Graph neural networks (GNNs) are artificial neural networks designed for tasks whose inputs are graphs. Because graphs usually do not have a canonical ordering of their nodes, GNN architectures are commonly designed to be permutation equivariant: reordering the nodes in the input reorders the corresponding node representations in the same way.
Graph neural networks (GNNs) are artificial neural networks designed for tasks whose inputs are graphs. Because graphs usually do not have a canonical ordering of their nodes, GNN architectures are commonly designed to be permutation equivariant: reordering the nodes in the input reorders the corresponding node representations in the same way.
Source: Wikipedia "Graph neural network" · CC BY-SA 4.0
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