Extended Data Fig. 1: Design space for blocks of the architectures.
From: An adaptive graph learning method for automated molecular interactions and properties predictions
a, Feed-forward Block. It takes a tensor as input and outputs a tensor. Abbreviations and their full name correspond as follows: Norm(Normalization), ReLU(Rectified linear units), CeLU(Continuously differentiable exponential linear units). b, Message Passing Block. It takes a graph as input and outputs a graph. Abbreviations and their full name correspond as follows: GCN(Graph convolutional networks), GAT(Graph attention networks), MPN(Message-passing neural networks), Tri-MPN(Triplet message-passing neural networks), Light Tri-MPN(Light triplet message-passing neural networks). c, Fusion Block. It takes a graph as input and outputs a tensor. Dot means the dot multiplication operation. d, Global Pooling Block. It takes a graph as input and outputs a tensor.
