LSTM-attention-guided graph neural networks for integrated genotype–Environment modeling in maize yield prediction
Fig 5
Architecture C retains the message-passing structure of Architecture B and introduces a global supernode attention readout applied after K propagation layers.
The supernode attends to all genotype and environment embeddings to produce a compact graph-level representation used for prediction.