Graph Neural Network: In a Nutshell
Graph Neural Networks (GNNs) are a type of deep learning model that are specifically designed to identify and interpret complex patterns and dependencies within data that is structured as a graph. Unlike traditional neural networks that assume data instances are independent and identically distributed, GNNs leverage the connections between data points. This makes them particularly useful for relational data that is represented as graphs.
General Mathematics of GNNs
GNN operates on a graph denoted as \(G = (V, E)\), where \(V\) represents the set of nodes and \(E\) represents the set of edges connecting these nodes. Each node \(v\) in \(V\) is associated with a feature vector \(X_v\) which encapsulates the attributes or properties of that node.