The Light Graph Convolution (LGC) operator from the "LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation" paper.

The DimeNet++ from the "Fast and Uncertainty-Aware Directional Message Passing for Non-Equilibrium Molecules" paper. Conv1d module with lazy initialization of the in_channels argument of the Conv1d that is inferred from the input.

Not only do girls here achieve academic excellence but they enjoy contributing to the school and wider community. The Recurrent Event Network model from the "Recurrent Event Network for Reasoning over Temporal Knowledge Graphs" paper. The equilibrium aggregation layer from the "Equilibrium Aggregation: Encoding Sets via Optimization" paper. Performs aggregations with one or more aggregators and combines aggregated results, as described in the "Principal Neighbourhood Aggregation for Graph Nets" and "Adaptive Filters and Aggregator Fusion for Efficient Graph Convolutions" papers. ConvTranspose1d module with lazy initialization of the in_channels argument of the ConvTranspose1d that is inferred from the input.

Rearranges elements in a tensor of shape ( ∗ , C × r 2 , H , W ) (*, C \times rThe 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 measures the triplet loss given an input tensors x 1 x1 x 1, x 2 x2 x 2, x 3 x3 x 3 and a margin with a value greater than 0 0 0. The Weisfeiler Lehman (WL) operator from the "A Reduction of a Graph to a Canonical Form and an Algebra Arising During this Reduction" paper. Applies pair normalization over node features as described in the "PairNorm: Tackling Oversmoothing in GNNs" paper.

Conv3d module with lazy initialization of the in_channels argument of the Conv3d that is inferred from the input. Applies the Softmin function to an n-dimensional input Tensor rescaling them so that the elements of the n-dimensional output Tensor lie in the range [0, 1] and sum to 1. BatchNorm3d module with lazy initialization of the num_features argument of the BatchNorm3d that is inferred from the input. 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. We are ambitious for their whole development in preparing them to be perceptive and caring global citizens who are not only prepared for the 21st century but who understand its complexity.

The self-attention pooling operator from the "Self-Attention Graph Pooling" and "Understanding Attention and Generalization in Graph Neural Networks" papers.

Finally, we added full support for customization of aggregations into the SAGEConv layer — simply override its aggr argument and utilize the power of aggregation within your GNN. The powermean aggregation operator based on a power term, as described in the "DeeperGCN: All You Need to Train Deeper GCNs" paper. InstanceNorm2d module with lazy initialization of the num_features argument of the InstanceNorm2d that is inferred from the input. The dynamic edge convolutional operator from the "Dynamic Graph CNN for Learning on Point Clouds" paper (see torch_geometric.The topology adaptive graph convolutional networks operator from the "Topology Adaptive Graph Convolutional Networks" paper. The Neural Fingerprint model from the "Convolutional Networks on Graphs for Learning Molecular Fingerprints" paper to generate fingerprints of molecules. The LINKX model from the "Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods" paper. Notably, all aggregations share the same set of forward arguments, as described in detail in the torch_geometric.