Graph neural network (GNN)

Graph Neural Networks (GNNs) are a powerful tool for analyzing and learning from data represented as graphs, where entities are nodes and relationships between entities are edges. Unlike traditional neural networks that operate on grid-like data (e.g., images) or sequential data (e.g., text), GNNs are specifically designed to handle the complexities of graph structures. The core idea behind GNNs is message passing or graph convolution. Each node aggregates information from its neighbors and updates its own representation based on this aggregated information. This process is repeated for several iterations (layers), allowing information to propagate through the graph and enabling nodes to 'learn' representations that capture their local and global context within the graph. Different GNN architectures exist, varying in how they aggregate neighbor information (e.g., Graph Convolutional Networks (GCNs), Graph Attention Networks (GATs), GraphSAGE). GNNs are used in a wide range of applications, including social network analysis, drug discovery, recommendation systems, and knowledge graph completion. Their ability to reason about relationships and dependencies between entities makes them particularly well-suited for tasks where graph structure is important.