A graph neural network (GNN) is a machine learning model designed to process data represented as graphs. Graphs consist of nodes (entities) and edges (relationships between nodes), and GNNs learn to capture patterns in these structures by propagating and aggregating information across nodes and edges. Unlike traditional neural networks that work on grid-like data (e.g., images or sequences), GNNs handle irregular, interconnected data. For example, a GNN might analyze a social network graph by updating each user’s node representation based on their friends’ activity, enabling predictions like identifying communities or recommending connections.
GNNs are closely related to knowledge graphs, which are structured databases that represent real-world facts as nodes (e.g., “Paris” or “France”) and edges (e.g., “is capital of”). Knowledge graphs often lack explicit features for nodes or edges, making GNNs a natural fit for tasks like node classification, link prediction, or graph completion. For instance, a GNN could predict missing relationships in a knowledge graph about countries by analyzing existing connections. Suppose a knowledge graph contains “Paris → capital → France” but lacks “Berlin → capital → Germany.” A GNN might infer the missing link by learning patterns from neighboring nodes (e.g., other European capitals) and their relational contexts.
A practical application of GNNs on knowledge graphs is recommendation systems. Consider a movie knowledge graph where nodes represent users, movies, and genres, and edges capture interactions (e.g., “user watched movie” or “movie belongs to genre”). A GNN could process this graph to predict which movies a user might like by propagating preferences through connected nodes. For example, if a user watches sci-fi movies linked to “Star Wars,” the GNN might recommend “Dune” by analyzing shared genre connections. This approach leverages the graph’s relational structure to improve accuracy over methods that treat users or items as isolated data points.