A Graph neural network is a class of artificial neural networks for processing data that can be represented as graphs.
In the more general subject of "Geometric Deep Learning", some existing neural network architectures can be interpreted as GNNs operating on suitably defined graphs.
Convolutional neural networks in the context of computer vision can be seen as a GNN applied to graphs structured as pixel grids.
Transformers in the context of natural language processing can be seen as GNNs applied to complete graphs whose nodes are words in a sentence.
Key to the design of GNNs is the use of pairwise message passing so that graph nodes iteratively update their representations by exchanging information with their neighbors.
Implementation and experiments of graph neural netwokrs, like gcn, graphsage and gat.
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