# Graph Neural Networks: Architectures, Stability, and Transferability

@article{Ruiz2021GraphNN, title={Graph Neural Networks: Architectures, Stability, and Transferability}, author={Luana Ruiz and Fernando Gama and Alejandro Ribeiro}, journal={Proceedings of the IEEE}, year={2021}, volume={109}, pages={660-682} }

Graph neural networks (GNNs) are information processing architectures for signals supported on graphs. They are presented here as generalizations of convolutional neural networks (CNNs) in which individual layers contain banks of graph convolutional filters instead of banks of classical convolutional filters. Otherwise, GNNs operate as CNNs. Filters are composed of pointwise nonlinearities and stacked in layers. It is shown that GNN architectures exhibit equivariance to permutation and…

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