 14: Graph Neural Networks (GNNs) with PyTorch
Abstract: Graph Neural Networks (GNNs) are a type of deep learning architecture designed to analyze and make predictions on data structured as graphs, which consist of nodes and the relationships (edges) between them . They are used across many fields, including social network analysis, molecular modeling, recommender systems, and computer vision, because they can handle the complex, relational nature of graph-structured data which is difficult for traditional neural networks to process. How GNNs work Graph structure : GNNs process data where entities are represented as nodes and their connections as edges. Information can be stored on both nodes and edges. Learning from neighbors : GNNs work by having each node aggregate information from its neighbors. Through message-passing layers, nodes iteratively update their representations by combining features from their local neighborhood. Deepening understanding : W...