Graph Neural Networks (GNNs)

Graph Neural Networks (GNNs) are neural models that leverage explicit graph structure and relational context to aggregate features across nodes and edges. They are applied to tasks such as node classification, link prediction, and graph classification. GNNs entail model assumptions, scalability challenges, and overfitting trade-offs.

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Neural network architectures for processing and analyzing graph-structured data via relational message passing.

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Wikipedia: Graph neural network Survey: Graph Neural Networks (Zhou et al., arXiv)PyTorch Geometric (GitHub)

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