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The directional message passing neural network (DimeNet) from the "Directional Message Passing for Molecular Graphs" paper. A generic wrapper for computing graph convolution on directed graphs as described in the "Edge Directionality Improves Learning on Heterophilic Graphs" paper. The graph convolutional operator with initial residual connections and identity mapping (GCNII) from the "Simple and Deep Graph Convolutional Networks" paper. The graph attentional propagation layer from the "Attention-based Graph Neural Network for Semi-Supervised Learning" paper. The Attentive FP model for molecular representation learning from the "Pushing the Boundaries of Molecular Representation for Drug Discovery with the Graph Attention Mechanism" paper, based on graph attention mechanisms.

Applies graph normalization over individual graphs as described in the "GraphNorm: A Principled Approach to Accelerating Graph Neural Network Training" paper. Applies Instance Normalization over a 5D input (a mini-batch of 3D inputs with additional channel dimension) as described in the paper Instance Normalization: The Missing Ingredient for Fast Stylization.The relational graph convolutional operator from the "Modeling Relational Data with Graph Convolutional Networks" paper. Applies a multi-layer Elman RNN with tanh ⁡ \tanh tanh or ReLU \text{ReLU} ReLU non-linearity to an input sequence. The Deep Graph Infomax model from the "Deep Graph Infomax" paper based on user-defined encoder and summary model \(\mathcal{E}\) and \(\mathcal{R}\) respectively, and a corruption function \(\mathcal{C}\). Creates a criterion that optimizes a multi-class multi-classification hinge loss (margin-based loss) between input x x x (a 2D mini-batch Tensor) and output y y y (which is a 2D Tensor of target class indices). The Principal Neighbourhood Aggregation graph convolution operator from the "Principal Neighbourhood Aggregation for Graph Nets" paper.

Performs GRU aggregation in which the elements to aggregate are interpreted as a sequence, as described in the "Graph Neural Networks with Adaptive Readouts" paper. The Batch Representation Orthogonality penalty from the "Improving Molecular Graph Neural Network Explainability with Orthonormalization and Induced Sparsity" paper. Rearranges elements in a tensor of shape ( ∗ , C × r 2 , H , W ) (*, C \times r The Frequency Adaptive Graph Convolution operator from the "Beyond Low-Frequency Information in Graph Convolutional Networks" paper. The self-attention pooling operator from the "Self-Attention Graph Pooling" and "Understanding Attention and Generalization in Graph Neural Networks" papers.The LINKX model from the "Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods" paper. The graph convolutional operator from the "Semi-supervised Classification with Graph Convolutional Networks" paper. Applies the Softplus function Softplus ( x ) = 1 β ∗ log ⁡ ( 1 + exp ⁡ ( β ∗ x ) ) \text{Softplus}(x) = \frac{1}{\beta} * \log(1 + \exp(\beta * x)) Softplus ( x ) = β 1 ​ ∗ lo g ( 1 + exp ( β ∗ x )) element-wise.