Network methods for diagonal integration of unpaired single-cell multiomics data: a review.

Using these nodes as sources, the information is propagated across the network, amplifying the signal and revealing clusters of highly significant nodes. 2.2.2 Graph neural networks and hypergraphs GNNs and their sub-variants, graph convolutional nets (GCN) and graph attention nets (GAT), form a powerful class of methods for capturing complex interactions and nonlinearities in biological data ( Zhang et al. 2021 ) (see Fig. 2C for a schematic depiction of spatial graph convolution, or message passing).

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