Graph Neural Networks (GNNs)
AI designed to understand data that comes in networks and connections, like social networks, molecules, or supply chains.
Definition
Neural networks built to operate on graph-structured data, where entities are nodes and their relationships are edges. Instead of assuming inputs are independent, a GNN learns by repeatedly aggregating information from each node's neighbors, so a node's representation reflects its position and connections in the wider network. This message-passing approach captures relational structure that grid- or sequence-based networks miss.
Why it matters
Much real-world data is relational: social networks, molecules, supply chains, and transaction graphs. GNNs are a leading approach for fraud detection, drug discovery, recommendation systems, and logistics, where the connections between entities carry as much signal as the entities themselves.
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