Home Glossary Graph Neural Network (GNN)

Graph Neural Network (GNN)

A graph neural network, or GNN, learns from data organized as nodes and the relationships connecting them. Instead of treating every record independently, it passes and combines information between neighboring nodes so each representation reflects its local structure. This makes GNNs suitable for social networks, molecular chemistry, fraud rings, transportation systems, recommendation engines, and knowledge graphs. They can predict properties of nodes, classify entire graphs, or estimate whether a connection should exist. Performance depends on graph quality, sampling, message-passing depth, and how rapidly information becomes oversmoothed across layers. Large or constantly changing graphs also create memory, latency, privacy, and monitoring challenges.

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